Plant Health Monitoring Using Convolutional Neural Networks with Automated Leaf Classification and Counting

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.

Plant phenotyping relevance

植物画像から病害状態と葉数を推定するCNNベースの取得・解析パイプラインが研究の中心であり、植物表現型の計測手法として明示的に開発・評価されている。

Code and data availability

The paper describes a CNN for leaf health classification but provides no public dataset, image collection, code repository, model checkpoint, or supplement with availability language. The dataset is only vaguely described ('a curated set of annotated plant images') with no source, deposit, or URL; tools mentioned (Kera

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