Design and Implementation of an IOT-Assisted Image Processing System for Early Plant Disease Identification

Abstract

Agriculture plays a vital role in economic development, and plant diseases significantly affect crop productivity. Early detection of plant leaf diseases is essential to reduce crop loss and improve yield. This paper proposes an Internet of Things (IoT)-based plant leaf disease detection system using image processing and deep learning techniques. The system captures realtime images of plant leaves using a camera module and processes them using convolutional neural networks (CNN) for classification. The processed results are transmitted to a cloud platform for remote monitoring. The proposed model improves accuracy and enables early detection compared to traditional manual methods. Experimental results demonstrate that deep learning-based approaches achieve high accuracy in identifying plant diseases, making the system efficient for smart agriculture applications.

Code and data availability

The paper describes an IoT-assisted CNN plant disease detection system but provides no public dataset, image collection, code, model checkpoint, or supplement with availability statements or URLs. Datasets are mentioned only generically ('images can also be obtained from datasets') with no named deposit or access path.

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