Abstract: The "Cotton Leaf Disease Detection and Automated Spraying System" offers an intelligent, image-based solution for identifying plant diseases and performing precision pesticide spraying with minimal human intervention. By utilizing real-time image acquisition, a CNN-based classification model, and embedded actuation via Raspberry Pi, the system ensures reliable, automated treatment of diseased cotton plants. A Flask-based interface, along with onboard sensors, supports responsive decision-making, while the mobile platform enables deployment across diverse field environments.
Keywords: Cotton Leaf Disease, Convolutional Neural Network (CNN), Image Processing, Raspberry Pi, Automated Spraying, Machine Learning, Precision Agriculture, Flask Web Interface, Pesticide Control, Smart Farming.
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DOI:
10.17148/IJARCCE.2025.14586
[1] DR. SRINIVAS BABU P, ABHISHEK, CHANDRAMOHAN N C, HARISH V R, NAGAN GOUDA HALVI, "COTTON LEAF DISEASE DETECTION USING RASPBERRY PI WITH MACHINE LEARNING AND IMAGE PROCESSING," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2025.14586
