Intelligent Grapevine Disease Detection Using IoT Sensor Network.

Abstract

The Internet of Things (IoT) has gained significance in agriculture, using remote sensing and machine learning to help farmers make high-precision management decisions. This technology can be applied in viticulture, making it possible to monitor disease occurrence and prevent them automatically. The study aims to achieve an intelligent grapevine disease detection method, using an IoT sensor network that collects environmental and plant-related data. The focus of this study is the identification of the main parameters which provide early information regarding the grapevine's health. An overview of the sensor network, architecture, and components is provided in this paper. The IoT sensors system is deployed in the experimental plots located within the plantations of the Research Station for Viticulture and Enology (SDV) in Murfatlar, Romania. Classical methods for disease identification are applied in the field as well, in order to compare them with the sensor data, thus improving the algorithm for grapevine disease identification. The data from the sensors are analyzed using Machine Learning (ML) algorithms and correlated with the results obtained using classical methods in order to identify and predict grapevine diseases. The results of the disease occurrence are presented along with the corresponding environmental parameters. The error of the classification system, which uses a feedforward neural network, is 0.05. This study will be continued with the results obtained from the IoT sensors tested in vineyards located in other regions.

Plant phenotyping relevance

IoTセンサーネットワークと機械学習によるブドウ樹の疾病状態推定が研究の中心であり、古典的な疾病同定との比較・アルゴリズム改善も行っているため、植物フェノタイピング手法として採用する。

Code and data availability

The paper reports IoT sensor-based grapevine disease monitoring with ML analysis, but provides no public dataset, image, code, or model deposit. The only availability statement is a project website (MERIAVINO), which is not an explicit deposit of the paper's sensor/phenotype data or analysis code. TensorFlow is a cited

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