GREENHOUSE COST INDEX METHODOLOGY BASED ON THE DIVERSE REGIONS OF IRAQ

Abstract

Greenhouses have become widespread structures that create an ideal microclimate for growing crops worldwide. A greenhouse is a structure that allows people to regulate climatic conditions, such as, temperature and humidity. There are many different designs of greenhouses, but generally, these buildings include large areas of transparent material to capture the light and heat of the sun. They also offer protection from unfavorable weather conditions and pests, providing a popular solution for crop production worldwide, including Iraq, which uses alternative energy sources for climate control. Using machine learning models has helped design different greenhouse types; however, their ability to predict costs and designs based on features is yet to exist. Therefore, to address these issues, this study aimed to develop cost-effective and user-friendly greenhouse systems through two different approaches: Firstly, the use of random forests (RFs) model with the highest precision (0.99) formulated the cost of the greenhouse for new input data to calculate a greenhouse cost estimate based on the system's performance as a benchmark while selecting the greenhouse's features through training and testing, and secondly, the use of the farmer's desired price as a basis for developing a greenhouse design. This scientific approach will enable the farming community to manage the costs of various aspects, such as, building materials, energy sources, climate control devices, water and fertilizer delivery, growing substrates, internal logistics, and labor. The presented research will provide farmers with a practical basis that also considers the constraints, i.e., the economy, climate, law, market, and resource availability. It will empower the farmers to make the right decisions regarding greenhouse systems with their specific requirements and circumstances.