GREENHOUSE YIELD PREDICTION USING FEATURE SELECTION AND ENHANCED ARTIFICIAL NEURAL NETWORK ALGORITHM

Abstract

Greenhouse yield prediction is a crucial aspect of modern agriculture, aiming to optimize production and resource management in controlled environments. Accurate yield prediction in greenhouse agriculture is paramount for optimizing resource allocation and maximizing productivity, yet the complex interplay of numerous environmental and operational factors poses a significant challenge. In this work, Improved Chicken Swarm Optimization (ICSO) and Enhanced Artificial Neural Network (EANN) algorithm is proposed. The main steps of this research includes pre-processing, feature selections, and classifications for greenhouse yield prediction. Initially min-max normalization algorithm is proposed to improve the quality of the given dataset. Then, ICSO algorithm is introduced for feature selection which selects more relevant and significant features from the given dataset. These ICSO variants, by leveraging their enhanced search capabilities and adaptability, provide robust and effective solutions for optimizing greenhouse management, resource allocation, and yield prediction. It generates best fitness values based on the higher accurate features. Finally, EANN is applied to perform the yield prediction in the given greenhouse dataset. EANN is focused to effectively manage the complexities of greenhouse environments and optimize crop yields via hidden neurons. Also, it is used for optimizing environmental controls, detecting diseases, and managing resources efficiently, ultimately leading to increased productivity and sustainable greenhouse operations. The training and testing process is conducted and it is used to provide more accurate results. The experimental results prove that the proposed APSO-EANN algorithm provides better greenhouse yield results in terms of higher accuracy, precision and RMSE values than the existing algorithms