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Optimized sequential model for superior classification of plant disease.

Yogesh Chimate¹, Sangram Patil², K Prathapan²

¹Department of Computer Science and Engineering, D. Y. Patil Agriculture and Technical University, Talsande, Maharashtra, India. yogeshchimate@dyp-atu.org.

Scientific Reports

|January 29, 2025

Summary

Deep learning, specifically Convolutional Neural Networks (CNN), offers a powerful solution for accurate plant disease detection in Indian agriculture. This advanced method significantly improves early diagnosis, safeguarding crop yields and farmer livelihoods.

Keywords:

Disease classification Feature extraction Machine learning Plant disease

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Area of Science:

Agricultural Science
Computer Science
Artificial Intelligence

Background:

Indian agriculture is crucial for the economy, but plant diseases pose a significant threat to crop yields and farmer income.
Traditional disease diagnosis relies on human expertise, which is often inaccurate, inefficient, and unsuitable for large-scale farming.
Early disease symptoms are often invisible, making timely and precise detection challenging with conventional methods.

Purpose of the Study:

To explore the application of deep learning, specifically Convolutional Neural Networks (CNN), for enhanced plant disease detection.
To improve the accuracy and efficiency of diagnosing plant diseases in crops like mango and groundnut.
To provide a scalable and reliable solution for agricultural disease management.

Main Methods:

Utilized Convolutional Neural Networks (CNN), a deep learning architecture capable of automatic feature extraction from large datasets.
Collected and processed image data from mango and groundnut leaves from field visits in western Maharashtra and online sources.
Employed image processing techniques including normalization, resizing, and data augmentation to optimize the dataset for classification.

Main Results:

The developed CNN model achieved a high accuracy rate of 96% in plant disease detection.
Demonstrated superior performance compared to traditional machine learning techniques that require laborious manual feature extraction.
Image processing techniques significantly improved classification results and dataset quality.

Conclusions:

Convolutional Neural Networks offer a highly accurate and efficient method for plant disease detection in agriculture.
CNN models can continuously adapt and improve performance through iterative training, leading to reduced errors.
This deep learning approach presents a viable alternative to conventional methods, enhancing crop protection and supporting farmers.