Year: 2025
Type: article
Abstract: Plant diseases can cause severe damage to crops and lead to food shortages and financial losses for both farmers and the agricultural sector. Detecting these diseases early is essential for protecting crops, increasing agricultural productivity, and ensuring food security. This paper introduces a new intelligent edge computing framework that provides a cost-effective, portable, and energy-efficient solution for deep learning-based automated plant disease detection. Unlike cloud-dependent systems, the proposed framework operates independently of an internet connection, making it ideal for real-time field deployment. It employs the NVIDIA Jetson Nano as an edge computing device and incorporates an Android-based interface for user interaction. The system utilizes a convolutional neural network (CNN) for feature extraction, followed by a deep classification network to identify plant diseases. Plant images are captured by a smartphone and transmitted to the Jetson Nano over a local WiFi network using the KDE Connect application for processing. After classification, the image with the predicted disease category is sent back to the smartphone using FTP for user display. The framework was trained and evaluated on the PlantVillage dataset involving 38 disease categories for 14 different types of healthy and diseased crop leaves, achieving a maximum accuracy of 99.1%. This efficient and practical system demonstrates the potential of edge AI in precision agriculture by enabling on-device disease diagnosis without relying on cloud computing infrastructure.
Authors: Adel Jalal Yousif, Fadhil Kadhem Zaidan, Nebras Jalel Ibrahim
Institution: University of Diyala
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $75
OpenAlex ID: W4413974512