GreenDiagnosis: Intelligent Crop Disease Detection Using Deep Learning Algorithm

Year : 2025 | Volume : 14 | Issue : 02 | Page : 8 18
    By

    Rohit Harn,

  • Sonali Bhosale,

  • Prajyot Wasekar,

  • Atul Mohkare,

  1. Professor, SKNCOE (Smt. Kashibai Navale College of Engineering, SPPU, Pune, ,
  2. Professor, SKNCOE (Smt. Kashibai Navale College of Engineering, SPPU, Pune, ,
  3. Professor, SKNCOE (Smt. Kashibai Navale College of Engineering, SPPU, Pune, ,
  4. Professor, SKNCOE (Smt. Kashibai Navale College of Engineering, SPPU, Pune, ,

Abstract

Agriculture in parts of India relies on labour-intensive traditions, maintaining disease-free crops is crucial. Manual methods can be inaccurate, driving farmers towards AI-based solutions. AI offers a proactive approach to address real-time farming challenges. Among these is the invasion of pests, which diminishes crop quality. Combating pest-related diseases poses a challenge, prompting innovation. Effective surveillance and early detection of crop diseases play a pivotal role in ensuring global food security and agricultural sustainability. In recent years, advancements in artificial intelligence, particularly deep learning algorithms, have demonstrated remarkable potential in revolutionizing various domains, including agriculture. This project introduces “Deep Farming,” an innovative approach that harnesses the power of deep learning algorithms to enhance intelligent crop disease surveillance. The proposed Deep Farming system integrates state-of-the-art deep learning techniques with crop disease monitoring to create a robust and automated solution for disease detection and classification. Through rigorous testing and evaluation, the PCA DeepNet framework has demonstrated remarkable accuracy and precision in detecting and classifying crop leaf diseases. With accuracy rates consistently exceeding 90% and precision scores surpassing 95%, the system exhibits exceptional reliability and efficacy in identifying disease symptoms with minimal false positives. Additionally, its robust performance is further highlighted by superior recall and F1-score metrics, indicating comprehensive disease detection capabilities and overall system effectiveness. This level of accuracy and precision translates into tangible benefits for farmers, enabling early intervention and targeted management strategies to mitigate crop losses and safeguard agricultural productivity. The system learns intricate patterns and features from high-resolution images of crops, thereby enabling accurate and efficient identification of various disease symptoms. The system’s architecture is designed to process diverse datasets, encompassing a wide array of crop types and disease manifestations. The PCA DeepNet framework demonstrated superior accuracy, precision, recall, and F1-score for crop leaf disease detection, with efficient training and inference times, establishing it as a robust solution for agricultural disease management.

Keywords: Tomato leaf diseases, artificial intelligence, deep learning, computer vision, Principle Analysis Component, faster convolutional neural network.

[This article belongs to Research & Reviews : Journal of Agricultural Science and Technology ]

How to cite this article:
Rohit Harn, Sonali Bhosale, Prajyot Wasekar, Atul Mohkare. GreenDiagnosis: Intelligent Crop Disease Detection Using Deep Learning Algorithm. Research & Reviews : Journal of Agricultural Science and Technology. 2025; 14(02):8-18.
How to cite this URL:
Rohit Harn, Sonali Bhosale, Prajyot Wasekar, Atul Mohkare. GreenDiagnosis: Intelligent Crop Disease Detection Using Deep Learning Algorithm. Research & Reviews : Journal of Agricultural Science and Technology. 2025; 14(02):8-18. Available from: https://journals.stmjournals.com/rrjoast/article=2025/view=211942


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Regular Issue Subscription Original Research
Volume 14
Issue 02
Received 30/09/2024
Accepted 24/10/2024
Published 12/04/2025
Publication Time 194 Days
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