Abstract

The detection of weeds at the stages of cultivation is very important for detecting and preventing plant diseases and eliminating significant crop losses, and traditional methods of performing this process require large costs and human resources, in addition to exposing workers to the risk of contamination with harmful chemicals. To solve the above tasks, also in order to save herbicides and pesticides, to obtain environmentally friendly products, a program for detecting agricultural pests using the classical K-Nearest Neighbors, Random Forest and Decision Tree algorithms, as well as YOLOv5 neural network, is proposed. After analyzing the geographical areas of the country, from the images of the collected weeds, a proprietary database with more than 1000 images for each class was formed. A brief review of the researchers' scientific papers describing the methods they developed for identifying, classifying and discriminating weeds based on machine learning algorithms, convolutional neural networks and deep learning algorithms is given. As a result of the research, a weed detection system based on the YOLOv5 architecture was developed and quality estimates of the above algorithms were obtained. According to the results of the assessment, the accuracy of weed detection by the K-Nearest Neighbors, Random Forest and Decision Tree classifiers was 83.3 %, 87.5 %, and 80 %. Due to the fact that the images of weeds of each species differ in resolution and level of illumination, the results of the neural network have corresponding indicators in the intervals of 0.82–0.92 for each class. Quantitative results obtained on real data demonstrate that the proposed approach can provide good results in classifying low-resolution images of weeds.

Highlights

  • The agricultural sector is one of the main branches of the economy of our country, since this industry annually provides 35–40 % of income to the state budget, and 15 % of the entire labor force of the country is employed in this sector

  • Weed control and monitoring of crop diseases have become an urgent task in the robotization of agriculture [1]

  • The development of a pest control system that performs the detection and removal of weeds is the main area of research in the agricultural industry

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Summary

Introduction

The agricultural sector is one of the main branches of the economy of our country, since this industry annually provides 35–40 % of income to the state budget, and 15 % of the entire labor force of the country is employed in this sector. Weed control and monitoring of crop diseases have become an urgent task in the robotization of agriculture [1]. Monitoring of diseases and weeds at the stages of cultivation is very important for detecting and preventing diseases and eliminating significant crop losses, and traditional methods of performing this process require high costs and human resources, besides exposing workers to the risk of contamination with harmful chemicals. The development of a pest control system that performs the detection and removal of weeds is the main area of research in the agricultural industry. The introduction of intelligent weed detection systems will solve the problem of saving herbicides and pesticides, which are in demand means to combat plant diseases, various weeds and vectors of dangerous diseases in industrial agricultural production

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