Rainfall Prediction for Enhancing Crop-Yield based on Machine Learning Techniques

The agriculture industry is the backbone of the economy in nations like India. Many agricultural crops in India have been impacted by climate change. As a country's population grows, its reliance on agriculture grows, and the country's economic process suffers as a result. In this situation, crop yields have a significant impact on the country's economic progress. There were insufficient food grains to feed the populace. Despite the existence of numerous methodologies or procedures for estimating agricultural production, their accuracy is not up to par. According to the literature review, there are no acceptable remedies or technologies to address the aforementioned condition. This method represented a push toward over-mechanization of agriculture. Agriculturists were encouraged and supported to engage in technology-based farming. This initiative attempts to assist farmers in forecasting future harvests and properly managing their costs by taking into account variables such as temperature, rainfall, and land acreage. To forecast the crop output of a certain agriculture region depending on the quantity of rainfall, the proposed method employs machine learning techniques and multilayer Perceptron. Farmers will be able to anticipate crop yields prior to planting and make the appropriate investment decisions as a result of this research. To estimate agricultural yields early in the harvest, this approach also focuses on appropriate marketing and storage stages. The findings of the suggested system are made available to the farmer group. The suggested technique is beneficial for precisely anticipating agricultural yield output.