A Quadratic Traversal Algorithm of Shortest Weeding Path Planning for Agricultural Mobile Robots in Cornfield

Abstract

In order to improve the weeding efficiency and protect farm crops, accurate and fast weeds removal guidance to agricultural mobile robots is an utmost important topic. Based on this motivation, we propose a time-efficient quadratic traversal algorithm for the removal guidance of weeds around the recognized corn in the field. To recognize the weeds and corns, a Faster R-CNN neural network is implemented in real-time recognition. Then, an ultra-green characterization (EXG) hyperparameter is used for grayscale image processing. An improved OTSU (IOTSU) algorithm is proposed to accurately generate and optimize the binary image. Compared to the traditional OTSU algorithm, the improved OTSU algorithm effectively shortens the search speed of the algorithm and reduces the calculation processing time by compressing the range of the search grayscale range. Finally, based on the contour of the target plants and the Canny edge detection operator, the shortest weeding path guidance can be calculated by the proposed quadratic traversal algorithm. The experimental results proved that our search success rate can reach 90.0% on the testing date. This result ensured the accurate selection of the target 2D coordinates in the pixel coordinate system. Transforming the target 2D coordinate point in the pixel coordinate system into the 3D coordinate point in the camera coordinate system as well as using a depth camera can achieve multitarget depth ranging and path planning for an optimized weeding path.

Machine Learning in Agriculture: A Comprehensive Updated Review

“…The transformation of data gathered by UAVs into meaningful information is, however, still a challenging task, since both data collection and classification need painstaking effort [58]. ML algorithms coupled with imaging technologies or non-imaging spectroscopy can allow for real-time differentiation and localization of target weeds, enabling precise application of herbicides to specific zones, instead of spraying the entire fields [59] and planning of the shortest weeding path [60].…”

Deep neural network based interactive fuzzy Bayesian search algorithm for low‐cost smart farming automation model

“…A quadratic traversal algorithm to obtain a short weeding path for agricultural mobile robots in the cornfields was implemented by Zhang et al 13 The major intention of this paper was to enhance the efficiency during weed removal and to remove the weeds as quickly as possible. Various performance parameters namely success rate, execution time as well as accuracy was computed to determine the performance of the system.…”

Evaporation Forecasting through Interpretable Data Analysis Techniques

“…Machine learning (ML) techniques are efficient tools for solving complex, dynamic, and non-linear problems; they are capable of predicting different parameters through the relationships between inputs and outputs without considering the internal mechanisms of the system [13]. ML has been particularly applied in the field of agriculture [14] for crop management [15], yield prediction [16], disease detection [17], weed detection [18], crop recognition [19], crop quality [20], water management [21], soil management [22], and livestock management [23]. Regarding the evaporation prediction through ML models, some works have been proposed but mainly using ML methods as black box, i.e., the internals.…”