Intelligent Monitoring System to Assess Plant Development State Based on Computer Vision in Viticulture

Abstract

Plant health plays an important role in influencing agricultural yields and poor plant health can lead to significant economic losses. Grapes are an important and widely cultivated plant, especially in the southern regions of Russia. Grapes are subject to a number of diseases that require timely diagnosis and treatment. Incorrect identification of diseases can lead to large crop losses. A neural network deep learning dataset of 4845 grape disease images was created. Eight categories of common grape diseases typical of the Black Sea region were studied: Mildew, Oidium, Anthracnose, Esca, Gray rot, Black rot, White rot, and bacterial cancer of grapes. In addition, a set of healthy plants was included. In this paper, a new selective search algorithm for monitoring the state of plant development based on computer vision in viticulture, based on YOLOv5, was considered. The most difficult part of object detection is object localization. As a result, the fast and accurate detection of grape health status was realized. The test results showed that the accuracy was 97.5%, with a model size of 14.85 MB. An analysis of existing publications and patents found using the search “Computer vision in viticulture” showed that this technology is original and promising. The developed software package implements the best approaches to the control system in viticulture using computer vision technologies. A mobile application was developed for practical use by the farmer. The developed software and hardware complex can be installed in any vehicle. Such a mobile system will allow for real-time monitoring of the state of the vineyards and will display it on a map. The novelty of this study lies in the integration of software and hardware. Decision support system software can be adapted to solve other similar problems. The software product commercialization plan is focused on the automation and robotization of agriculture, and will form the basis for adding the next set of similar software.

Plant phenotyping relevance

ブドウの病害・健全状態を画像から推定する深層学習手法、データセット、ソフトウェア／ハードウェア統合システムが研究の中心であり、植物状態の取得・抽出方法を実質的に開発・評価している。

Code and data availability

The paper describes an author-created dataset of 4845 grape disease images and a YOLOv5-based detection system, but no supplied block contains any public deposit, repository, or availability statement for the dataset, images, code, or trained model. No paper-specific public asset is actionable.

No evidence-backed public reproduction asset is currently recorded.