Intelligent vineyard monitoring using YOLOv7

Abstract

The article discusses the technology for automated neural network monitoring of the vineyard’s physiological condition. The proposed solution is based on the integrated use of convolutional neural network method and machine vision technologies. The training of the YOLOv7 neural network was implemented in the Python environment using the PyTorch framework and the OpenCV computer vision library. The dataset consisting of 6320 images of grape leaves (including healthy and diseased ones) has been used for neural network training. The obtained results showed that the detection accuracy is at least 91%. Visualization of monitoring results has been carried out using heatmap, allowing to obtain information about vineyard physiological condition in dynamics. The proposed mathematical model allows to calculate the monitored vineyard’s area made by one complex per day.