A Deep Learning hybrid framework CNN-LSTM Agricultural Yield Prediction

Abstract

Modern AI based models’ data analyzation capability provides better approach towards the soil, weather, and crop data to determine the right time to sow, irrigate, and harvest. With help of better and reduced resources wastage and increases the productivity. AI models can easily predict the agriculture practices and activities like pest infestations, disease outbreaks, and weather patterns, enabling farmers to take preventative actions. This study is bases on hybrid CNN-LSTM for the weather and soil data to predict the yield. The IoT technology is integrated for collection of datasets. The model CNN-LSTM obtained 0.93 accuracy, with 0.1822 of test loss. The results of training show the overfitting is not performed and provided better results by using 100 epochs.

Keywords: Deep Learning, IoT, Smart agriculture farming, Data evaluation