Effective planning and resource allocation in agricultural methods depend on accurate estimate of harvest numbers. The precise estimate of harvest quantity is the challenge here. Farmers, companies in the agriculture sector, and legislators all depend on accurate harvest predictions to guide their decisions on crop management, distribution, and market planning. Therefore, in agriculture, the demand for accurate crop forecasts is first priority. While distributors need estimates for logistical preparations, farmers must effectively allocate their resources, and legislators base food security plans on these forecasts. Using the expertise and knowledge of agricultural professionals, historical data analysis, and fundamental statistical techniques—the conventional method of harvest amount prediction was Though basic, these techniques lacked ability to handle intricate, multi-dimensional data. They also frequently found it impossible to handle the enormous amounts of current period data. Therefore, increasingly complex methods capable of analyzing vast and varied datasets to raise forecast accuracy were necessary. Advanced technology and machine learning approaches provide a chance to improve the accuracy of harvest quantity forecasts. Therefore, this study uses machine learning algorithms—more especially, neural networks and MLP regression—to predict harvest quantities based on different input data (probably includes elements like temperature, growth stage, and others) and consistently forecasts the harvest quantity. The suggested sophisticated neural network makes it possible to analyze large volumes of data, therefore supporting more accurate forecasts and practical insights. Furthermore, correct forecasts help stakeholders to maximize planting plans, control resources, lower waste, and guarantee a consistent food supply chain. By offering precise, data-driven harvest projections to satisfy these urgent demands, neural networks help to close the void created by conventional techniques.

plant development, harvest quantity, green house, machine learning.