Identification of weeds in fields based on computer vision technology

Abstract

The problem of multiple zones in computer vision, including pattern recognition in the agricultural sector, occupies a special place in the field of artificial intelligence in the modern aspect. The object of the study is the recognition of weeds based on deep learning and computer vision. The subject of the study is the effective use of neural network models in training, involving classification and processing using datasets of plants and weeds. The relevance of the study lies in the demand of the modern world in the use of new information technologies in industrial agriculture, which contributes to improving the efficiency of agro-industrial complexes. The interest of private agricultural enterprises and the state is caused by an increase in the yield of agricultural products. To recognize weeds, machine learning methods, in particular neural networks, were used. The process of weed recognition is described using the Mark model, as a result of processing 1,562 pictures, segmented images are obtained. Due to the annual increase in weeds on the territory of Kazakhstan and in the course of solving these problems, a new plant recognition code was developed and written in the scanner software module. The scanner, in turn, provides automatic detection of weeds. Based on the results of a trained neural network based on the MaskRCNN neural network model written in the scanner software module meeting new time standards, the automated plant scanning and recognition system was improved. The weed was recognized in an average of 0.2 seconds with an accuracy of 89 %, while the additional human factor was completely removed. The use of new technology helps to control weeds and contributes to solving the problem of controlling them