Crop Yield Prediction Using CNN-LSTM Model

Agriculture is one of the most important sectors of the Indian economy. Almost 70 percent of the Indian population is directly or indirectly related to agriculture. The practice of conventional agriculture nowadays has become inefficient due to various factors such as climate change and global warming. Climate change has negatively affected the crop production cycle and hence reduced crop yield. But the advancement in other technologies such as artificial intelligence and machine learning has proved wonders in other sectors and inculcating such techniques in farming is also a great step. To anticipate agricultural yield, statistical models are typically used, which takes time and is laborious. A significant development in the field is the rise of deep learning and machine learning trends. Deep learning models are more suited for making predictions since they naturally possess the ability to do feature extraction in huge datasets. In this paper, a deep learning model CNN-LSTM is employed to make correct predictions of wheat yield. The proposed model outperforms the existing deep learning models in terms of different errors.