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

IF 1.9 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Computation Pub Date : 2023-09-03 DOI:10.3390/computation11090171

Marina Rudenko, A. Kazak, N. Oleinikov, Angela N. Mayorova, Anna Dorofeeva, Dmitry Nekhaychuk, Olga Shutova

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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.

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基于计算机视觉的葡萄栽培植物发育状态智能监测系统

植物健康在影响农业产量方面发挥着重要作用，植物健康状况不佳可能导致重大经济损失。葡萄是一种重要的广泛种植的植物，特别是在俄罗斯南部地区。葡萄易患多种疾病，需要及时诊断和治疗。对病害的错误识别可能导致巨大的作物损失。建立了4845张葡萄病害图像的神经网络深度学习数据集。研究了黑海地区常见的葡萄病害8大类:霉病、黄斑病、炭疽病、Esca、灰腐病、黑腐病、白腐病和葡萄细菌性癌病。此外，还包括一组健康植物。本文提出了一种基于YOLOv5的基于计算机视觉的葡萄栽培植物生长状态监测的选择性搜索算法。目标检测中最困难的部分是目标定位。实现了葡萄健康状况的快速、准确检测。测试结果表明，准确率为97.5%，模型大小为14.85 MB。通过搜索“葡萄栽培中的计算机视觉”对现有出版物和专利进行分析，表明该技术具有独创性和前景。开发的软件包实现了使用计算机视觉技术的葡萄栽培控制系统的最佳途径。为农民实际使用开发了一个移动应用程序。开发的软件和硬件综合体可以安装在任何车辆上。这样一个移动系统将允许对葡萄园的状态进行实时监控，并将其显示在地图上。本研究的新颖之处在于软硬件的集成。决策支持系统软件可以用于解决其他类似问题。软件产品商业化计划的重点是农业的自动化和机器人化，并将成为增加下一套类似软件的基础。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computation Mathematics-Applied Mathematics

CiteScore

3.50

自引率

4.50%

发文量

201

审稿时长

8 weeks

期刊介绍： Computation a journal of computational science and engineering. Topics: computational biology, including, but not limited to: bioinformatics mathematical modeling, simulation and prediction of nucleic acid (DNA/RNA) and protein sequences, structure and functions mathematical modeling of pathways and genetic interactions neuroscience computation including neural modeling, brain theory and neural networks computational chemistry, including, but not limited to: new theories and methodology including their applications in molecular dynamics computation of electronic structure density functional theory designing and characterization of materials with computation method computation in engineering, including, but not limited to: new theories, methodology and the application of computational fluid dynamics (CFD) optimisation techniques and/or application of optimisation to multidisciplinary systems system identification and reduced order modelling of engineering systems parallel algorithms and high performance computing in engineering.