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Agriculture planning plays a significant role in economic growth and the food security of agro-based country. Crop yield prediction and selection of crops are the most challenging tasks in agricultural domain and it depends on different parameters such as production rate, market price and government policies. Among the two primary tasks, the crop yield prediction is one of the most demanding tasks for every nation. Due to uncertain climatic changes, farmers are struggling to attain a satisfactory amount of yield from the crops. Many researchers have studied on the prediction of weather, prediction of yield rate of crop, crop classification and soil classification for agriculture planning using statistical methods or machine learning techniques. This study focuses on the prediction of major crops in Andhra Pradesh region and presents an enhanced algorithm known as Deep Convolutional Regression Network (DCRN), which is trained and tested on agricultural data collected from farmers. The experimental results showed that the DCRN method achieved nearly 97% prediction accuracy when compared with existing methods like Decision Tree (DT), Self-Organizing Map (SOM).

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