Deep Learning Based Speculative Analysis of Diverse Nature Inspired Optimization Algorithms in Agriculture

The main process involved in Deep learning is the optimization as it helps to select the best from the different available alternatives such as weights and learning rates. Deep Neural Networks (DNNs) have shown a great potential in a variety of fields. However, as neural networks get deeper and datasets become larger, optimizing these networks becomes increasingly challenging. In a deep learning model, there are some functions that are highly responsible for making accurate predictions. They are objective function, activation function, and loss function. Deep learning model training includes learning the parameters to fulfill the objective function. Typically, the goal is to decrease the loss experienced throughout the learning process. To increase the accuracy of the model, an optimisation process is performed repeatedly until it achieves a good level of convergence. In this research study, diverse optimization algorithms ranging from behavioral patterns of animals or birds such as FFO, PSO, HOA, ALO, WOA, LWBA, FOA and CSOA are reviewed and the evaluation has been done based on the following performance metrics like accuracy, precision, f1 score, and recall. Among all these algorithms, WOA outperformed and produced an accuracy of about 99% with less error rate.