Advanced Neural Networks for Anticipating Plant Development and Harvest Quantity in Controlled Green House Conditions

Abstract

In agricultural practices, predicting harvest quantities is crucial for effective planning and resource allocation. The problem is the accurate prediction of harvest quantities. Predicting harvests is vital for farmers, agricultural businesses, and policymakers to make informed decisions about crop management, distribution, and market planning. Thus, the need for accurate harvest predictions is paramount in agriculture. Farmers need to plan their resources efficiently, distributors require forecasts for logistical arrangements, and policymakers rely on these predictions for planning food security measures. The traditional approach to harvest quantity prediction involved relying on the experience and knowledge of agricultural experts, historical data analysis, and basic statistical methods. These methods, while foundational, had limitations in handling complex, multi-dimensional data. Additionally, they often couldn't process the immense volumes of data available in the modern era. Hence, there was a need for more sophisticated techniques that could analyze diverse and large datasets to improve prediction accuracy. With the advent of advanced technologies and machine learning techniques, there's an opportunity to enhance the accuracy of harvest quantity predictions. Therefore, this research leverages machine learning algorithms, specifically neural networks and MLP regression, to predict harvest quantities based on various input factors (likely including factors like temperature, growth stage, and others) and forecast the harvest quantity reliably. The proposed advanced neural network allows for the analysis of vast datasets, enabling more accurate predictions and actionable insights. In addition, accurate predictions enable stakeholders to optimize planting schedules, manage resources effectively, reduce waste, and ensure a stable food supply chain. The integration of neural networks fills the gap left by traditional methods, providing accurate, data-driven harvest forecasts to meet these pressing needs.