In plain words: A review of ten years of studies using neural networks to spot and track weeds in farm fields, comparing how well different designs tell crops from weeds. It ends with shared reporting rules so future results are easier to judge and compare.
Abstract · Deep learning, machine vision in agriculture in 2021
Over the past decade, unprecedented progress in the development of neural networks influenced dozens of different industries, including weed recognition in the agro-industrial sector. The use of neural networks in agro-industrial activity in the task of recognizing cultivated crops is a new direction. The absence of any standards significantly complicates the understanding of the real situation of the use of the neural network in the agricultural sector. The manuscript presents the complete analysis of researches over the past 10 years on the use of neural networks for the classification and tracking of weeds due to neural networks. In particular, the analysis of the results of using various neural network algorithms for the task of classification and tracking was presented. As a result, we presented the recommendation for the use of neural networks in the tasks of recognizing a cultivated object and weeds. Using this standard can significantly improve the quality of research on this topic and simplify the analysis and understanding of any paper.
Ildar Rakhmatulin
arXiv:2103.04893 · cs.CV, cs.LG, eess.IV · submitted Mar 3, 2021
abstract · pdf
