Soil nutrient prediction for paddy cultivation via soil fertility and pH trained hybrid architecture: Recommendations based on nutrient deficiency

Abstract

Soil testing can assist in determining how much fertilizer is necessary, as it depends on the fertility and crop of the soil. Through soil fertility and pH-trained hybrid architecture, a new soil nutrient prediction model for paddy agriculture is proposed in this work. First, data acquisition takes place, which is the act of gathering soil data, and it is subsequently preprocessed using the Improved Normalization method. A soil information dataset is employed in this work to help with this. Subsequently, the preprocessed data undergoes data augmentation; the correlation method facilitates an enhanced data augmentation procedure. In this case, the data used for the correlation approach is min-max normalization data. The augmented data is used to extract soil properties such as pH level and soil fertility index. Additionally, a hybrid classifier strategy that combines RNN and Modified LSTM is suggested for nutrient prediction. Lastly, this article suggested some fertilizers for nutritional insufficiency based on the projection. The hybrid prediction classifiers that have been suggested perform better in experiments than the classic classifier models, which include LSTM, RNN, SVM, Bi-GRU, and DNN, in terms of sensitivity, accuracy, FPR, MCC, precision, and efficiency in predicting nutrients. Even though the CNN (0.075), Bi-GRU (0.080), LSTM (0.087), DBN (0.078), Enhanced-1DCNN DLM (0.080), RNN (0.085), and RFA (0.052) obtained maximal FPR ratings, the FPR of the Modified LSTM $+$ RNN scheme is 0.052.