23 results about "Growth parameter" patented technology

The invention relates to the technical field of agricultural planting management, and discloses an intelligent agricultural planting method and system. The method comprises the following steps: analyzing nutritional requirements based on environment sensing and plant image data, and generating nutrition allocation data; carrying out growth prediction through seedling image features and formulating a cultivation standard; spectral parameters are extracted according to environment condition analysis and canopy images, and an environment regulation and control scheme is formed; combining growth state evaluation and pest and disease damage characteristics to calculate health indexes; a management strategy is optimized through growth parameter time sequence analysis; and determining the harvesting opportunity and grade evaluation according to the plant maturity and quality characteristics. According to the application, environment monitoring, growth diagnosis, quality evaluation and market analysis are organically combined, so that more accurate and intelligent agricultural planting decision support is provided.

The invention discloses a plant greenhouse intelligent monitoring and environment optimization management system and method. The method comprises the steps that a detection module obtains environment data, resource data and equipment feedback data of a vegetable greenhouse in real time; the acquisition module acquires vegetable types, growth days and growth parameters; the health prediction module calculates the current growth index according to the growth parameters and predicts the index of the next growth stage; the establishing module calculates a difference value between the predicted growth index and the standard growth index through a target function; the parameter optimization module adopts an improved dung beetle optimization algorithm, adjusts an optimization process according to equipment feedback and outputs optimal cultivation parameters; the condition generation module generates a resource scarcity state according to the resource data and provides adjustment limitation; the processing module adjusts a theoretical range based on the optimal parameter and the resource constraint; and the in-greenhouse adjustment module dynamically adjusts the greenhouse environment according to the optimization result. Through integration of sensor data and an optimization algorithm, dynamic adjustment of the resource ratio in the vegetable greenhouse is realized, and the vegetable growth efficiency is improved.

The invention relates to the technical field of aquaculture, and discloses an ecological aquaculture system, which comprises a culture pond as a basic component of the whole device and used for bearing and assembling a central data processing system and a subordinate lower structural component thereof; the central data processing system is used for receiving, processing and storing data from each monitoring module; the multi-parameter water quality monitoring module monitors key water quality indexes including dissolved oxygen, temperature, pH value, ammonia nitrogen content and nitrite content in real time; the aquatic animal growth monitoring module is used for measuring growth parameters including body length and body weight of aquatic animals through an image recognition technology; through the multi-parameter water quality monitoring device, the culture environment and the state of the aquatic animals can be mastered in real time, so that accurate management is performed, and the disease early warning device can find disease risks in an early stage, take prevention and treatment measures in time and reduce the influence of diseases on the growth of the aquatic animals.

The invention relates to the technical field of agriculture, and provides a greenhouse tomato plant growth parameter monitoring method, device, equipment and product. The method comprises the following steps: preprocessing initial multi-modal data of a tomato plant under multiple view angles; carrying out fusion modeling on the obtained target multi-modal data to obtain three-dimensional reconstruction data of the tomato plant; identifying a key growth part of the tomato plant based on the three-dimensional reconstruction data, and obtaining a target growth parameter of the key growth part; and obtaining a future predicted value of the target growth parameter based on the current time sequence data of the target growth parameter. According to the method, the data acquisition dimension is increased, the influence of mutual overlapping and shielding between plants on the data is avoided through multi-view acquisition, and after three-dimensional reconstruction is performed based on the data, the key parts of the plants can be accurately identified and the target growth parameters of the plants can be acquired; and the future value of the target growth parameter is predicted through the current time sequence data of the target growth parameter, so that the target growth parameter is dynamically monitored.

The invention discloses an Internet of Things supervision method and system applied to strawberry growth management, and relates to the technical field of smart agriculture, and the method comprises the steps: integrating historical strawberry growth full-cycle multi-dimensional data, constructing a strawberry growth knowledge graph, taking different growth stages as nodes, and obtaining the optimal growth interval of each node; tracking a dynamic evolution track of the soil saline-alkali parameters on the time sequence, and constructing a dynamic prediction model of the target soil saline-alkali parameters; based on a multi-dimensional sensor, monitoring growth parameters in real time, inputting a knowledge graph to judge the growth node of the strawberry in the time period, predicting the trend by integrating the growth parameters in the time period and soil saline-alkali parameters, calculating the deviation degree of the growth node in the time period and the optimal growth interval, and evaluating whether the growth parameters in the time period are in a safe range or not; if it is judged that the growth is in the abnormal growth risk period, a layered early warning mechanism is implemented, a case with the highest similarity in the knowledge graph is paired, and a solution is rapidly provided. The method has the beneficial effects that the yield and quality of strawberries are improved.

The invention discloses dsRNA for targeted regulation of a freshwater shrimp PKA-R1 gene and an obtaining method and application of the dsRNA, and belongs to the technical field of biology.Firstly, the freshwater shrimp PKA-R1 gene is obtained, then a gene segment with the sequence of SEQ ID NO: 3 is obtained through RNA interference and other technologies, dsPKA-R1 is synthesized through the gene segment, and after the synthesized dsPKA-R1 is injected into the pericardial cavity of a freshwater shrimp, it is proved that the dsPKA-R1 can slow down growth of the freshwater shrimp through growth parameter comparison.

The invention discloses a multi-crop growth synchronous monitoring system based on remote sensing, and relates to the technical field of agricultural remote sensing monitoring. Comprising a remote sensing data acquisition module, a primary perception and feature preliminary screening module, a depth feature extraction and coupling reasoning module, a primary cross check and uncertainty quantification module, an iterative optimization and secondary check feedback module and a result synthesis and decision output module. According to the multi-crop growth synchronous monitoring system based on remote sensing, synchronous parallel processing of crop identification and growth parameter inversion is realized by constructing a depth feature extraction and coupling reasoning module, and the problem of insufficient monitoring precision caused by similar spectral features under a multi-crop mixed planting condition is effectively solved. According to the system, a crop special model is automatically matched through a dynamic routing mechanism, and the accuracy and reliability of inversion results of growth parameters of different crops are improved in combination with multiple verification and an iterative optimization process.

The present application relates to the field of agricultural technologies, and provides a method, device, equipment and product for monitoring the growth parameters of greenhouse tomato plants. The method includes: preprocessing the initial multi-modal data of tomato plants from multiple perspectives; performing fusion modeling on the obtained target multi-modal data to obtain three-dimensional reconstruction data of tomato plants; identifying key growth parts of tomato plants based on the three-dimensional reconstruction data, and obtaining target growth parameters of the key growth parts; and obtaining future predicted values of the target growth parameters based on the current time-series data of the target growth parameters. The present application increases the data collection dimension, and through multi-perspective collection, it avoids the influence of mutual overlap and occlusion between plants on the data. After three-dimensional reconstruction based on these data, it can accurately identify the key parts of the plants and obtain their target growth parameters, and then predict their future values through the current time-series data of the target growth parameters, realizing dynamic monitoring of the target growth parameters.

The application provides an environment-driven edible mushroom growth prediction method and system, and belongs to the technical field of intelligent agriculture, and comprises the following steps: constructing an environment feature vector corresponding to each time according to environment data in a to-be-predicted period; inputting the environment feature vector into a dynamic parameter mapping model trained based on an actual growth parameter label to obtain a dynamic growth parameter corresponding to each time; calling a dynamic biological growth equation in which a static constant term is reconstructed into a dynamic variable parameter; and assigning the dynamic growth parameter to the dynamic biological growth equation to obtain a biomass growth prediction value at each time. The application fuses the flexibility of data driving and the interpretability of a mechanism model by constructing a mapping relationship between an environment and a mechanism parameter by using machine learning and driving a mechanism equation by using a dynamic parameter, so that high-precision prediction of the biomass of edible mushrooms under a complex dynamic environment is realized.

The invention belongs to the technical field of broiler feeding monitoring, and particularly relates to a broiler feeding prediction system and method based on nutritional requirements. By dynamically monitoring the growth parameters of the broiler chickens and combining historical successful breeding data, accurate prediction and management of broiler chicken breeding are achieved, growth abnormality can be found in time and corresponding measures can be taken by comparing the growth difference between the bred chicken flocks and the successfully bred chicken flocks, the uncertainty in the broiler chicken growth process is effectively reduced, and the breeding efficiency of the broiler chickens is improved. Meanwhile, by adjusting the feed formula, it is ensured that the broiler chickens obtain the optimal nutrition supply, the growth difference between the broiler chickens is further reduced, after feed adjustment, the weight gain condition of the to-be-improved group can be tracked in real time, the optimization effect of the feed formula is ensured, and by means of dynamic evaluation of sectioned verification time periods, the quality of the broiler chickens is improved. The improvement condition can be timely and accurately known, so that the feeding strategy is continuously optimized, and the purpose of improving the overall breeding efficiency is achieved.

The embodiment of the invention relates to the technical field of breeding management, and discloses a multi-dimensional data-based breeding method, which comprises the following steps of: determining feather characteristic parameters of chicks according to feather characteristic information and pixel point sizes; according to the length of the main wing feather and the length of the covered main wing feather, determining the gender and the predicted growth parameter information of the corresponding chicks, and according to the predicted growth parameter information, performing primary screening; inputting the breeding hen image information into a feature recognition model to obtain corresponding breeding hen features; inputting the cockscomb image into a cockscomb recognition model to determine a corresponding cockscomb type; and if the feather color, the chicken plantar color and the cockscomb type are matched with the variety parameters of the corresponding breeding hens, reserving the breeding hens and feeding the breeding hens into cages, and recording individual characteristic parameters. According to the scheme of the embodiment of the invention, through deep fusion of an image recognition technology and multi-dimensional phenotypic data, a complete-cycle accurate breeding system from chicks to breeding hens is constructed, and sex identification automation, variety verification standardization and growth prediction intelligence are realized.

The utility model provides a device for measuring beef cattle growth parameters, and relates to the technical field of beef cattle growth measurement. The device for measuring the growth parameters of the beef cattle comprises a base, a weighing device is embedded in the top of the base, two fixing frames are fixedly connected to the top of the base, a measuring mechanism for measuring the width of the beef cattle is slidably connected to the fixing frames, and the measuring mechanism comprises a two-way screw rotationally arranged on the fixing frames. A bidirectional screw is driven to rotate, the two side faces of a cattle body are clamped under the action of a movable block, an extrusion rod and side transverse plates, the width of the cattle is measured under the action of an infrared distance measuring sensor and a reflecting plate, the weight of the cattle is weighed through a weighing device, and the left and right sides of the cattle are limited through the two side transverse plates; the head of the cattle penetrates through a circular through groove in the movable frame, the head of the cattle is fixed under the action of the fixing mechanism, and it is prevented that the head of the cattle shakes left and right, and measurement of workers is hindered.

The invention relates to the technical field of pregnancy fetus health detection, in particular to a pregnancy fetus intelligent identification and development state analysis system, which comprises a data acquisition and processing module, a fetus positioning analysis module, a development prediction module and a development evaluation and risk early warning module, performing transformation processing and space standardization adjustment on the initial detection data of the pregnancy fetus by using the data acquisition and processing module to obtain target information of the pregnancy fetus; the fetus positioning analysis module outputs contour feature information of a pregnancy fetus in combination with the target information, and obtains a positioning analysis result of the pregnancy fetus; the development prediction module combines a growth parameter prediction model and target information analysis to obtain a growth parameter prediction result of the pregnancy fetus; and the development evaluation and risk early warning module performs analysis and early warning on development conditions and potential risk factors of the pregnant fetuses according to the target information, the positioning analysis result and the growth parameter prediction result. The efficiency and accuracy of fetal monitoring during pregnancy are improved, and a basis is provided for clinical decision making.

The invention discloses an agricultural intelligent cultivation control system and method based on strategy optimization, and belongs to the technical field of agriculture. Related data and plant growth parameters in an agricultural environment are collected through a sensor assembly, and a growth state judgment model is constructed; importing the collected growth parameters into a growth state judgment model for growth state judgment, constructing an energy conversion evaluation model, importing respiration data in the monitored agricultural environment into the energy conversion evaluation model for evaluation, constructing a temperature matching analysis model, and performing temperature matching analysis according to a growth state judgment result and a dark respiration rate. Whether the current temperature is matched with plant growth and physiological activities or not is evaluated, a matched temperature range is analyzed according to a matching judgment result and a temperature optimization algorithm, and crop growth is promoted.

The present application belongs to the technical field of broiler feeding monitoring, and particularly relates to a broiler feeding prediction system and method based on nutritional requirements. The present application realizes accurate prediction and management of broiler feeding by dynamically monitoring the growth parameters of broilers and combining historical successful breeding data. By comparing the growth differences between the breeding chicken group and the successfully bred chicken group, growth abnormalities can be found in time and corresponding measures can be taken, effectively reducing the uncertainty in the growth process of broilers. At the same time, by adjusting the feed formula, the best nutritional supply for broilers is ensured, further reducing the growth differences among broilers. After the feed is adjusted, the weight gain of the group to be improved can be tracked in real time to ensure the optimization effect of the feed formula. Through dynamic evaluation of the sub-section verification period, the improvement situation can be understood in time and accurately, so as to continuously optimize the feeding strategy and achieve the purpose of improving the overall breeding efficiency.

The embodiment of the specification provides a kind of agricultural product monitoring method, the method comprises: obtaining the variety information of agricultural product, planting area information and growth image;Based on variety information, planting area information and growth image, the growth parameter of agricultural product is predicted by vigor prediction model;Wherein, growth parameter includes one or more of the growth cycle, plant height, crown width and flower amount of agricultural product;Vigor prediction model includes feature extraction layer and vigor prediction layer, feature extraction layer is used to process growth image, and determine image feature;Vigor prediction layer is used to process image feature, variety information, planting area information, and determine growth parameter;When growth parameter does not satisfy preset condition, send early warning reminder to target terminal.

The invention discloses a germination process control method and system for germinated food, and the method comprises the steps: building historical germination based on the growth conditions of the germinated food in each growth stage, and storing the reference growth data of the germinated food in each growth stage; collecting growth data of the germinated food in each stage in real time, and obtaining the change rate of each growth parameter of the germinated food; comparing the real-time sprouting food growth data with historical sprouting data of a corresponding growth cycle in a historical sprouting database to obtain an error value; constructing a growth deviation analysis model, and analyzing and acquiring growth deviation factors of each germinated food based on the calculated error values; and generating a germination process influence factor regulation instruction, and dynamically regulating the germination influence factors to control the germination process. The method has the advantages that through real-time monitoring, historical data comparison and LSTM neural network analysis, the growth process of germinated food is accurately regulated and controlled, growth conditions are optimized, and stable food quality is ensured.

This application discloses an intelligent matching method and system for lamb feeding programs based on growth prediction, relating to the field of lamb feeding technology. The method includes: acquiring a set of growth parameters and a set of feeding behavior parameters for the target lamb; predicting growth status and acquiring feeding proficiency parameters and rumen development parameters; acquiring developmental synergy parameters to determine the lamb's developmental pattern; and intelligently matching a lamb feeding program based on the feeding proficiency parameters, rumen development parameters, and the lamb's developmental pattern. This solves the technical problems of traditional lamb feeding programs relying on experience-based judgment, which leads to strong subjectivity and significant lag, and the inability of standardized feeding patterns to meet the individualized needs of lambs.

The invention discloses a crop growth trend prediction method based on ambient light recognition and related equipment thereof, and belongs to the technical field of artificial intelligence. The method comprises the following steps: firstly, collecting growth parameters in a crop growth process and corresponding ambient light parameters; then, the linear correlation degree between the growth parameters and ambient light is calculated through statistical analysis, the linear correlation degree serves as a weighted weight, and weighting processing is carried out on the parameters; thirdly, a model training data set is constructed through the weighted data, and the model training data set is used for training and generating a model capable of predicting the crop growth risk trend; and when a growth trend prediction instruction is received, obtaining to-be-predicted crop growth data, inputting the to-be-predicted crop growth data into the risk trend prediction model, and outputting a crop growth risk trend prediction result. The invention also relates to the technical field of block chains, and the growth data is stored on a block chain network. According to the invention, through deep fusion of ambient light identification and crop growth data, accurate prediction of the crop growth risk trend is realized.

The invention provides a multi-cycle rice drought tolerance evaluation method and system based on a multi-index comprehensive evaluation model, and the method comprises the following steps: obtaining growth parameters of rice in different growth cycles, screening the growth parameters, obtaining corresponding key growth parameters, and calculating the drought tolerance of the rice according to the key growth parameters; according to the key growth parameters, constructing a multi-index comprehensive evaluation model corresponding to different growth cycles of the rice; setting different planting environment parameters, performing experimental cultivation on the to-be-evaluated rice variety, collecting actual key growth parameters and actual planting parameters of the to-be-evaluated rice variety in different growth cycles in the experimental cultivation process, and substituting the actual growth parameters and the actual planting parameters into the multi-index comprehensive evaluation model for calculation, preliminary evaluation results corresponding to different growth cycles are obtained; performing weighted fusion on the plurality of preliminary evaluation results to obtain a final evaluation result; the method provided by the invention can more completely reflect the drought-enduring characteristic of the rice in the whole growth process.

The application belongs to the technical field of intelligent agriculture, and particularly relates to a melon seedling feature recognition and analysis method based on image multi-dimensional features, which first synchronously collects two-dimensional image information and three-dimensional point cloud information of a melon crop seedling raising area, forms multi-modal data and performs spatial calibration and preprocessing, so as to extract two-dimensional semantic features and three-dimensional geometric features of target seedlings, recognize the current growth stage of the target seedlings based on the extracted features, dynamically adjust the fusion weights of the two-dimensional semantic features and the three-dimensional geometric features, and perform feature fusion by using a cross-modal attention mechanism to generate a dynamic fusion feature vector, input the dynamic fusion feature vector into a task recognition model, output the health status and quantitative growth parameters of the target seedlings, realize high-precision, self-adaptive and multi-dimensional perception of the melon seedling morphology and growth health status, and have an important promoting effect on the high-quality development of modern intelligent agriculture.

The invention relates to the technical field of soybean breeding, in particular to a soybean breeding method based on intelligent agricultural application, and aims to solve the technical problems that factors such as field environmental climate and soil fertility are different every year, and environmental factors and growth indexes remarkably related to excellent characters cannot be accurately found during breeding in the prior art. Comprising the following steps: delimiting a measuring area, setting an identifier, configuring a sensor, collecting land parcel data, collecting growth data, summarizing the data, establishing a data analysis model, monitoring soybean growth environment data in real time through the sensor by virtue of an intelligent agricultural technology, and performing big data analysis by combining a data integration platform. According to the method, the growth performance of different soybean plants in a specific environment can be rapidly and accurately evaluated, plants with excellent characters and various growth parameters suitable for the plants can be screened out in a short time, the plants with the excellent characters grow rapidly by maintaining the various growth parameters, and the yield of the soybean plants is improved. And the time required from hybridization to acquisition of stable and excellent varieties is greatly shortened.

The present application provides a crop growth prediction method, device, equipment and medium, wherein the method includes: determining a lossless two-dimensional image data set of an original three-dimensional point cloud data set based on an image backtracking algorithm; fusing the original three-dimensional point cloud data set, the lossless two-dimensional image data set and meteorological factor data, and inverting the growth parameters of different crops in combination with their spatiotemporal specificity to obtain an optimal crop growth parameter multidimensional deep learning model, so as to evaluate the growth parameter information of different crops and obtain the spatiotemporal distribution of the growth parameters of different crops. The present application determines a lossless two-dimensional image data set of an original three-dimensional point cloud data set based on an image backtracking algorithm to obtain more accurate features, and then constructs an optimal crop growth parameter multidimensional deep learning model through the original three-dimensional point cloud data set, the lossless two-dimensional image data set and meteorological factor data to obtain the spatiotemporal distribution of the growth parameters of different crops, thereby improving the accuracy of the crop growth parameter prediction results.