Long short-term memory neural network-based multi-level model for smart irrigation
Abstract
Rice is a staple food crop around the world, and its demand is likely to rise significantly with growth in population. Increasing rice productivity and production largely depends on the availability of irrigation water. Thus, the efficient application of irrigation water such that the crop doesn’t experience moisture stress is of utmost importance. In the present study, a long short-term memory (LSTM)-based neural network with logistic regression has been used to predict the daily irrigation schedule of drip-irrigated rice. The correlation threshold of 0.75 was used for the selection of features, which helped in limiting the number of input parameters. Also, a dataset based on the recommendation of a domain expert, and another used by the tool Agricultural Production Systems Simulator (APSIM) was used for comparison. Field data comprising of weather station data and past irrigation schedules has been used to train the model. Grid search algorithm has been used to optimize the hyperparameters of the model. Nested cross-validation has been used for validating the results. The results show that the correlation-based selected dataset is as effective as the domain expert-recommended dataset in predicting the water requirement using LSTM as the base model. The models were evaluated on different parameters and a multi-criteria decision evaluation (Technique for Order of Preference by Similarity to Ideal Solution [TOPSIS]) was used to find the best performing.
