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A Systematic Analysis of Prediction for Cotton Crop Disease Using Machine Learning Techniques

Year: 2025

Type: article

Abstract: Agriculture is one of the basic entities in Indian economy and to maintain the progressive production of food it is considered to be the most important aspects. This project uses machine learning to create a smart system for detecting and predicting cotton crop diseases. By employing image recognition and analysing datasets of disease symptoms, the system will enable farmers to identify diseases early, leading to healthier crops and increased yields. The system integrates data preprocessing, model training, and real-time disease detection using deep learning. This technology will improve farmers' decision-making, minimize crop losses, and support sustainable agriculture. Accuracy, scalability, and user-friendliness for cotton farmers are key focuses of this project.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15063928

Authors: Patil, Sagar, Sonone, Chetan, Patil, Chetan, Ingle, Abhishek, Karande, Dr. M. U.

Institution: Padmashri Dr. V. B. Kolte College of Engineering, Malkapur

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Zero hunger, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W6912070773

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