Demystifying Polyhouse Microclimates: A Review of Modelling Tools and Strategies
K.V.L. Supraja *
Department of Soil and Water Conservation Engineering, Dr. NTR CAE, Bapatla, Andhra Pradesh, India.
K.Krupavathi
Department of Irrigation and Drainage Engineering, Dr. NTR CAE, Bapatla, Andhra Pradesh, India.
R.Ganesh Babu
Department of Irrigation and Drainage Engineering, CAE, Madakasira, India.
Ch. Someswara Rao
Department of Food Processing Engineering, Dr. NTR College of Food Science and Technology, Bapatla, India.
*Author to whom correspondence should be addressed.
Abstract
It is impossible to feed the entire population of the world with conventional agriculture in this period of sudden climate change and degradation of natural resources. It makes it essential to adapt the protected cultivation, which makes it possible to provide favorable conditions for plant growth all-round the year. Thus, efficient management of protected cultivation techniques helps to obtain sustainable agriculture. For this, modelling of microclimate inside the greenhouse helps to better understand the dynamic variability of the microclimatic characteristics and their impact on crop growth. For this study, nearly 480 papers were reviewed on different aspects of microclimate modeling and machine learning algorithms out of which, 150 articles published in journals of high impact factors were selected and up to 80 references were cited in this article. This paper explored the available modelling techniques which include Physical-based models (offer high accuracy but require extensive computation time) and Data-driven models (faster but necessitate large datasets for analysis.). This review helps the researchers to get a detailed knowledge regarding the microclimate inside a polyhouse and various models available for greenhouse microclimate modelling and their ability to simulate the microclimate efficiently and accurately. Microclimate modeling helps understand the dynamic variations within a greenhouse and their impact on crops. The limitations of current models (computational time vs. data requirements) emphasize the need for hybrid model development. The rise of greenhouse automation and precision agriculture underscores the importance of accurate microclimate modeling. This paper, thus highlights the critical role of microclimate modeling in sustainable greenhouse agriculture, providing a comprehensive analysis of existing modeling techniques and their limitations. Thus, supporting the development of automated and data-driven greenhouse management practices.
Keywords: Protected cultivation, microclimate modeling, CFD, ML, greenhouse automation