Authors : Shashank M P; Suraj G; Uday M; Vikram Sarathy; Dr. Bhagyashree Ambore

Volume/Issue : Volume 9 - 2024, Issue 11 - November

Google Scholar : https://tinyurl.com/bdejzchz

Scribd : https://tinyurl.com/mwk7udpz

DOI : https://doi.org/10.5281/zenodo.14379763

Abstract : This project introduces a machine learning- based solution designed to enhance agricultural productivity by providing data-driven crop recommendations and plant disease identification. Crop recommendations are based on analyzing soil nutrients, environmental conditions, and historical data to identify the most suitable crops for specific regions. For disease identification, convolutional neural networks (CNNs) are utilized to detect and classify plant diseases from images, aiding in early and accurate interventions. These functionalities are accessible through a user-friendly interface, allowing farmers to input key variables and receive actionable insights. Additionally, the system integrates real-time weather data and crop planning tools to help farmers optimize planting schedules. By leveraging both predictive and analytical modules, this smart farming solution addresses common challenges in agriculture such as crop selection, disease management, and environmental adaptability. Initial evaluations show that the system offers significant improvements in decision-making efficiency and crop yield potential, making it a valuable tool for modern, data-informed farming.

Keywords : Crop Recommendation, Disease Identification, Machine Learning, Convolutional Neural Network, Real- Time Weather, Crop Planning, Agricultural Productivity, Data-Driven Decisions.

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