Satellite Image Analysis for Agricultural Field Forecasting Using Machine Learning

Abstract-With the progression of machine learning methods recently, a branch of artificial intelligence was revealed for forecasting and prediction in agriculture field. That is a benefit to works that have to do with agriculture. Recent developments in agricultural practices and methods have highlighted the importance of accurate monitoring, particularly with regards to field monitoring such as paddy areas, in order to take timely control measures for food security and other supportive actions. Moreover, regular monitoring of an area, a landscape, and the entire earth is beneficial by using one of the important sources, that is satellite images, providing information through multi-temporal images. Best source of images complexity because they are indifferent of atmospheric conditions like wind, sun light etc. It combines deep learning specifically convolutional neural networks (CNNs)–and satellite imaging to model crop yield. We suggest a hybrid model that uses data from various sources and real-time integration to provide scalability, accuracy, and reliability to solve practical challenges, such as dataset diversity as well as computational efficiency.