Big Data Analytics for Yield Prediction in Precision Agriculture

Abstract

India’s agriculture industry is crucial to its economic growth and one of its most fundamental endeavors. Regarding a country’s economic prosperity, agriculture is among the most significant factors contributing to the happiness and well-being of its citizens. To improve agricultural output, “smart agriculture,” or the use of technology, strives for more accurate disease control, irrigation, and yield prediction. Precision agriculture is applying big data analytics and the Internet of Things to the farming industry. Agricultural production will increase dramatically as a result of this. The Internet of Things (IoT) and massive amounts of data are used in precision agriculture to improve crop quality and yields. In this research, we use the grape plants and their associated factors (temperature, humidity, rainfall, pH, sun irradiance, etc.) from the Smart Agriculture dataset to develop an N-stage CNN. In this work, we use machine learning approaches for irrigation scheduling and the DoubleGAN methodology for disease diagnosis in plants. This effort aims to create an N-stage CNN model that will significantly boost agricultural output by enhancing grape plant yield. The yield prediction is quite accurate since we considered practically all necessary characteristics and photos for plant development, including irrigation schedule and disease detection.