Data-Driven Framework for Crop Categorization using Random Forest-Based Approach for Precision Farming Optimization

Making incorrect choices when selecting crops can result in substantial financial losses for farmers, primarily because of a limited understanding of the unique needs of each crop. Each farm possesses unique characteristics, influencing the effectiveness of modern agricultural solutions. Challenges persist in optimizing farming methods to maximize yield. This study aims to mitigate these issues by developing a data-driven crop classification and cultivation advisory system, leveraging machine learning algorithms and agricultural data. By analysing variables such as soil nutrient levels, temperature, humidity, pH, and rainfall, the system offers tailored recommendations for crop selection and cultivation practices. This approach optimizes resource utilization, enhances crop productivity, and promotes sustainable agriculture. The study emphasizes the importance of pre-processing data, such as handling missing values and normalizing features, to ensure reliable model training. Various machine learning models, including Random Forests, Bagging Classifier, and AdaBoost Classifier, were employed, demonstrating high accuracy rates in crop classification tasks. The integration of real-time weather data, market prices, and profitability analysis further refines decision-making, while a mobile application facilitates convenient access for farmers. By incorporating user feedback and continuous data collection, the system's performance can be continuously improved, offering precise and economically viable agricultural advice.