Year: 2024
Type: article
Abstract: Agriculture is vital to the Indian economy, with cash crops playing a significant role in rural income. Farmers face challenges such as pest infestations, crop diseases, and climate unpredictability, resulting in severe financial losses. This study introduces an integrated platform leveraging machine learning (ML), image processing, and web-based technologies to mitigate these issues. Modules include disease detection using Convolutional Neural Networks (CNN), crop yield prediction with Random Forest, and fertilizer recommendations using soil health parameters. These modules are integrated into a Django/flask-based web application, offering a user-friendly interface. Results demonstrate high accuracy and scalability, providing actionable insights to empower farmers. This initiative addresses agricultural challenges through data-driven solutions, promoting sustainable farming practices and improved productivity. Keywords: Agriculture, Machine Learning, Crop Yield Prediction, plant disease Detection, Fertilizer Recommendation, Sustainable Farming
DOI: 10.55041/ijsrem40343
Authors: Pallavi Pattanashetti, Keerthi A H, Manasa C S, Nagarathna M, Marimuthu K
Institution: Presidency University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: bronze
Funder: Presidency University
OpenAlex ID: W4405953908