Enhanced Plant Health Monitoring with Dual Head CNN for Leaf Classification and Disease Identification

This study introduces a novel Dual-Head Convolutional Neural Network (DH-CNN) designed to enhance plant health monitoring by concurrently classifying plant leaves and detecting diseases. The monitoring of plant health is essential for ensuring food security and improving agricultural productivity, underscoring the need for accurate and efficient disease detection methods. Utilizing the extensive PlantVillage dataset, the DH-CNN model proposed in this research demonstrates significant improvements compared to existing approaches. The model achieved a classification accuracy of 99.71% for leaf classification and 99.26% for disease identification across 13,576 previously unseen images. Featuring a shared feature extraction layer and two classifier heads, the DH-CNN architecture optimizes performance for both classification tasks. This novel approach aims to support agricultural automation by providing precise and effective plant species and disease detection, thereby contributing to better crop management and disease control strategies.

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Recommended citation: Sajeeb Kumar Ray, Md. Anwar Hossain, Naima Islam, Mirza A.F.M. Rashidul Hasan, Enhanced plant health monitoring with dual head CNN for leaf classification and disease identification, Journal of Agriculture and Food Research, Volume 21, 2025, 101930
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