Investigating Optimization Methods and Loss Functions for Training Neural Networks: A Comparative Study

Abstract

In training neural networks for plant disease prediction, this study examines the efficacy of several optimization techniques and loss functions. Various combinations of optimizers and loss functions are investigated to assess their effect on model performance using a comparative research technique. The study makes use of a dataset that includes pictures of both healthy and diseased plants together with labels indicating whether or not the plants are diseased. Plant disease prediction models can be made more accurate by employing the best optimization strategies and loss functions, which are discovered through extensive testing and research. The results further our knowledge of the best practices for neural network training in agricultural applications, especially with regard to disease control and plant health monitoring.