Serial Cascaded Deep Feature Extraction-based Adaptive Attention Dilated model for Crop Recommendation Framework

Effective crop farming depends on wise selection of crops. It is an essential factor that has to be fulfilled before beginning an agricultural endeavor. Conventionally, the crop that has to be grown is selected without considering the location and cultivated site's characteristics by only considering its profit and demand on the market. Choosing the best crop for the circumstances can minimize the need for additional fertilizer and water for irrigation and help in attaining enhanced crop yield. Therefore, choosing the right crop is crucial for a successful agricultural situation. Thus, a novel crop recommendation model by considering the soil and geographical conditions is developed to aid the farmers in choosing the appropriate crop for the right condition so that the overall production can be enhanced to increase the overall profit and decrease the losses faced by the farmers. At first, a certain geographical area is selected, and the ideal parameters for growing a particular plant are gathered from the standard database. Next, the deep optimal features are extracted using a Serial Cascaded network in which an autoencoder is cascaded with a "Dimensional Convolutional Neural Network (1DCNN)" from the gathered data. The obtained deep features are optimally selected using the developed Modified Movement Territory of Fire Hawk Optimizer (MMTFHO). These optimally selected features are given to the Adaptive and Attention-based Hybrid Network (AAHNet) in which "Gated Recurrent Unit (GRU), and Long Short Term Memory (LSTM)" are utilized for choosing the right crop for the provided geographical condition. The parameters in the AAHNet are optimized using the same enhanced MMTFHO algorithm for improving the precision of the appropriate crop selection process. The final prediction of crops for the given geographical condition is obtained from the AAHNet. The final or overall rating of the recommended approach regarding accuracy metrics is 96.73%.