124 results about "Row crop" patented technology

A computer-implemented method for crop type identification using satellite observation and weather data. The method includes extracting current and historical data from pixels of satellite images of a target region, generating temporal sequences of vegetation indices, based on the weather data, converting each timestamp of the temporal sequences into a modified temporal variable correlating with actual crop growth, training a classifier using a set of historical temporal sequences of vegetation indices with respect to the modified temporal variable as training features and corresponding historically known crop types as training labels, identifying a crop type for each pixel location within the satellite images using the trained classifier and the historical temporal sequences of vegetation indices with respect to the modified temporal variable for a current crop season, and estimating a crop acreage value by aggregating identified pixels associated with the crop type.

The invention relates generally to harvesters and, more particularly, to a combine for harvesting row crops, which has been modified, to collect and measure grain from continuously from a plurality of strip test plots.

The invention discloses a method for efficiently reducing the continuous cropping obstacles of greenhouse soil, relating to the technical field of high-efficiency facility agricultural cultivation and meaning a technology of applying calcium cyanamide, organic materials, water and the like in soil before crop sowing or transplanting, and performing a soil reaction in a waiting period in an airtight condition to rapidly and efficiently kill pests in the soil, decrease acidification degree, relieve secondary salinization and increase the utilization rate of nitrogen fertilizer, thereby being capable of efficiently reducing the continuous cropping obstacles of the soil. The method for efficiently reducing the continuous cropping obstacles of greenhouse soil comprises the following steps that: 1) a proper treatment time, wherein soil treatment is performed in the seasons having a daily minimum temperature of not less than 5 DEG C; 2) determination for the dosage of calcium cyanamide, wherein the dosage of calcium cyanamide is usually 20-80 kg in per 667 m<2> of soil; and 3) field operation: (1) broadcasting for organic materials; (2) broadcasting for calcium cyanamide; (3) rotary tillage; (4) furrowing; (5) film coating, water irrigation and sealing; (6) soil reaction; and (7) film opening and airing: after the soil treatment is finished, the film is opened, ventilated or aired, and then crop sowing or transplanting can be performed.

The invention discloses a yield-increasing plantation method for a Chinese medicinal material scutellaria root. The method comprises: (1) a method for interplanting the scutellaria root among wide-row crops such as corn and the like; and (2) a method for interplanting the scutellaria root among narrow-row crops such as soybean and the like. By the yield-increasing plantation method for the Chinese medicinal material scutellaria root, the scutellaria root can be promoted to be planted in a large area in a standardized mode, and the method is a high economic benefit mode for planting the scutellaria root in dry land. For the scutellaria root planted by the method, various indexes meet the content requirements of Good Agricultural Practice (GAP), and the method has the advantages of low investment and high benefit. The plantation of the Chinese medicinal material scutellaria root is rapidly developed to meet the increasing social requirements, so that considerable social and economic benefits can be created and a good ecological environment benefit can be brought.

An auger conveyor (104) is provided for a row crop harvesting head (100) that has a hinged frame (102). The auger conveyor (104) has a first section (122) that is coupled to a second section (120, 124) with a universal joint (130).

Systems and methods for predicting pest susceptibility, comprising steps to receive geocoded geospatial image data of a crop field from sensors, receive microclimate data of the crop field, determine a crop vigor map for the crop field, and then generate a pest susceptibility map utilizing a risk model based on the crop vigor map and the microclimate data. The pest susceptibility map comprises a measure of a susceptibility of a crop in the crop field to one or more crop pests at one or more locations. In some embodiments, the method also comprises steps to generate a treatment plan (e.g., pesticide application) and to estimate an anticipated return on investment (ROI). The system therefore leverages remote-sensed data, machine data, analytics, and machine learning to enable farmers to predict, prevent, and control the outbreak of crop pests to greatest economic effect. Such a system addresses a fundamental problem in agriculture.

The invention provides a crops water forcing index determining device and method. The determining device includes: data collecting and processing units, composed of a data collecting probe and of data collector and calculating unit, respectively and connected with each other; the data collecting probe includes infrared temperature measuring probe, ventilated psychrometer, solar net radiation meter, and wind velocity indicator. It also includes application of determining method in determining crops water forcing index. The monitoring station can be set in any position in the farm, enhancing working efficiency, making it unnecessary bury the system underground, providing convenient condition for agricultural mechanical labor. The system applies CWSI theoretical mode to diagnose water forcing and form an irrigating policy, enhancing diagnosis precision, able to largely reduce the waste in water resource and enhancing the utilization ratio of irrigating water resource.

The invention provides a crop growth trend analysis system comprising an image acquisition module, an internet of things module and an image processing module. The image acquisition module acquires a crop image and then transmits the crop image to the image processing module through the internet of things module. The image processing module performs grayscale processing on the crop image to obtain the pixel amount of each of the three primary colors of R, G and B of the crop image. The pixel amount of each color is compared with the total pixel amount of the image to obtain the duty ratio of each color. The image processing module extracts the crop contour from the crop image, and the image is de-weighted so that that area of the crop is obtained. The area proportion of the crop is obtained by comparing the area of the crop with the total area of the crop image, the monitoring of the growth state of the crop is realized, the growth environment of the crop is timely and automatically adjusted through the terminal equipment and the labor cost is reduced.

There is provided method of analyzing a field of crops using a drone. The method initially includes remotely controlling the drone to fly the drone to adjacent the field of crops. The method further includes remotely controlling the drone to land the drone adjacent the crops. The method further includes remotely controlling the drone to drive the drone on the ground along the crops. The method further includes capturing ground-based images of the crops at an upward view angle using a multispectral camera on-board the drone.

The invention provides a crop yield prediction method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining meteorological information and geographic position information of a to-be-predicted crop in a preset growth period; according to the meteorological information and the geographic position information, predicting the total biomass of the to-be-predicted crop in the preset growth period, wherein the preset growth period is the growth duration of the to-be-predicted crop from planting to maturation; obtaining an environmental fertilizer efficiency yield increasing ratio corresponding to the to-be-predicted crop, wherein the environmental fertilizer efficiency yield-increasing ratio is used for representing the influence of the fertilizer on the growth of the to-be-predicted crop; and adjusting the total biomass according to the environmental fertilizer efficiency yield increase ratio to obtain the predicted yield of the to-be-predicted crop in the preset growth period. When the crop yield is predicted, the influence of different fertilization amounts on the crop yield is considered, and the crop yield in the actual production processcan be predicted more accurately.

The invention belongs to the technical field of agricultural control, and discloses a water-saving irrigation control system and a control method. The water-saving irrigation control system comprises an agricultural data acquisition module, an environmental data acquisition module, a stored water quantity acquisition module, an image acquisition module, a central control module, an image processing module, a crop image extraction module, an analysis module, an irrigation determination module, an irrigation parameter calculation module, a judgment module, an irrigation module and a recovery module. According to the water-saving irrigation control system provided by the invention, crop images are extracted by collecting environment images of a planting area, crop states are analyzed and processed, whether crops need to be irrigated or not is intelligently judged by combining environment temperature and humidity and soil temperature and humidity data, irrigation parameters are automatically calculated based on the corresponding data, and irrigation is carried out, so that water resources can be saved, the inaccuracy of manual judgment is eliminated, and the intelligent degree of water-saving irrigation control is improved.

The invention belongs to the technical field of irrigation, and discloses a water-fertilizer integrated water-saving irrigation control system and method, a medium, equipment and a terminal. The water-fertilizer integrated water-saving irrigation control system comprises a planting area image acquisition module, an image processing module, an image analysis module, a crop information summarization module, a soil humidity monitoring module, a central control module, a meteorological information acquisition module, an evaporation capacity calculation module, an irrigation capacity determination module, a fertilizer selection module and a water-fertilizer integrated irrigation module. According to the water-fertilizer integrated water-saving irrigation control system, the crop information of a planting area is obtained by obtaining and analyzing an visible light image, so that the water demand of crops and the irrigation amount per unit area can be conveniently determined; the evaporation capacity of the planting area is calculated by obtaining the soil humidity of the planting area and meteorological information of the planting area, irrigation information is obtained, the requirement for growth of the crops in the planting area is better met, crop growth is facilitated, and irrigation water can be saved during irrigation.

An embodiment of the invention provides a credit line processing method, a credit line processing device, a crop identification method and a crop identification device. The credit line processing method comprises the steps of: acquiring plot information of a crop plot of a target user; taking plot coordinate information and time information contained in the plot information as input parameters, and calling a crop identification interface to identify crop types; determining a confidence coefficient of the plot information according to the crop type distribution returned by the crop identification interface and the target crop types contained in the plot information; and determining the credit line of the target user based on the confidence coefficient, the plot information and the crop attributes corresponding to the target crop types.

The invention discloses a crop seedling stage drought resistance identification method employing bottom upward percolation re-watering technology. The method comprises following steps: (1) preparing materials; (2) manufacturing water tanks; (3) adding soil into boxes; (4) combing the sowing boxes and the water tanks; (5) sowing seeds; (6) re-watering the seeds; and (7) draining the water. The beneficial effects of the method are that the crop seedling stage drought resistance identification method employs the bottom upward percolation re-watering technology, so that seedling stage drought resistance identification on crops having different germplasms can be performed on a large scale, and the identification is easy; the crops can be simultaneously re-watered in batch through the bottom upward percolation re-watering technology, the influence caused by different watering time in the same repeating process is avoided, and the working efficiency is improved; a problem that heat shock effects on the crops may be easily formed through a soil surface watering manner under a high-temperature and high light intensity condition is overcome; a soil hardening phenomenon is avoided; the physiological metabolism and growth of roots are not influenced; the water content between the sowing boxes and in the sowing boxes are the same; and the accuracy of a test is greatly improved.

The invention relates to a crop disease identification method and system based on DCGAN and RDN, and belongs to the field of agricultural informatization and plant protection, the method comprises the following steps: firstly, carrying out data collection, including a data set disclosed by a network and manually collected data, then, guaranteeing the accuracy and distribution balance of a training data set through the technologies of data visualization, data cleaning, DCGAN data generation and the like; dividing the processed data according to the proportion of 60% of a training set, 20% of a verification set and 20% of a test set; according to the method, a deep residual network (RDN) recognition model is constructed, and after model training parameters are set, a training set and a verification set are loaded for model training; and finally, the trained model is applied to a crop leaf disease identification system for crop disease prediction, and crop disease categories and probabilities are returned by the system. According to the method, various diseases of various crops can be recognized, and particularly, the recognition accuracy under the condition of uneven sample distribution can be improved.

The invention relates to the technical field of agricultural production equipment, and provides a row-controlled detection device, a cultivator, a cultivator-fertilizer and a control method thereof. The row-controlled detection device comprises a mounting seat, an angle sensor and two detection rods, first ends of the two detection rods are hinged to the mounting seat, second ends of the two detection rods are spaced, and the angle sensor is mounted on the mounting seat and used for acquiring the deflection angle of the detection rods relative to the mounting seat. The positions of row-controlled crops can be accurately sensed by the row-controlled detection device to acquire the walking state of mechanical equipment in cultivation operation, row-controlled operation is further accuratelyimplemented, seedling injury caused by cultivating equipment is effectively prevented, and the problem of crop yield reduction caused by seedling injury is effectively prevented.

The invention provides a farming intellectualization method and device and control equipment, and the method comprises the steps: obtaining the real-time monitoring data of a target land parcel in the current growth season of a planted crop based on a yield estimation request in the growth season of the crop, obtaining the corresponding historical monitoring data in a time interval not covered by the real-time monitoring data, and obtaining the real-time monitoring data of the target land parcel in the current growth season of the crop; according to the real-time monitoring data, the historical monitoring data and the yield estimation model of the target land parcel, crop yield estimation is performed to determine the estimated yield of the target land parcel in the current growing season, and crop yield estimation can be dynamically performed in the whole growing season in real time; and according to the estimated yield of the target land parcel in the current growth season, the historical yield information of the crops, the real-time monitoring data and the agricultural knowledge graph information, the first farming task for the target land parcel is issued, so that farming operation can be performed on the target land parcel with low estimated yield in time when the estimated yield is low; the crop yield of a target plot can be greatly improved.