STM Journals Peer-reviewed research from Consortium e-Learning Network

Research and Reviews : Journal of Crop science and Technology Original Research

Deep Learning models for real time detection of crop diseases in the Maharashtra/Mumbai district

  1. Vaishnavi Mandlik MCA Thakur Institute of Management Studies, Career Development & Research (TIMSCDR)
  2. Manan Mehta MCA Thakur Institute of Management Studies, Career Development & Research (TIMSCDR)
  3. Ms. Rupali Jadhav MCA Thakur Institute of Management Studies, Career Development & Research (TIMSCDR)

Abstract

This research project addresses the critical agricultural challenge of crop disease management in the Maharashtra region of India by leveraging modern deep learning techniques. The primary objective is to identify, implement, and compare the efficacy of various deep learning architectures—including Convolutional Neural Networks (CNNs), MobileNet, and EfficientNet—for the real-time classification of diseases in key crops such as cotton, soybean, and sugarcane. A custom dataset of agricultural images specific to Maharashtra's climatic conditions was curated and preprocessed. The models were trained and tested on various parameters such as accuracy, precision, recall, F1-score, and computational complexity to determine their appropriateness and applicability on mobile devices and Edge devices. The results suggest that, although various models of EfficientNet yield the highest accuracy in crop classification, MobileNet is the most balanced and practical one, and thus a more viable option to be applied in a field-level context in a real-time manner. The report serves as a valuable guide in developing a crop disease monitoring system using AI technology, capable of markedly decreasing crop destruction and increasing farmer productivity in the region.

Keywords

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