A Novel Crop Prediction and Fertilizer Recommendation System Using Python
Lalitha Boddeda1, Kalla Sandhya2, Parisam Saibabu3, K. Manga Pushpa4, Ramesh Kumar Bonda5, Ellapu Yagna Varahala Rao6*
1Dept. of ECE, Avanthi Institute of Engineering and Technology, Tamaram, Makavarapalem, Anakapalle, India
2Dept. of ECE, Visakha Institute of Engineering and Technology, Narava, Visakhapatnam, India
3Dept. of ECE, Avanthi Institute of Engineering and Technology, Tamaram, Makavarapalem, Anakapalle, India
4Dept. of ECE, Visakha Institute of Engineering and Technology, Narava, Visakhapatnam, India
5Dept. of ECE, Avanthi Institute of Engineering and Technology, Tamaram, Makavarapalem, Anakapalle, India
*6Dept. of EEE, Visakha Institute of Engineering and Technology, Narava, Visakhapatnam, India
1lalithaboddeda27@gmail.com
2kallasandhya96@gmail.com
3saibabuparisam@gmail.com
4kmangapushpa@gmail.com
5rameshkumarbonda@gmail.com
*6eyagna3@gmail.com
Abstract— Agriculture is a critical sector that relies heavily on optimal decision-making for crop selection and fertilizer application. Traditional farming methods often lead to inefficiencies due to a lack of precise, data-driven recommendations. This project, "A Novel Crop Prediction and Fertilizer Recommendation System Using Python," aims to assist farmers in selecting the most suitable crop based on soil nutrients and recommending appropriate fertilizers for enhanced yield. The system leverages Flask, a Python web framework, to build an interactive web-based platform where users can input soil parameters such as Nitrogen (N), Phosphorus (P), and Potassium (K), along with the intended crop. Based on this data, the system predicts the best-performing crops and suggests the most appropriate fertilizers using predefined criteria. The system uses data-driven logic to classify crops as top gainers or top losers and recommends fertilizer types based on nutrient levels. The project consists of a user-friendly web interface developed using HTML, CSS, and JavaScript, integrated with Python for backend processing. The Flask-based API processes user inputs and dynamically generates recommendations. The model ensures that farmers receive accurate and efficient suggestions, thus enhancing productivity, reducing soil degradation, and promoting sustainable farming practices. By implementing this system, we aim to empower farmers with data-backed agricultural insights, ultimately improving decision-making and optimizing fertilizer use for better crop yields.
Keywords—Crop Prediction, Data-Driven Agriculture, Fertilizer Recommendation, Flask, Precision Agriculture Soil Nutrients, Python, , Sustainable Farming, Web Application.
