Authors : G. Akhil Kumar

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/mt69wdtb

Scribd : https://tinyurl.com/57a8a7ka

DOI : https://doi.org/10.38124/ijisrt/25nov1449

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Abstract : This Research paper develops an automated Plant Health Monitoring system that leverages Convolutional Neural Networks (CNNs) to perform simultaneous leaf- level disease classification and leaf counting from plant images. The proposed pipeline uses a CNN-based feature extractor feeding two task-specific branches: a classification head that identifies healthy versus diseased leaves (and the disease type) and a counting head that estimates leaf number via a regression/segmentation approach. Input images are preprocessed with augmentation and normalization to improve robustness to lighting, occlusion, and background variation. The model is trained on a curated set of annotated plant images and adapted for efficient inference using transfer learning and lightweight architectures suitable for edge deployment. Results show the approach provides reliable disease detection and accurate leaf counts, enabling timely alerts and actionable insights for precision agriculture. The system aims to reduce manual inspection effort, speed up diagnosis, and support better crop-management decisions.

Keywords : Leaf Segmentation, Deep Learning, Convolutional Neural Networks, Precision Agriculture, Smart Farming.

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