Efficient prediction of recommended crop variety through soil nutrients using deep learning algorithm - Manjula, E; Djodiltachoumy, S (Journal of Postharvest Technology, 2022)
Agriculture and its related activities account for India’s GDP to the tune of about 17%, in addition to that for 70% of the country’s population, it is still the most popular occupation. Precision Agriculture, especially ’crop recommender systems,’ is a paradigm that includes these strategies. In this research, crop recommended prediction is based on historical data that includes parameters such as phosphorus (P), soil nitrogen (N), temperature, humidity, potassium (K) content, rainfall, pH, and crop name. All of these data variables will be evaluated, and the data will be trained and tested to forecast crop production using the suggested Gated Recurrent Units (GRU) for developing a model. The GRU’s test results are associated to those of the most regularly utilized deep learning approaches such as Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). With a validation accuracy of 0.9709 for 100 epoch, the GRU surpassed all other models. The results demonstrate that using the GRU model to analyze agricultural data enhances the model’s performance by 0.98 in aspects of weighted accuracy. While comparing with LSTM model, CNN model came in second. As a result, the GRU model is exceptionally good at predicting the recommended crop at the conclusion.