AI-IoT based smart agriculture pivot for plant diseases detection and treatment

Abstract

Abstract There are some key problems faced in modern agriculture that IoT-based smart farming. These problems such shortage of water, plant diseases, and pest attacks. Thus, artificial intelligence (AI) technology cooperates with the Internet of Things (IoT) toward developing the agriculture use cases and transforming the agriculture industry into robustness and ecologically conscious. Various IoT smart agriculture techniques are escalated in this field to solve these challenges such as drop irrigation, plant diseases detection, and pest detection. Several agriculture devices were installed to perform these techniques on the agriculture field such as drones and robotics but in expense of their limitations. This paper proposes an AI-IoT smart agriculture pivot as a good candidate for the plant diseases detection and treatment without the limitations of both drones and robotics. Thus, it presents a new IoT system architecture and a hardware pilot based on the existing central pivot to develop deep learning (DL) models for plant diseases detection across multiple crops and controlling their actuators for the plant diseases treatment. For the plant diseases detection, the paper augments a dataset of 25,940 images to classify 11-classes of plant leaves using a pre-trained ResNet50 model, which scores the testing accuracy of 99.8%, compared to other traditional works. Experimentally, the F1-score, Recall, and Precision, for ResNet50 model were 99.91%, 99.92%, and 100%, respectively.

Plant phenotyping relevance

植物葉画像から病害状態を推定する深層学習モデルと、検出・処置用のIoTシステム／ハードウェアを中心的に開発・評価しているため、植物フェノタイピング手法として含める。

Code and data availability

The paper's plant-phenotyping measurements (ResNet50 training/validation/testing on 25,940 leaf images) are based on the public New Plant Diseases Dataset from Kaggle, explicitly stated by the authors. No author analysis code, trained model checkpoints, or supplementary data deposit is mentioned.