Autonomous detection of crop rows based on adaptive multi-ROI in maize fields

Crop rows detection in maize fields remains a challenging problem due to variation in illumination and weeds interference under field conditions.This study proposed an algorithm for detecting crop rows based on adaptive multi-region of interest(multi-ROI).First,the image was segmented into crop and soil and divided into several horizontally labeled strips.Feature points were located in the first image strip and initial ROI was determined.Then,the ROI window was shifted upward.For the next image strip,the operations for the previous strip were repeated until multiple ROIs were obtained.Finally,the least square method was carried out to extract navigation lines and detection lines in multi-ROI.The detection accuracy of the method was 95.3%.The average computation time was 240.8 ms.The results suggest that the proposed method has generally favorable performance and can meet the real-time and accuracy requirements for field navigation.

Automatic Guidance of an Agricultural Tractor along with the Side Shift Control of the Attached Row Crop Cultivator

This paper presents the automatic guidance system of an agricultural tractor and the side shift control of the attached row crop cultivator using electro-hydraulic actuators. In order to simulate the dynamic behaviour of the tractor along with the attached cultivator, the modified bicycle model was adopted. Steering angle sensor, fibre optic gyroscope （FOG） and RTK-DGPS technologies are assumed for measurements of the steering angle, yaw rate and the lateral position of the tractor, respectively. The kinematics model was used for the implement. In this study four cascade controllers were designed and simulated for tractor guidance which consists ofPD, PD, P and PID controllers. Other PI and PID controllers also had been designed for implement side shifting purpose. Then, these two systems were combined and the performance of the whole system was evaluated through the simulation results. According to the results tractor reaches the desired path after less than 10 seconds. Simulations showed that the maximum deviation of the tractor from the desired path was about 5 cm within this period. And the cultivator blades would follow the predetermined path with steady state error of about 5 cm too.

InstaCropNet:An efficient Unet-Based architecture for precise crop row detection in agricultural applications

Autonomous navigation in farmlands is one of the key technologies for achieving autonomous management in maize fields.Among various navigation techniques,visual navigation using widely available RGB images is a cost-effective solution.However,current mainstream methods for maize crop row detection often rely on highly specialized,manually devised heuristic rules,limiting the scalability of these methods.To simplify the solution and enhance its universality,we propose an innovative crop row annotation strategy.This strategy,by simulating the strip-like structure of the crop row's central area,effectively avoids interference from lateral growth of crop leaves.Based on this,we developed a deep learning network with a dual-branch architecture,InstaCropNet,which achieves end-to-end segmentation of crop row instances.Subsequently,through the row anchor segmen-tation technique,we accurately locate the positions of different crop row instances and perform line fitting.Experimental results demonstrate that our method has an average angular deviation of no more than 2°,and the accuracy of crop row detection reaches 96.5%.

Accurate crop row recognition of maize at the seedling stage using lightweight network

Accurate extraction of crop row is very important for automation of agricultural production.Crop rows are required for accurate machine guidance in agricultural production such as fertilization,plant protection,weeding and harvesting.In this study,an efficient crop row detection algorithm called Crop-BiSeNet V2 was proposed,which combined BiSeNet V2 with a spatial convolutional neural network.The proposed Crop-BiSeNet V2 detected crop rows in color images without the use of threshold and other pre-information such as number of rows.A data set had 2697 maize crop images was constructed in challenging field trial conditions such as variable light,shadows,presence of weeds,and irregular crop shape.The proposed system was experimentally determined to overcome the interference of different complex scenes.And it can be applied to crop rows of different numbers,straight lines and curves.Different analyses were performed to check the robustness of the algorithm.Comparing this algorithm with the Fully Convolutional Networks(FCN)algorithm,it exhibited superior performance and saved 84.85 ms.The accuracy rate reached 0.9811,and the detection speed reached 65.54 ms/frame.The Crop-BiSeNet V2 algorithm proposed in this study show strong generalization performance for seedling crop row recognition.It provides high-reliability technical support for crop row detection research and assists in the study of intelligent field operation machinery navigation.

A unified canopy bidirectional reflectance (BRDF) model for row ceops

Row sowing is a basic crop sowing method in China,and thus an accurate Bidirectional Reflectance Distribution Function (BRDF) model of row crops is the foundation for describing the canopy bidirectional reflectance characteristics and estimating crop ecological parameters.Because of the macroscopically geometric difference,the row crop is usually regarded as a transition between continuous and discrete vegetation in previous studies.Were row treated as the unit for calculating the four components in the Geometric Optical model (GO model),the formula would be too complex and difficult to retrieve.This study focuses on the microscopic structure of row crops.Regarding the row crop as a result of leaves clumped at canopy scale,we apply clumping index to link continuous vegetation and row crops.Meanwhile,the formula of clumping index is deduced theoretically.Then taking leaf as the basic unit,we calculate the four components of the GO model and develop a BRDF model for continuous vegetation,which is gradually extended to the unified BRDF model for row crops.It is of great importance to introduce clumping index into BRDF model.In order to evaluate the performance of the unified BRDF model,the canopy BRDF data collected in field experiment,"Watershed Allied Telemetry Experiment Research (WATER)",from May 30th to July 1st,2008 are used as the validation dataset for the simulated values.The results show that the unified model proposed in this paper is able to accurately describe the non-isotropic characteristics of canopy reflectance for row crops.In addition,the model is simple and easy to retrieve.In general,there is no irreconcilable conflict between continuous and discrete vegetation,so understanding their common and individual characteristics is advantageous for simulating canopy BRDF.It is proven that the four components of the GO model is the basic motivational factor for bidirectional reflectance of all vegetation types.

Natural and anthropogenic rates of soil erosion

Regions of land that are brought into crop production from native vegetation typically undergo a period of soil erosion instability,and long term erosion rates are greater than for natural lands as long as the land continues being used for crop production.Average rates of soil erosion under natural,non-cropped conditions have been documented to be less than 2 Mg ha^(-1) yr^(-1).On-site rates of erosion of lands under cultivation over large cropland areas,such as in the United States,have been documented to be on the order of6 Mg ha^(-1) yr^(-1)or more.In northeastern China,lands that were brought into production during the last century are thought to have average rates of erosion over this large area of as much as 15 Mg ha^(-1) yr^(-1) or more.Broadly applied soil conservation practices,and in particular conservation tillage and no-till cropping,have been found to be effective in reducing rates of erosion,as was seen in the United States when the average rates of erosion on cropped lands decreased from on the order of 9Mg ha^(-1) yr^(-1) to 6 or 7Mg ha^(-1) yr^(-1) between 1982 and 2002,coincident with the widespread adoption of new conservation tillage and residue management practices.Taking cropped lands out of production and restoring them to perennial plant cover,as was done in areas of the United States under the Conservation Reserve Program,is thought to reduce average erosion rates to approximately 1 Mg ha^(-1) yr^(-1) or less on those lands.