Abstract

Cotton is one of the most important crops in India and plays a vital role in the agricultural and textile economy. However, cotton productivity is frequently affected by plant diseases such as leaf spot, wilt, boll rot, and bacterial blight, which lead to significant yield loss and increased dependence on chemical pesticides. In addition to disease detection, understanding the growth stage of the crop is equally important for effective crop management and decision-making. Early identification of both disease conditions and plant development stages can support timely agricultural interventions. This paper presents CottonLeafNet-Pilot, a lightweight hybrid CNN–Transformer framework designed for integrated cotton disease detection and growth stage monitoring. The proposed architecture combines convolutional neural networks for local feature extraction with attention-based transformer modules for capturing global contextual relationships. The model also incorporates attention mechanisms to focus on disease-affected regions while suppressing irrelevant background information. The framework is trained and evaluated on a curated dataset of 6,000 cotton leaf images collected under diverse field conditions. Experimental results demonstrate an overall classification accuracy of 96.2% with a macro F1-score of 95.9%, while maintaining a lightweight architecture suitable for deployment on mobile and edge devices. The study demonstrates the feasibility of developing indigenous AI solutions for precision agriculture, enabling integrated monitoring of cotton crop health and development stages. The proposed approach supports sustainable farming practices and contributes to the vision of ATMANIRBHAR BHARAT in agricultural technology.

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