An Efficient Crop Recommendation System Using Optimized Random Forest Approach

Machine learning (ML)-based crop and fertilizer recommendation is a fast expanding field of agricultural research with the potential to completely change how we think about agricultural production. Given the limited amount of arable land and rising food demand, it is imperative to find ways to safeguard the environment, lower costs, and boost agricultural output. This is where using machine learning in agriculture is useful. The Crop and fertilizer recommendation system aims to give farmers useful insights so they can maximize crop productivity. The methodical technique used by the system includes feature selection, preprocessing, evaluation, training, assessment, and recommendation generation in addition to data collecting. The system generates a solid dataset by compiling data on crop history, soil properties, meteorological conditions, and fertilizer consumption. The correctness and dependability of the dataset are guaranteed by data preprocessing methods like cleaning, normalization, and slicing. The Random Forest model’s effectiveness is maximized by feature selection, which is used to find and use pertinent attributes including soil type, weather, and historical data. Different decision trees based on various feature and data set sets are used to train the RF model, a potent tool for both regression and classification. Assessment measures that evaluate the model’s performance and help with hyper parameter optimization include accuracy, precision, recall, and mean squared error. Ultimately, the technology provides farmers with tailored advice that they can access via an intuitive interface, enabling them to make decisions about crops and fertilizer in real time.