Crop Yield Prediction Using Optimized Convolutional Neural Network Model Based on Environmental and Phenological Data

Maximizing total profit by determining the most suitable crop for each piece of land in a complex agricultural region poses a significant challenge in cultivation management. However, numerous factors such as cost, yield, and selling price often lack clarity, rendering precise programming approaches impractical. The objective of this study is to identify an optimal crop prediction model that can assist farmers in selecting the appropriate crop based on local climatic conditions and soil nutrient levels. This research presents a novel hybrid model called the Modified Lion Optimizer (MLO)-based Convolutional Neural Network (CNN). By incorporating MLO, an efficient algorithm inspired by nature, this paper addresses the aforementioned challenge. The MLO algorithm assesses each solution using three metrics: expected, optimistic, and pessimistic values. This combination empowers the algorithm to discover reliable solutions even in the presence of uncertainties. Experimental results demonstrate that the proposed technique surpasses traditional deep learning models in terms of performance.