A Time-Series Based Yield Forecasting Model Using Stacked Lstm To Predict The Yield Of Paddy In Cauvery Delta Zone In Tamilnadu

Cauvery delta zone in Tamilnadu is called as “Nerkazhanchiyam” (the land of Paddy) of the state, as it has the potential to produce paddy in huge quantity that can be suffice the need of the state. This zone includes the districts such as Thanjavur, Tiruvarur, Nagapattinam, Trichy and Cuddalore. These districts account for about 53% of production of paddy in the state. Increasing the production of paddy in Cauvery Delta Zone would satisfy the requirement of rice in the state on the whole. This will also have a substantial influence on both the farmer's and the nation's economy. Forecasting the production of crops beforehand could assist the farmers in improving their productivity. This necessitates the design of a precise crop yield prediction model. Crop production in agriculture is primarily determined by a variety of factors that falls under three categories: technological (agricultural techniques, managerial decisions, etc.), biological (diseases, insects, pests, etc.), and environmental (climate change, etc.). Among these factors environmental factors pose a great challenge to the decision makers in developing a precise prediction model. Hence, it is proposed to develop a suitable yield prediction model to predict the yield of paddy in Cauvery delta region considering the environmental factors along with the supplied nutrients. The proposed prediction model makes use of Long Short Term Memory (LSTM) algorithm which is a popular deep learning algorithm, to forecast the yield of paddy. LSTM is well known for its better prediction using time series data. Performance of the proposed prediction model is measured using the training loss and validation loss.