Naveen et al. (2026) Automated Leaf Damage Assessment and Crop Classification Using Convolutional Neural Networks

Identification

• Journal: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
• Year: 2026
• Date: 2026-04-05
• Authors: J. Naveen, Mr. P. Venkateswarlu, K. Venkata Aadarsh, M. Geetha Reddy, G. Paul Thamas, T. Sai Pallavi
• DOI: 10.55041/ijsrem59110

Research Groups

1. Department of ECE, PBR VITS, Kavali, Nellore District, Andhra Pradesh, India

Short Summary

This paper presents an automated system utilizing Convolutional Neural Networks (CNN) for leaf damage assessment and crop classification to enhance agricultural productivity. The system integrates image capture, CNN-based disease detection and crop classification, real-time soil moisture sensing for irrigation, pest control activation, and GSM alerts to farmers, aiming to reduce manual effort and improve accuracy in smart farming practices.

Objective

• To design and implement an automated system for intelligent monitoring and analysis of crop health, specifically for leaf damage assessment and crop classification, using Convolutional Neural Networks to enhance agricultural productivity.

Study Configuration

• Spatial Scale: Individual plant leaves to agricultural fields (implied for deployment).
• Temporal Scale: Real-time monitoring and control (e.g., irrigation, pest control activation).

Methodology and Data

• Models used: Convolutional Neural Networks (CNN).
• Data sources: Images of plant leaves captured by a camera, soil moisture sensor data.

Main Results

• The developed system successfully processes plant leaf images using a CNN model to detect diseases and classify crops accurately.
• It integrates real-time soil moisture sensing for automated irrigation control via a water pump.
• The system activates pest control mechanisms upon disease detection.
• It provides alerts to farmers through GSM communication, reducing manual effort and improving disease detection accuracy.

Contributions

• Introduction of an integrated smart agriculture system combining CNN-based image analysis for crop health with IoT components (soil moisture sensing, water pump, pest control activation, GSM alerts).
• Offers a comprehensive solution for automated leaf damage assessment, crop classification, and environmental control, supporting smart farming practices.
• Aims to significantly reduce manual labor and enhance the precision of agricultural monitoring and intervention.

Funding

• Not specified in the provided text.

Citation

@article{Naveen2026Automated,
  author = {Naveen, J. and Venkateswarlu, Mr. P. and Aadarsh, K. Venkata and Reddy, M. Geetha and Thamas, G. Paul and Pallavi, T. Sai},
  title = {Automated Leaf Damage Assessment and Crop Classification Using Convolutional Neural Networks},
  journal = {INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT},
  year = {2026},
  doi = {10.55041/ijsrem59110},
  url = {https://doi.org/10.55041/ijsrem59110}
}