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@article{198901,
        author = {Dr. S. Sharon Priya and Annirrudhan S and Abdullah S and Yogesh Kumar B},
        title = {Weather Based Smart Crop Recommendation and Diseases Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {13233-13236},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198901},
        abstract = {Agriculture plays a vital role in food production and economic development. However, selecting suitable crops under varying soil and climatic conditions remains a significant challenge for farmers. This paper presents a Weather-Based Smart Crop Recommendation and Disease Prediction System that utilizes machine learning techniques to support efficient agricultural decision-making. The proposed system analyzes essential soil parameters such as Nitrogen (N), Phosphorus (P), Potassium (K), pH level, along with environmental factors including temperature, humidity, and rainfall to recommend optimal crops. A Random Forest algorithm is employed to ensure accurate and reliable predictions. In addition, the system incorporates a plant disease detection module that enables farmers to upload leaf images for identifying potential diseases using image analysis techniques. Furthermore, the system provides fertilizer recommendations based on soil nutrient levels and integrates market trend analysis to assist farmers in selecting economically profitable crops. The application is developed as a web-based platform using Streamlit, ensuring ease of use and accessibility. The proposed system enhances crop productivity, reduces potential losses, and promotes data-driven decision-making in modern agriculture.},
        keywords = {Agriculture, Crop Recommendation, Disease Prediction, Machine Learning, Random Forest, Soil Nutrients, Streamlit.},
        month = {April},
        }