CROP PREDICTION ACCORDING TO WEATHER

Abstract

The project aims to develop a user-friendly web application to predict suitable crops based on weather conditions in Pakistan's agriculture sector. The application will also provide recommendations on alternative crops that can survive adverse climatic conditions in addition to the forecast of primary crop selection. In addition, it will provide farmers with information on the required soil types and optimal levels of fertilizers for each recommended crop, to provide them with tailored guidance. Our study enhances the methods of crop prediction, enables farmers to benefit from valuable decision-making tools, and increases agricultural productivity by using a variety of machine-learning algorithms including Logistic Regression, Random Forest, K-Nearest Neighbours (KNN), Naive Bayes, Decision Tree, and Support Vector Machine (SVM) Following Agile methodology, we iteratively refine software construction, leveraging collaboration and adaptability. We also apply the Agile principles to enhance software functionality through the integration of new data in machine learning. Given factors like temperature, humidity, and rainfall, our methodology involves full data collection, thorough cleaning, and pre-processing. In the end, we are aiming to create a simple interface for widespread access to information and decision-making in Pakistan's agricultural landscape so that it can contribute to the financial stability of the country. Our Project predicts the crop yielding results with approx. 90% accuracy. It also provides alternative ways to the farmers for better results in future.