Development of a method and software tool for optimizing seasonal harvest management operations

Abstract

This research aims to develop a method and a corresponding software tool to optimize seasonal harvesting operations. The goal is to address inefficiencies in resource allocation and timing that negatively impact the profitability of agricultural businesses. The proposed solution integrates optimization algorithms to manage and schedule harvest operations, considering external variables such as weather conditions, labor availability, and market demands. Our approach combines machine learning techniques and data-driven decision-making models to predict optimal harvest times and maximize resource utilization. The main results demonstrate improved cost efficiency, increased yield, and minimized operational delays compared to traditional methods. This study`s novelty lies in integrating advanced optimization algorithms within a tailored software tool, which provides real-time adjustments based on environmental and market factors. The findings highlight the potential of innovative farming technologies to revolutionize harvest management operations, contributing to a more sustainable and profitable agricultural practice. This paper also presents the development of a method and software tool to optimize the management of seasonal harvesting operations. The study addresses the inherent challenges in agricultural scheduling, particularly for crops with narrow harvesting windows, to maximize yield and profitability. The results demonstrate significant efficiency and cost reduction improvements compared to traditional methods.