Forage harvesting system based on growth state prediction model

Abstract

The invention relates to the technical field of data processing, in particular to a pasture harvesting system based on a growth state prediction model, which comprises an acquisition module, a preliminary screening module, a judgment module, a determination module, an adjustment module, a prediction module, a selection module and a harvesting module. According to the method, key data are collected, and a high-quality pasture region is screened out; through primary screening of plant height and scab area, the area with the height meeting the requirement and with few plant diseases and insect pests is quickly locked; then, judging healthy and well-grown marked checks by combining the area of the disease spots and the tiller number; determining concerned grids with high biomass and excellent quality according to the tiller number and the leaf area; adjusting the width of the grids according to the gathering degree of concerned grids in time and space; finally, a harvesting area is accurately selected in combination with the concerned checks and the predicted key checks, and the problems of uneven pasture quality and low harvesting efficiency caused by blind harvesting and excessive dependence on a static model are effectively solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a forage harvesting system based on a growth state prediction model. Background Art

[0002] With the continuous growth of the global population and the continuous improvement of living standards, the demand for livestock products is increasing day by day, and the importance of the livestock industry is becoming more and more prominent. As the basis of the livestock industry, the demand for high-quality forage has also increased significantly. High-quality forage can not only significantly improve the growth rate of livestock and the quality of livestock products such as meat and milk, but also enhance the disease resistance of livestock, reduce the occurrence of diseases, lower the breeding cost, and improve the breeding efficiency. In modern livestock and poultry farming, the large-scale and intensive farming mode is gradually popularized, which puts forward higher requirements for the stable supply and precise management of high-quality forage. At the same time, consumers' attention to the quality and safety of livestock products is also increasing continuously, requiring that forage be effectively monitored throughout the growth and harvesting process to ensure that its quality meets the standards, so as to ensure the safety and health of the final livestock products.

[0003] The patent document with the publication number of CN116952853A discloses an agricultural monitoring system for forage quality monitoring, which includes: an edge system, including one or more sensors for collecting environmental data and / or forage image data; a side system, including a side controller configured to perform real-time monitoring and analysis of forage quality according to the environmental data and / or forage image data collected by the edge system to obtain forage quality data; and a cloud system, including a data aggregation center for storing and managing the environmental data and / or the forage image data, the forage quality data, edge system data and side system data.

[0004] It can be seen that the agricultural monitoring system for forage quality monitoring has the following problems: when the monitoring area is large and the data volume is huge, it is impossible to ensure the timely update of forage quality data; although there are sensors, the accurate recognition and real-time adjustment capabilities in complex environments need to be improved; the in-depth analysis and application of data are insufficient, and the data value cannot be fully exploited to provide more valuable decision-making support for forage planting and harvesting; during the harvesting process, it is impossible to monitor the quality indicators of forage in real time, and it is even more impossible to dynamically adjust the harvesting parameters according to these indicators, making it difficult to guarantee the quality of the harvested forage and unable to meet the personalized needs of different users for high-quality forage. Summary of the Invention

[0005] Therefore, the present invention provides a forage harvesting system based on a growth state prediction model to overcome the problems of uneven forage quality and low harvesting efficiency caused by blind harvesting and excessive reliance on static models in the prior art through real-time multi-dimensional data, a growth state prediction model and a dynamic adjustment mechanism.

[0006] To achieve the above object, the present invention provides a forage harvesting system based on a growth state prediction model, comprising: A collection module is used to obtain in real time the plant height, diseased spot area, tiller number and leaf area of ​​the forage grass in each square to be tested based on a preset square width; A primary screening module, which is connected to the collection module and is used to obtain a number of temporary grids by primary screening according to the plant height, the diseased spot area and a preset plant height range; A determination module, which is connected to the collection module and the primary screening module respectively, and is used to determine a number of marked squares according to the diseased spot area and the tillering number of each temporary square; A determination module, which is connected to the acquisition module and the determination module respectively, and is used to determine a number of focus squares according to the tillering number and the leaf area of ​​each marked square; An adjustment module, connected to the determination module, for adjusting the preset square width according to the position of each of the focus squares to obtain an adjusted square width; A prediction module, which is connected to the collection module and the adjustment module respectively, and is used to input the plant height, the diseased spot area, the tiller number and the leaf area collected based on the adjusted grid width into a preset growth state prediction model to predict a number of key grids; A selection module, which is connected to the determination module and the prediction module respectively, and is used to select a number of harvesting squares according to the key squares and the concerned squares determined based on the adjustment square width; A harvesting module is connected to the selection module and is used for harvesting the grass in each harvesting grid.

[0007] Furthermore, the determination module includes: A normalization processing unit, used to normalize all the lesion areas within a preset determination time to obtain an area normalized data set, and to normalize all the tiller numbers within a preset determination time to obtain a tiller normalized data set; A determination unit is connected to the normalization processing unit and is used to determine a number of the marked squares according to the lesion normalization data set and the tillering normalization data set.

[0008] Furthermore, the determination unit includes: A correlation calculation subunit is used to calculate the correlation coefficient between the lesion normalized data set and the tiller normalized data set to obtain a change correlation; The determination subunit is connected to the correlation calculation subunit and is used to determine the temporary square as the marked square when the change correlation is less than a preset correlation threshold, so as to determine a plurality of marked squares.

[0009] Further, the determining module includes: A tiller fluctuation value calculation unit for calculating the standard deviation of the tiller numbers at each moment from the initial moment to the preset determination duration, obtaining a plurality of tiller fluctuation values; A leaf area fluctuation value calculation unit for calculating the standard deviation of the leaf areas at each moment from the initial moment to the preset determination duration, obtaining a plurality of leaf area fluctuation values; A determining unit, which is respectively connected to the tiller fluctuation value calculation unit and the leaf area fluctuation value calculation unit, for determining a plurality of concerned grids according to all the tiller fluctuation values and all the leaf area fluctuation values.

[0010] Further, the determining unit includes: A tiller change curve drawing subunit for drawing a curve of the change of all the tiller fluctuation values over time within the preset determination duration, obtaining a tiller change curve; A leaf area change curve drawing subunit for drawing a curve of the change of all the leaf area fluctuation values over time within the preset determination duration, obtaining a leaf area change curve; A synchronization degree calculation subunit, which is respectively connected to the tiller change curve drawing subunit and the leaf area change curve drawing subunit, for calculating the cosine similarity of the tiller change curve and the leaf area change curve, obtaining a change synchronization degree; A determining subunit, which is connected to the synchronization degree calculation subunit, for determining the marked grid as the concerned grid when the change synchronization degree is greater than a preset synchronization degree threshold, so as to determine a plurality of concerned grids.

[0011] Further, the adjustment module includes: A dispersion calculation unit for calculating the standard deviation of the positions of each of the concerned grids, obtaining a dispersion; A dispersion deviation calculation unit, which is connected to the dispersion calculation unit, for calculating the relative deviation between the dispersion and a preset dispersion threshold when the dispersion is greater than the preset dispersion threshold, obtaining a dispersion deviation; An adjustment unit, which is connected to the dispersion deviation calculation unit, for adjusting the preset grid width according to the dispersion deviation and the preset dispersion deviation threshold when the dispersion deviation is greater than a preset dispersion deviation threshold, obtaining an adjusted grid width.

[0012] Further, the adjustment unit includes: A uniform index calculation subunit, which is used to mark all timestamps within a preset adjustment duration where the dispersion deviation is greater than the preset dispersion deviation threshold. When the number of all timestamps is greater than a preset quantity threshold, calculate the time intervals between all adjacent timestamps, and calculate the ratio of the standard deviation to the mean of the time intervals to obtain a uniform index; An adjustment factor calculation subunit, which is connected to the uniform index calculation subunit and is used to calculate the relative deviation between the dispersion deviation and the preset dispersion deviation threshold to obtain an adjustment factor when the uniform index is less than a preset uniform index threshold; An adjustment subunit, which is connected to the adjustment factor calculation subunit and is used to increase the preset grid width according to the adjustment factor and a preset adjustment coefficient to obtain an adjusted grid width.

[0013] Furthermore, the selection module includes: A ratio calculation unit, which is used to calculate the ratio of the number of the key grids to the number of the concerned grids to obtain a quantity ratio; A coincidence degree calculation unit, which is connected to the ratio calculation unit and is used to count the number of overlapping grids among all the key grids and all the concerned grids to obtain an overlapping quantity when the quantity ratio is greater than the minimum value of a preset ratio range and less than the maximum value of the preset ratio range. When the number of key grids is greater than the number of concerned grids, calculate the ratio of the overlapping quantity to the number of key grids to obtain a coincidence degree, or when the number of key grids is less than the number of concerned grids, calculate the ratio of the overlapping quantity to the number of concerned grids to obtain a coincidence degree; A selection unit, which is connected to the coincidence degree calculation unit and is used to determine the key grids as the harvesting grids to select several harvesting grids when the coincidence degree is greater than a preset coincidence degree threshold.

[0014] Furthermore, the preliminary screening module includes: A height comparison unit, which is used to compare the plant height with the preset plant height range to obtain a height comparison result; A preliminary screening unit, which is connected to the height comparison unit and is used to preliminarily screen a number of temporary grids according to the lesion area when the height comparison result is that the plant height is greater than the minimum value of the preset plant height range and less than the maximum value of the preset plant height range.

[0015] Furthermore, the preliminary screening unit includes: A lesion comparison unit, which is used to compare the lesion area with a preset lesion area threshold to obtain a lesion comparison result; A primary screening unit, which is connected to the lesion comparison unit and is used to determine the test square as the temporary square when the lesion comparison result shows that the lesion area is smaller than the preset lesion area threshold, so as to initially screen out a number of temporary squares.

[0016] Compared with the prior art, the beneficial effect of the present invention is to screen out high-quality forage grass areas by collecting key data. First, through the primary screening of plant height and lesion area, quickly lock in the areas with the required height and fewer pests and diseases. Then, combine the lesion area and tiller number to determine the healthy and well-growing marked squares. Next, determine the concerned squares with high biomass and excellent quality according to the tiller number and leaf area; adjust the square width according to the aggregation degree of the concerned squares in time and space to optimize the harvesting plan; finally, combine the key squares and concerned squares predicted by the growth state prediction model to accurately select the harvesting area, effectively solving the problems of uneven forage grass quality and low harvesting efficiency caused by blind harvesting and over-reliance on static models.

[0017] Furthermore, by calculating the correlation coefficient of the normalized data sets of the two, their correlation can be quantified. When determining the marked squares based on the correlation coefficient, only the areas with weak correlation between the lesion area and the tiller number are retained, which can screen out the areas where the forage grass grows stably and is less affected by lesions, and can effectively screen out the marked squares that meet the requirements to ensure the forage grass quality.

[0018] Furthermore, by calculating the correlation coefficient of the normalized lesion data set and tiller data set, the strength of the linear relationship between the two can be quantified. When the change correlation degree is less than the preset correlation degree threshold, it indicates that there is a weak correlation between the lesion area and the tiller number. Such a logical relationship means that the appearance of the lesion has little impact on the tillering, so the temporary square can be determined as the marked square, which can effectively screen out the areas where the forage grass grows stably and is less affected by lesions.

[0019] Furthermore, by calculating the standard deviation of the tiller number and leaf area within a preset determination period, the fluctuation of the tiller number and leaf area can be quantified. The fluctuation of the tiller number reflects the stability of the tillering growth of the forage grass, while the fluctuation of the leaf area reflects the change of the photosynthesis ability of the forage grass. The two reflect the health status and growth trend of the forage grass during the growth process. Combining these two fluctuation values can more comprehensively evaluate the growth state of the forage grass.

[0020] Furthermore, by plotting the curves of the tiller fluctuation value and the leaf area fluctuation value over time and calculating the cosine similarity of the two curves, the synchronization degree between the tiller change and the leaf area change is quantified. Both the tiller number and the leaf area are key indicators for measuring the growth status of forage grass. Their synchronous changes usually reflect the coordination of forage grass in aspects such as nutrient utilization efficiency and photosynthesis ability during the growth process. When the change synchronization degree is greater than the preset synchronization degree threshold, it indicates that the forage grass in the marked grid has good consistency in tiller growth and leaf expansion, which often means that the forage grass grows healthily and has excellent quality, meeting the growth characteristics of high-quality forage grass. Therefore, it is determined as a concerned grid.

[0021] Furthermore, the dispersion degree is obtained by calculating the standard deviation of the positions of the concerned grids. The smaller the standard deviation, the more concentrated the concerned grids; conversely, the larger the standard deviation, the more dispersed. When the dispersion degree is greater than the preset dispersion degree threshold, then calculate the relative deviation between the dispersion degree and the preset dispersion degree threshold. When the dispersion degree deviation is greater than the preset dispersion degree deviation threshold, it indicates that the concerned grids are relatively dispersed. At this time, adjust the preset grid width, increase the width to divide the area more carefully, and improve the accuracy of the harvesting plan.

[0022] Furthermore, by marking the relevant timestamps within the preset adjustment duration and calculating the uniformity index of the time intervals when the quantity reaches the standard, the time regularity of the occurrence of the dispersion degree deviation can be accurately evaluated. When the uniformity index is less than the preset threshold, it indicates that the time intervals are relatively uniform, which means that the occurrence time distribution of the situation where the dispersion degree deviation exceeds the preset threshold within the preset adjustment duration is regular, indicating that the non-uniformity of the forage grass distribution shows a certain stability in time, and at the same time reflects the stability of the system's monitoring of the forage grass distribution, indicating that the system can reliably capture the signal of the forage grass distribution change. Calculating the relative deviation between the dispersion degree deviation and the preset threshold as the adjustment factor can quantify the adjustment amplitude. According to the adjustment factor and the preset adjustment coefficient, increase the preset grid width, which can expand the harvesting range, make the forage grass distribution in each grid more uniform, and reduce the harvesting problems caused by uneven distribution.

[0023] Furthermore, by comprehensively considering the quantity ratio and the coincidence degree, first judge whether the quantity ratio is within a reasonable range. If it exceeds the range, directly determine the concerned grid as a harvesting grid, which can quickly screen out the situations that obviously do not meet the quantity requirements. If the quantity ratio is within a reasonable range, then further judge through the coincidence degree. When the numbers of key grids and concerned grids are different, different calculation methods are adopted to ensure the accuracy of the coincidence degree, thus more objectively reflecting the coincidence situation between the two, improving the accuracy and reliability of determining the harvesting grid, and avoiding misjudgment. It reduces human intervention and errors, improves the efficiency and quality of selecting the harvesting grid, ensures the objectivity and accuracy of the data, and provides strong support for the subsequent harvesting operation.

[0024] Furthermore, a rough screening is performed by comparing the plant height. The plant height reflects the growth stage and development of the forage. Plants that are too high or too low may have problems of over-maturity or poor growth, respectively, affecting the quality and nutritional value. The area of ​​lesions reflects the health of the forage. Too many lesions will reduce the quality and yield. Therefore, by first screening by height, and then performing a secondary screening based on the area of ​​lesions, the forage area that meets the requirements can be accurately locked, the harvesting efficiency can be improved, and the forage quality can be ensured.

[0025] Furthermore, by screening the diseased spot area, which is a key indicator for assessing the health status of forage, an excessively large diseased spot area usually indicates that the forage is invaded by diseases, reducing the quality of the forage. When the diseased spot area is smaller than the preset diseased spot area threshold, the forage that is severely affected by the disease can be effectively excluded, and the forage areas that meet the height and health requirements can be efficiently screened out, thereby improving the harvesting efficiency and forage quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a schematic diagram of a forage harvesting system based on a growth state prediction model of this embodiment; Figure 2 The decision logic diagram of the decision unit for deciding the marked squares in this embodiment; Figure 3 A decision logic diagram for determining a focus grid for this embodiment; Figure 4 This is a decision logic diagram of a temporary grid obtained by the initial screening of the initial screening unit in this embodiment. DETAILED DESCRIPTION

[0027] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0028] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0029] See also Figure 1 As shown, it is a flow chart of the container electronic tag testing method of this embodiment; This embodiment provides a forage harvesting system based on a growth state prediction model, including: A collection module is used to obtain in real time the plant height, diseased spot area, tiller number and leaf area of ​​the forage grass in each square to be tested based on a preset square width; A primary screening module, which is connected to the collection module and is used to obtain a number of temporary grids by primary screening according to the plant height, the diseased spot area and a preset plant height range; A determination module, which is respectively connected to the acquisition module and the preliminary screening module, and is used to determine a number of marked squares according to the lesion area and the tiller number of each of the temporary squares; A determination module, which is respectively connected to the acquisition module and the determination module, and is used to determine a number of concerned squares according to the tiller number and the leaf area of each of the marked squares; An adjustment module, which is connected to the determination module, and is used to adjust the preset square width according to the positions of the concerned squares to obtain an adjusted square width; A prediction module, which is respectively connected to the acquisition module and the adjustment module, and is used to input the plant height, the lesion area, the tiller number, and the leaf area collected based on the adjusted square width into a preset growth state prediction model to predict a number of key squares; A selection module, which is respectively connected to the determination module and the prediction module, and is used to select a number of harvesting squares according to the key squares and the concerned squares determined based on the adjusted square width; A harvesting module, which is connected to the selection module, and is used to harvest the forage grass in each of the harvesting squares.

[0030] The acquisition module obtains data in the forage grass field operation scene in real time. "Each square to be measured" refers to a plurality of square areas divided in the forage grass field based on a preset square width. The forage grass data in each area will be collected and analyzed separately. These squares are partitions of the entire forage grass field, which can more carefully understand the growth conditions of forage grass in different areas, facilitating subsequent precise management and harvesting decisions. The plant height reflects the growth stage and maturity of the forage grass, and is obtained through a laser range finder; the lesion area shows the degree of damage to the forage grass by diseases, and is identified using image recognition technology; the tiller number reflects the growth density and growth trend of the forage grass, and is counted with the aid of computer vision algorithms; the leaf area refers to the area size of the forage grass leaves, which reflects the photosynthesis ability, growth trend, and potential biomass accumulation of the forage grass, and is identified using image recognition technology.

[0031] The preset square width refers to the width of each square preset when dividing the squares in the forage grass field, which depends on the forage grass variety, the area and terrain of the forage grass field, and is usually set between 1 meter and 10 meters. In this embodiment, it is set to 5 meters, which can not only ensure the representativeness of data collection, but also facilitate actual operation and management, and at the same time can adapt to the growth characteristics and field management requirements of most forage grasses.

[0032] The preset plant height range refers to the height range used to screen out forage that meets the harvesting conditions, which depends on the variety, growth stage, and harvesting purpose of the forage, and is usually set between 30 cm and 80 cm. In this embodiment, it is set to 40 cm to 70 cm to ensure that the forage has reached an appropriate maturity, while avoiding affecting the quality and harvesting efficiency of the forage due to the plant being too tall or too short.

[0033] The preset growth state prediction model is constructed using a hybrid neural network architecture that combines a long short-term memory network (LSTM) and a convolutional neural network (CNN). The CNN is used to extract the spatial features of multi-dimensional data such as the height of the forage plant, the area of the disease spots, the number of tillers, and the leaf area, and then the feature sequence extracted by the CNN is input into the LSTM layer, allowing the LSTM to capture the dynamic change trend of the data according to the time series characteristics, such as analyzing the increase and decrease law of the number of tillers over time. Finally, the features output by the LSTM are integrated through a fully connected layer and mapped to the classification space of the key grids.

[0034] 1. Initial parameters 1.1 CNN layer: Convolution kernel size: Initially set to 3×3; Stride: 1; Padding method: "same", so as to keep the relative stability of the input and output sizes when extracting features; Initial learning rate: 0.001; The weights of the convolution kernel are initialized using the Xavier normal distribution with random initialization; The initial value of the bias term is set to 0.

[0035] 1.2 LSTM layer: Initial number of hidden layer units: 64, which can balance the fitting ability and computational complexity of the model; The weight matrix is initialized using the Xavier normal distribution with random initialization; Initial learning rate: 0.001; Initial value of the bias term of the forget gate: 1, to help the LSTM better retain memory information in the initial stage of training.

[0036] 1.3 Fully connected layer: Initial value of the number of neurons: 128; Initial learning rate: 0.001; The weight matrix uses the He normal distribution with random initialization; Initial value of the bias term; 0.

[0037] 2. Training process Data Preparation: Collect historical forage growth data, and construct a training set with data such as plant height, lesion area, tiller number, and leaf area collected according to the adjusted grid width. Based on the forage growth law and expert experience, label whether the sample is a key grid, and divide the training set, validation set, and test set according to the ratio of 7:2:1.

[0038] Forward Propagation and Loss Calculation: Input the training data into the model. After the CNN extracts spatial features, the feature sequence enters the LSTM to capture temporal features, and finally the fully connected layer outputs the prediction probability of the key grid. Use the cross-entropy loss function to measure the difference between the prediction probability and the true label, and calculate the loss value.

[0039] Backward Propagation and Optimization: Use the Adam optimization algorithm to update the model parameters by backward propagation according to the calculated loss value. During the training cycle (100 epochs), adjust the learning rate according to the loss change of the validation set. If the validation set loss does not decrease for 5 consecutive epochs, multiply the learning rate by 0.1 to promote the model to gradually converge.

[0040] Regularization and Early Stopping: To prevent overfitting, add L2 regularization terms to the CNN and LSTM layers, and set the regularization coefficient to 0.0001; at the same time, set the early stopping mechanism. When the validation set loss does not decrease for 10 consecutive epochs, stop training and save the model parameters with the best performance on the validation set.

[0041] 3. Trained Model After training, the model solidifies the weight and bias parameters of each layer.

[0042] The CNN layer can accurately extract the spatial features of data such as plant height. For example, it can identify the lesion edge features in the lesion area image; The LSTM layer masters the dynamic feature law of the data in the time dimension, such as knowing the trend that the tiller number first increases and then stabilizes over time; The fully connected layer can accurately map these fused features to the classification results of the key grids. For the input data such as plant height, it can stably output the probability value of each grid being a key grid.

[0043] 4. Final Output For the data of plant height, lesion area, tiller number, and leaf area collected based on the adjusted grid width, the model finally outputs the probability value of each grid to be tested being a key grid. By setting a probability threshold (0.5), the grids with a probability greater than the threshold are determined as key grids, thus realizing the accurate prediction of key grids.

[0044] By obtaining the plant height, lesion area, tiller number, and leaf area of forage grass in real time. Then, based on the collected plant height and the preset plant height range, preliminary screening is carried out to obtain a number of temporary grids. According to the lesion area and tiller number of the temporary grids, a number of marked grids are further determined. Combining the tiller number and leaf area of the marked grids, a number of concerned grids are determined. According to the positions of the concerned grids, the preset grid width is adjusted to obtain the adjusted grid width. The plant height, lesion area, tiller number, and leaf area collected based on the adjusted grid width are input into the preset growth state prediction model to predict a number of key grids. According to the key grids and the concerned grids determined based on the adjusted grid width, a number of harvesting grids are selected. Finally, the forage grass in the harvesting grids is harvested.

[0045] By collecting key data, high-quality forage grass areas are screened out; First, through preliminary screening based on plant height and lesion area, areas that meet the height requirements and have fewer pests and diseases are quickly locked; Then, combined with the lesion area and tiller number, marked grids that are healthy and growing well are determined. Then, according to the tiller number and leaf area, concerned grids with high biomass and excellent quality are determined; According to the aggregation degree of the concerned grids in terms of time and space, the grid width is adjusted to optimize the harvesting plan; Finally, combined with the key grids and concerned grids predicted by the growth state prediction model, the harvesting area is accurately selected, effectively solving the problems of uneven forage grass quality and low harvesting efficiency caused by blind harvesting and over-reliance on static models.

[0046] Specifically, the determination module includes: A normalization processing unit for normalizing all the lesion areas within the preset determination duration to obtain an area normalization data set, and normalizing all the tiller numbers within the preset determination duration to obtain a tiller normalization data set; A determination unit, which is connected to the normalization processing unit, for determining a number of the marked grids according to the lesion normalization data set and the tiller normalization data set.

[0047] The preset determination duration is a time period preset for determining the growth state of forage grass, which depends on the growth speed, growth cycle of forage grass, as well as the frequency and precision requirements of the harvesting operation, and is usually set between 24 hours and 7 days. In this embodiment, it is set to 3 days, which can not only ensure that enough data samples are collected to reflect the change trend of the forage grass growth state, but also not miss the critical period of forage grass growth due to too long a duration.

[0048] By normalizing all the lesion areas and all the tiller numbers within a preset determination duration respectively, an area normalization data set and a tiller normalization data set are obtained, that is, the lesion areas and the tiller numbers are respectively scaled to between 0 and 1 to eliminate the dimensional and order-of-magnitude differences between different data. This is prior art and will not be elaborated here. Then, a number of marked squares are determined based on these two normalization data sets.

[0049] By calculating the correlation coefficient of the two normalization data sets, their correlation can be quantified. When determining the marked squares based on the correlation coefficient, only the regions with weak correlation between the lesion area and the tiller number are retained, which can screen out the regions where the forage grass grows stably and the influence of lesions is small, and can effectively screen out the marked squares that meet the requirements to ensure the quality of the forage grass.

[0050] Please continue to refer to Figure 2 as shown, which is the determination logic diagram of the determination unit for determining the marked squares in this embodiment; The determination unit includes: A correlation calculation sub-unit for calculating the correlation coefficient of the lesion normalization data set and the tiller normalization data set to obtain the change correlation degree; A determination sub-unit, which is connected to the correlation calculation sub-unit, for determining the temporary square as the marked square when the change correlation degree is less than a preset correlation degree threshold to determine a number of marked squares.

[0051] The preset correlation degree threshold is a reference value for judging the correlation between the lesion area and the tiller number, which depends on factors such as the growth characteristics of the forage grass variety and the law of disease influence, and is usually set between -1 and 1. In this embodiment, it is set to 0.5, which can effectively screen out the temporary squares with weak correlation between the lesion area and the tiller number, ensuring that the growth condition of the forage grass in the marked squares is more stable and reliable.

[0052] By calculating the correlation coefficient of the area normalization data set and the tiller normalization data set, the change correlation degree is obtained. If the change correlation degree is less than the preset correlation degree threshold, the corresponding temporary square is determined as the marked square, thereby gradually screening out a number of marked squares that meet the requirements.

[0053] By calculating the correlation coefficient of the normalized lesion data set and the tiller data set, the strength of the linear relationship between the two can be quantified. When the change correlation degree is less than the preset correlation degree threshold, it indicates that there is a weak correlation between the lesion area and the tiller number. Such a logical relationship means that the appearance of the lesion has little influence on the tillering, so the temporary square can be determined as the marked square, which can effectively screen out the regions where the forage grass grows stably and is less affected by the lesions.

[0054] Specifically, the determination module includes: The tiller fluctuation value calculation unit is used to calculate the standard deviation of the tiller numbers at each moment from the initial moment to the preset determined duration, and obtain a number of tiller fluctuation values; The leaf area fluctuation value calculation unit is used to calculate the standard deviation of the leaf areas at each moment from the initial moment to the preset determined duration, and obtain a number of leaf area fluctuation values; The determination unit is respectively connected to the tiller fluctuation value calculation unit and the leaf area fluctuation value calculation unit, and is used to determine a number of concerned grids according to all the tiller fluctuation values and all the leaf area fluctuation values.

[0055] The preset determined duration is the time period for calculating the changes between the tiller numbers and the leaf areas, which depends on the stability of the forage growth and the precision requirements of the harvesting operation, and is usually set between 3 days and 10 days. In this embodiment, it is set to 5 days, which can timely reflect the fluctuation of the forage growth while ensuring the representativeness of the data.

[0056] By calculating the standard deviation of the tiller numbers at each moment from the initial moment to the preset determined duration, a number of tiller fluctuation values are obtained; then, the standard deviation of the leaf areas at each moment from the initial moment to the preset determined duration is calculated, and a number of leaf area fluctuation values are obtained; finally, a number of concerned grids are determined according to all the tiller fluctuation values and the leaf area fluctuation values.

[0057] By calculating the standard deviation of the tiller numbers and the leaf areas within the preset determined duration, the fluctuations of the tiller numbers and the leaf areas can be quantified. The fluctuation of the tiller numbers reflects the stability of the tiller growth of the forage, while the fluctuation of the leaf areas reflects the change of the photosynthesis ability of the forage. Both reflect the health status and growth trend of the forage during the growth process. Combining these two fluctuation values can more comprehensively evaluate the growth state of the forage.

[0058] Please continue to refer to Figure 3 as shown, which is the determination logic diagram of the concerned grids determined by the determination unit in this embodiment; The determination unit includes: The tiller change curve drawing sub-unit is used to draw the curve of the change of all the tiller fluctuation values over time within the preset determined duration, and obtain the tiller change curve; The leaf area change curve drawing sub-unit is used to draw the curve of the change of all the leaf area fluctuation values over time within the preset determined duration, and obtain the leaf area change curve; The synchronization degree calculation sub-unit is respectively connected to the tiller change curve drawing sub-unit and the leaf area change curve drawing sub-unit, and is used to calculate the cosine similarity of the tiller change curve and the leaf area change curve, and obtain the change synchronization degree; A determination subunit, which is connected to the synchronization degree calculation subunit, is used to determine the marked square as the concerned square when the change synchronization degree is greater than a preset synchronization degree threshold, so as to determine a number of concerned squares.

[0059] The preset synchronization degree threshold is a standard value used to determine whether the synchronization of the tiller change curve and the leaf area change curve meets the standard. It depends on the growth characteristics of the forage grass, the data acquisition accuracy, and the requirements of the harvesting decision for synchronization. It is usually set between 0.7 and 0.9. In this embodiment, it is set to 0.8, which can effectively screen out the squares with a high synchronization degree of the tiller number and the leaf area change as the concerned squares while ensuring the determination accuracy.

[0060] By relying on all the tiller fluctuation values and their corresponding time points within a preset determination duration, a curve that can intuitively show the trend of tiller fluctuation changing with time is drawn, that is, the tiller change curve; similarly, the leaf area change curve is drawn. Subsequently, vectorization processing is respectively performed on these two curves to obtain the corresponding vector forms, which is prior art and will not be elaborated here; then, the cosine similarity between the tiller change curve vector and the leaf area change curve vector is calculated to obtain the change synchronization degree; finally, when the change synchronization degree exceeds the preset synchronization degree threshold, this marked square is determined as the concerned square to determine a number of concerned squares.

[0061] By drawing the curves of the tiller fluctuation value and the leaf area fluctuation value changing with time, and calculating the cosine similarity of the two curves, the synchronization degree between the tiller change and the leaf area change is quantified. The tiller number and the leaf area are both key indicators for measuring the growth status of the forage grass, and their synchronous changes usually reflect the coordination of the forage grass in aspects such as nutrient utilization efficiency and photosynthesis ability during the growth process. When the change synchronization degree is greater than the preset synchronization degree threshold, it indicates that the forage grass in this marked square has good consistency in tiller growth and leaf expansion, which often means that the forage grass grows healthily and has excellent quality, meeting the growth characteristics of high-quality forage grass, so it is determined as the concerned square.

[0062] Specifically, the adjustment module includes: A dispersion calculation unit, which is used to calculate the standard deviation of the positions of each of the concerned squares to obtain the dispersion; A dispersion deviation calculation unit, which is connected to the dispersion calculation unit, is used to calculate the relative deviation between the dispersion and the preset dispersion threshold when the dispersion is greater than the preset dispersion threshold, so as to obtain the dispersion deviation; An adjustment unit, which is connected to the dispersion deviation calculation unit, is used to adjust the preset square width according to the dispersion deviation and the preset dispersion deviation threshold when the dispersion deviation is greater than the preset dispersion deviation threshold, so as to obtain the adjusted square width.

[0063] The preset dispersion threshold is a standard value used to measure the dispersion degree of the concerned grids, which depends on the distribution characteristics of the forage in the field, the uniformity of forage growth, and the requirements of the harvesting operation for the forage coverage rate. It is usually set between 1.0 and 3.0. In this embodiment, it is set to 1.5, which can better distinguish whether the distribution of the concerned grids is too dispersed. When the dispersion degree exceeds this value, it indicates that the grid distribution may be too sparse, and the grid width needs to be adjusted to optimize the distribution.

[0064] The preset dispersion deviation threshold is a standard value used to determine whether the relative deviation between the dispersion degree and the preset dispersion threshold is acceptable. It depends on the stability of forage growth, the accuracy requirements of the harvesting operation, and the adaptability of the system to the changes in forage distribution, etc. It is usually set between 10% and 30%. In this embodiment, it is set to 20%, which can provide a certain degree of flexibility while ensuring the accuracy of the harvesting operation to cope with the natural fluctuations and uncertainties of forage distribution.

[0065] The dispersion degree is obtained by calculating the standard deviation of the positions of each concerned grid. When the dispersion degree is greater than the preset dispersion threshold, the relative deviation between the dispersion degree and the preset dispersion threshold is then calculated to obtain the dispersion deviation. Then, when the dispersion deviation is less than the preset dispersion deviation threshold, the preset grid width is adjusted according to the dispersion deviation and the preset dispersion deviation threshold, and finally the adjusted grid width is obtained.

[0066] The dispersion degree is obtained by calculating the standard deviation of the positions of the concerned grids. The smaller the standard deviation, the more concentrated the concerned grids are; on the contrary, the larger the standard deviation, the more dispersed they are. When the dispersion degree is greater than the preset dispersion threshold, the relative deviation between the dispersion degree and the preset dispersion threshold is then calculated. When the dispersion deviation is greater than the preset dispersion deviation threshold, it indicates that the concerned grids are relatively dispersed. At this time, the preset grid width is adjusted to increase the width to divide the area more finely and improve the accuracy of the harvesting plan.

[0067] Specifically, the adjustment unit includes: A uniform index calculation sub-unit, which is used to mark all timestamps during the preset adjustment duration when the dispersion deviation is greater than the preset dispersion deviation threshold. When the number of all timestamps is greater than the preset number threshold, the time intervals between all adjacent timestamps are calculated, and the ratio of the standard deviation to the mean value of the time intervals is calculated to obtain the uniform index; An adjustment factor calculation sub-unit, which is connected to the uniform index calculation sub-unit, and is used to calculate the relative deviation between the dispersion deviation and the preset dispersion deviation threshold to obtain the adjustment factor when the uniform index is less than the preset uniform index threshold; An adjustment sub-unit, which is connected to the adjustment factor calculation sub-unit, and is used to increase the preset grid width according to the adjustment factor and the preset adjustment coefficient to obtain the adjusted grid width.

[0068] The preset adjustment duration is the time period used to statistically analyze and evaluate the occurrence of dispersion deviation, which depends on factors such as the dynamic characteristics of forage growth, the frequency of harvesting operations, and the system's response speed requirements for changes in forage distribution. It is usually set between 1 day and 7 days. In this embodiment, it is set to 3 days, which can timely capture the changing trend of forage distribution density.

[0069] The preset quantity threshold is the reference value used to determine whether the number of occurrences of dispersion deviation greater than the preset dispersion deviation threshold in the preset market is normal. It depends on the frequency of possible uneven distribution during forage growth and the system's requirements for the data sample size. It is usually set between 3 and 10. In this embodiment, it is set to 5, which can evaluate the frequency of dispersion deviation.

[0070] The preset uniformity index threshold is the reference value used to judge the uniformity of time intervals. It depends on the time distribution characteristics of the occurrence of dispersion deviation during forage growth and the requirements of harvesting operations for the uniformity of time intervals. It is usually set between 0.1 and 0.5. In this embodiment, it is set to 0.3, which can effectively distinguish the uniformity and non-uniformity of time intervals. When the uniformity index is less than 0.3, it indicates that the time intervals are relatively uniform.

[0071] The preset adjustment coefficient is the coefficient used to adjust the width of the preset grid. It depends on the spatial characteristics of forage distribution and the requirements of harvesting operations for accuracy. It is usually set between 0.1 and 0.5. In this embodiment, it is set to 0.3, which can ensure the adjustment effect while avoiding excessive changes in the grid width caused by over-adjustment.

[0072] By marking all the timestamps with dispersion deviation greater than the preset dispersion deviation threshold within the preset adjustment duration. When the number of these timestamps exceeds the preset quantity threshold, calculate the time intervals between all adjacent timestamps, and further calculate the ratio of the standard deviation to the mean of these time intervals to obtain the uniformity index. If the uniformity index is less than the preset uniformity index threshold, calculate the relative deviation of the dispersion deviation from the preset dispersion deviation threshold to obtain the adjustment factor. Finally, increase the width of the preset grid according to the adjustment factor and the preset adjustment coefficient to obtain the adjusted grid width.

[0073] By marking relevant timestamps within a preset adjusted duration and calculating the uniformity index of the time intervals when the quantity meets the standard, the time regularity of the occurrence of the dispersion deviation can be accurately evaluated. When the uniformity index is less than the preset threshold, it indicates that the time intervals are relatively uniform, meaning that the occurrence time distribution of the dispersion deviation exceeding the preset threshold within the preset adjusted duration is regular. This implies that the non-uniformity of the forage distribution shows a certain stability in time, and at the same time reflects the stability of the system's monitoring of the forage distribution, indicating that the system can reliably capture the signal of the change in the forage distribution. Calculating the relative deviation between the dispersion deviation and the preset threshold as an adjustment factor can quantify the adjustment amplitude. According to the adjustment factor and the preset adjustment coefficient, increasing the width of the preset grid can expand the harvesting range, make the forage distribution in each grid more uniform, and reduce the harvesting problems caused by uneven distribution.

[0074] Specifically, the selection module includes: A ratio calculation unit for calculating the ratio of the number of the key grids to the number of the concerned grids to obtain a quantity ratio. A coincidence degree calculation unit connected to the ratio calculation unit. When the quantity ratio is greater than the minimum value of the preset ratio range and less than the maximum value of the preset ratio range, it is used to count the number of overlapping grids among all the key grids and all the concerned grids to obtain an overlapping quantity. When the number of key grids is greater than the number of concerned grids, it calculates the ratio of the overlapping quantity to the number of key grids to obtain a coincidence degree. Or, when the number of key grids is less than the number of concerned grids, it calculates the ratio of the overlapping quantity to the number of concerned grids to obtain a coincidence degree. A selection unit connected to the coincidence degree calculation unit. When the coincidence degree is greater than the preset coincidence degree threshold, it determines the key grids as the harvesting grids to select a number of harvesting grids.

[0075] The preset ratio range is an interval used to determine whether the ratio of the number of key grids to the number of concerned grids is reasonable, depending on the growth distribution characteristics of the forage, the efficiency requirements of the harvesting operation, and the consistency requirements of the forage quality. It is usually set between 0.5 and 2.0. In this embodiment, it is set to [0.8, 1.2] to ensure that the number of key grids is not too large or too small relative to the number of concerned grids.

[0076] The preset coincidence degree threshold is a reference value used to decide whether to select key grids or concerned grids as harvesting grids, depending on the consistency requirements of the forage growth state, the accuracy of the harvesting operation, and the sensitivity of the forage quality to the coincidence degree. It is usually set between 0.6 and 0.9. In this embodiment, it is set to 0.7, which can provide a certain degree of flexibility while ensuring the accuracy of the selection of the harvesting grids.

[0077] By calculating the ratio of the number of key squares to the number of concerned squares, the quantity ratio is obtained. If the quantity ratio is less than the minimum value of the preset ratio range or greater than the maximum value of the preset ratio range, the concerned square is determined as a harvesting square. However, if the ratio comparison result shows that the quantity ratio is between the minimum and maximum values of the preset ratio range, the number of overlapping squares among all the key squares and all the concerned squares is counted to obtain the overlapping quantity. If the number of key squares is greater than the number of concerned squares, the ratio of the overlapping quantity to the number of key squares is calculated to obtain the overlapping degree; otherwise, the ratio of the overlapping quantity to the number of concerned squares is calculated to obtain the overlapping degree. Then, the overlapping degree is calculated. When the overlapping degree is greater than the preset overlapping degree threshold, the key square is determined as a harvesting square; when the overlapping degree is less than the preset overlapping degree threshold, the concerned square is determined as a harvesting square.

[0078] By comprehensively considering the quantity ratio and the overlapping degree, first judge whether the quantity ratio is within a reasonable range. If it exceeds the range, directly determine that the concerned square is a harvesting square, which can quickly screen out the situations that obviously do not meet the quantity requirements. If the quantity ratio is within a reasonable range, then further judge through the overlapping degree. When the number of key squares and the number of concerned squares are different, different calculation methods are adopted, ensuring the accuracy of the overlapping degree, thus more objectively reflecting the overlapping situation between the two, improving the accuracy and reliability of determining the harvesting square, and avoiding misjudgment. It reduces human intervention and errors, improves the efficiency and quality of selecting the harvesting square, ensures the objectivity and accuracy of the data, and provides strong support for the subsequent harvesting operation.

[0079] Specifically, the preliminary screening module includes: A height comparison unit for comparing the plant height with the preset plant height range to obtain a height comparison result; A preliminary screening unit connected to the height comparison unit for preliminarily screening a number of temporary squares according to the lesion area when the height comparison result shows that the plant height is greater than the minimum value of the preset plant height range and less than the maximum value of the preset plant height range.

[0080] By comparing the plant height with the preset plant height range, a height comparison result is obtained. If the result shows that the plant height is within the preset range, then a preliminary screening is carried out in combination with the lesion area to determine a number of temporary squares.

[0081] Through rough screening by comparing the plant height, the plant height reflects the growth stage and development of the forage grass. Plants that are too tall or too short may have problems of over-ripeness or poor growth respectively, affecting the quality and nutritional value. The lesion area reflects the health status of the forage grass. Too many lesions will reduce the quality and yield. Therefore, first conduct a preliminary screening by height and then conduct a secondary screening in combination with the lesion area, which can accurately lock in the forage grass area that meets the requirements, improve the harvesting efficiency, and ensure the forage grass quality.

[0082] Please continue to refer to Figure 4 as shown, which is the decision logic diagram of the temporary grid initially screened by the initial screening unit of this embodiment; The initial screening unit includes: A lesion comparison unit for comparing the lesion area with a preset lesion area threshold to obtain a lesion comparison result; An initial screening sub-unit, which is connected to the lesion comparison unit, and is used to determine the grid to be measured as the temporary grid when the lesion comparison result shows that the lesion area is less than the preset lesion area threshold, so as to initially screen a number of temporary grids.

[0083] The preset lesion area threshold refers to the benchmark value for judging whether the forage is healthy, which depends on the disease resistance, growth stage and use of the forage variety, and is usually set between 5% and 15%. In this embodiment, it is set to 10%, which can reduce the situation of misjudging as unqualified due to small lesion area as much as possible while ensuring the quality of the forage, and improve the accuracy of screening.

[0084] After the plant height meets the harvesting requirements, the lesion area is compared with the preset lesion area threshold. If the lesion area is less than the preset threshold, the grid to be measured is finally determined as a temporary grid, so as to gradually initially screen a number of temporary grids.

[0085] By screening the lesion area, the lesion area is a key indicator for evaluating the health status of the forage. An overly large lesion area usually indicates that the forage is invaded by diseases and reduces the quality of the forage. When the lesion area is less than the preset lesion area threshold, the forage seriously affected by diseases can be effectively excluded, and the forage area meeting the height and health requirements can be efficiently screened, improving the harvesting efficiency and the quality of the forage.

[0086] The above are only the preferred embodiments of the present invention and are not used to limit the present invention; for those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A forage harvesting system based on a growth state prediction model, characterized in that: include: A collection module is used to obtain in real time the plant height, diseased spot area, tiller number and leaf area of ​​the forage grass in each square to be tested based on a preset square width; A primary screening module, which is connected to the collection module and is used to obtain a number of temporary grids by primary screening according to the plant height, the diseased spot area and a preset plant height range; A determination module, which is connected to the collection module and the primary screening module respectively, and is used to determine a number of marked squares according to the diseased spot area and the tillering number of each temporary square; A determination module, which is connected to the acquisition module and the determination module respectively, and is used to determine a number of focus squares according to the tillering number and the leaf area of ​​each marked square; An adjustment module, connected to the determination module, for adjusting the preset square width according to the position of each of the focus squares to obtain an adjusted square width; A prediction module, which is connected to the collection module and the adjustment module respectively, and is used to input the plant height, the diseased spot area, the tiller number and the leaf area collected based on the adjusted grid width into a preset growth state prediction model to predict a number of key grids; A selection module, which is connected to the determination module and the prediction module respectively, and is used to select a number of harvesting squares according to the key squares and the concerned squares determined based on the adjustment square width; A harvesting module is connected to the selection module and is used for harvesting the grass in each harvesting grid.

2. The forage harvesting system based on the growth state prediction model according to claim 1, characterized in that: The determination module comprises: A normalization processing unit, used to normalize all the lesion areas within a preset determination time to obtain an area normalized data set, and to normalize all the tiller numbers within a preset determination time to obtain a tiller normalized data set; A determination unit is connected to the normalization processing unit and is used to determine a number of the marked squares according to the lesion normalization data set and the tillering normalization data set.

3. The forage harvesting system based on the growth state prediction model according to claim 2, characterized in that: The determination unit comprises: A correlation calculation subunit is used to calculate the correlation coefficient between the lesion normalized data set and the tiller normalized data set to obtain a change correlation; The determination subunit is connected to the correlation calculation subunit and is used to determine the temporary square as the marked square when the change correlation is less than a preset correlation threshold, so as to determine a plurality of marked squares.

4. The forage harvesting system based on the growth state prediction model according to claim 3, characterized in that: The determination module comprises: A tillering fluctuation value calculation unit is used to calculate the standard deviation of the tillering number at each time from the initial time to a preset determined time length, and obtain a plurality of tillering fluctuation values; A blade area fluctuation value calculation unit, used to calculate the standard deviation of the blade area at each moment from the initial moment to the preset determined time length, and obtain a plurality of blade area fluctuation values; A determination unit is connected to the tillering fluctuation value calculation unit and the leaf area fluctuation value calculation unit respectively, and is used to determine a number of focus grids according to all the tillering fluctuation values ​​and all the leaf area fluctuation values.

5. The forage harvesting system based on the growth state prediction model according to claim 4, characterized in that: The determining unit comprises: A tillering change curve drawing subunit is used to draw a curve showing the change of all the tillering fluctuation values ​​over time within the preset determined time period to obtain a tillering change curve; A blade area variation curve drawing subunit is used to draw a curve showing the variation of all the blade area fluctuation values ​​over time within the preset determined time period to obtain a blade area variation curve; a synchronization degree calculation subunit, which is connected to the tillering change curve drawing subunit and the leaf area change curve drawing subunit respectively, and is used to calculate the cosine similarity of the tillering change curve and the leaf area change curve to obtain the change synchronization degree; The determination subunit is connected to the synchronization calculation subunit and is used to determine the marked square as the focus square when the change synchronization degree is greater than a preset synchronization degree threshold, so as to determine a plurality of focus squares.

6. The forage harvesting system based on the growth state prediction model according to claim 5, characterized in that: The adjustment module comprises: A dispersion calculation unit, used to calculate the standard deviation of the position of each of the focus squares to obtain a dispersion; a dispersion deviation calculation unit connected to the dispersion calculation unit, and used to calculate the relative deviation between the dispersion and the preset dispersion threshold when the dispersion is greater than the preset dispersion threshold, so as to obtain the dispersion deviation; The adjustment unit is connected to the dispersion deviation calculation unit and is used to adjust the preset square width according to the dispersion deviation and the preset dispersion deviation threshold to obtain the adjusted square width when the dispersion deviation is greater than the preset dispersion deviation threshold.

7. The forage harvesting system based on the growth state prediction model according to claim 6, characterized in that: The adjustment unit comprises: A uniformity index calculation subunit is used to mark all time stamps whose dispersion deviation is greater than the preset dispersion deviation threshold within the preset adjustment time length, and when the number of all time stamps is greater than the preset number threshold, calculate the time intervals of all adjacent time stamps, and calculate the ratio of the standard deviation of the time interval to the mean to obtain a uniformity index; an adjustment factor calculation subunit, connected to the uniformity index calculation subunit, for calculating a relative deviation between the dispersion deviation and the preset dispersion deviation threshold value when the uniformity index is less than a preset uniformity index threshold value, to obtain an adjustment factor; The adjustment subunit is connected to the adjustment factor calculation subunit and is used to increase the preset square width according to the adjustment factor and the preset adjustment coefficient to obtain the adjusted square width.

8. The forage harvesting system based on the growth state prediction model according to claim 7, characterized in that: The selection module comprises: A ratio calculation unit, used for calculating the ratio of the number of the key grids to the number of the concerned grids to obtain a quantity ratio; an overlap calculation unit connected to the ratio calculation unit, for counting the number of overlapped squares in all the key squares and all the concerned squares to obtain the overlap number when the number ratio is greater than the minimum value of the preset ratio range and the number ratio is less than the maximum value of the preset ratio range, and for calculating the ratio of the overlapped number to the number of key squares to obtain the overlap when the number of key squares is greater than the number of concerned squares, or for calculating the ratio of the overlapped number to the number of concerned squares to obtain the overlap when the number of key squares is less than the number of concerned squares; A selection unit is connected to the overlap calculation unit and is used to determine that the key square is the harvesting square when the overlap is greater than a preset overlap threshold, so as to select a plurality of harvesting squares.

9. The forage harvesting system based on the growth state prediction model according to claim 8, characterized in that: The primary screening module comprises: A height comparison unit, used to compare the plant height with the preset plant height range to obtain a height comparison result; The primary screening unit is connected to the height comparison unit and is used to obtain a plurality of temporary grids according to the diseased spot area when the height comparison result shows that the plant height is greater than the minimum value of the preset plant height range and the plant height is less than the maximum value of the preset plant height range.

10. The forage harvesting system based on the growth state prediction model according to claim 9, characterized in that: The primary screening unit comprises: A lesion comparison unit, used to compare the lesion area with a preset lesion area threshold to obtain a lesion comparison result; The primary screening unit is connected to the lesion comparison unit and is used to determine that the square to be tested is the temporary square when the lesion comparison result shows that the lesion area is smaller than the preset lesion area threshold, so as to obtain a number of temporary squares through primary screening.

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