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

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 15 | 02 | Page :
    By

    Vaishnavi Mandlik,

  • Manan Mehta,

  • Ms. Rupali Jadhav,

  1. Research Scholar, MCA Thakur Institute of Management Studies, Career Development & Research (TIMSCDR), Maharashtra, India
  2. Research Scholar, MCA Thakur Institute of Management Studies, Career Development & Research (TIMSCDR), Maharastra, India
  3. Assistant Professor, MCA Thakur Institute of Management Studies, Career Development & Research (TIMSCDR), Maharastra, India

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: CNN, MobileNet, EfficientNet, Deep Learning, Plant Disease

How to cite this article:
Vaishnavi Mandlik, Manan Mehta, Ms. Rupali Jadhav. Deep Learning models for real time detection of crop diseases in the Maharashtra/Mumbai district. Research and Reviews : Journal of Crop science and Technology. 2026; 15(02):-.
How to cite this URL:
Vaishnavi Mandlik, Manan Mehta, Ms. Rupali Jadhav. Deep Learning models for real time detection of crop diseases in the Maharashtra/Mumbai district. Research and Reviews : Journal of Crop science and Technology. 2026; 15(02):-. Available from: https://journals.stmjournals.com/rrjocst/article=2026/view=246483


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Ahead of Print Subscription Original Research
Volume 15
02
Received 13/04/2026
Accepted 08/05/2026
Published 30/06/2026
Publication Time 60 Days
16

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