Farm Decision Support System(FDSS) with various environmental factors using Decision Trees

The implementation of Crop Selection as a means to cultivate the most suitable crop at the optimal time is crucial for achieving high crop yield. Several factors play a significant role in influencing crop yield, such as temperature, humidity, soil potential of hydrogen, rainfall, and more. To address the challenges, the proposed system aims to provide an effective solution. The chosen approach utilizes the machine learning algorithm known as "Decision Trees". A variety of cash crops, like tea and coffee, as well as vegetable crops, like sugarcane and potatoes, are being considered for testing. In order to construct a reliable model that can anticipate genotype response under diverse conditions. By using this advanced technology and utilizing a robust dataset, the Crop Selection system aims to empower farmers, ultimately leading to enhanced crop yields and improved agricultural outcomes. The system uses the values of nitrogen, phosphorus, hydrogen content in soil and also temperature and rainfall. When given the values, the system implementing the algorithm, will select the proper crop.