1. Introduction

The increasing market demand for high-quality food products have replaced open-field cultivations of Solanaceae and Cucurbits crops with modern plant production systems for more efficient use of available resources. Closed-field cultivations by means of commercial

© The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, © 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

distribution, and eproduction in any medium, provided the original work is properly cited.

greenhouses have been changed over the last three decades from basic structures to advanced controlled environments for optimizing plant's productivity and producing high yields at low expenses. The higher costs of greenhouse operation due to the initial investments on structure, equipment, and energy necessitate practicing advanced techniques of automation for efficient control of the microenvironment. Research trends in this field are directed toward developing innovative solutions for shifting from energy-consuming to energy neutral greenhouses with the ultimate objective of increasing profits. This is, however, challenging due to the lack of accurate information about interactions between crops and environment at different growth stages, as well as the complexity of the dynamic system that is subjected to changes with internal and external factors. Plant-based engineering has helped researchers with proper management policies to embrace these uncertainties through modeling and integrated-learning approaches. Several uncertainties with greenhouse cultivation include climate variability, expected yield, optimum references of microclimate parameters, comfort ratios, insecurity of resources, complexity of the system states, lack of accurate information about interactions between plants and environment, and the relationships between biological and ecological system.

Greenhouse microclimate control has been a large field of study for many years. Much work has been done for moderate cold climate conditions as opposed to tropical lowlands. In contrast to cold arid climate, the main objective of a greenhouse in hot and humid regions such as lowlands of south-east Asia (Figure 1) is to protect crop against fluctuations of external conditions such as extreme winds, heavy seasonal rainfalls, typhoons, extreme solar radiation, occasional water shortage, high air temperature, high humidity, and invasion of pests and diseases. The major concern with greenhouses in these regions is the crop stress due to the adverse microclimate that reduces plant evapotranspiration rate and causes production failure. Evaporative cooling systems by means of misting, pad-and-fan, and swamp cooling are widely used in tropical greenhouses of south-east Asia for manipulating crop growth microclimate; however, these systems have not reached their optimum potential due to their conventional automation and control methods. If properly managed, tropical greenhouses can provide suitable growth condition for tomato cultivation by maintaining inside microclimate close to the outside, with an expected yield that varies between 30 and over 100 tons/ha (vs. open-field yield of 15–30 tons/ha) depending on soil culture or hydroponics medium.

Profitability and investment returns of commercial greenhouses are tightly linked to management decisions. One of the main factors to be considered in this context is the sustainability of

Figure 1. Outside and inside view of tropical greenhouses in the lowlands of Malaysia.

greenhouses have been changed over the last three decades from basic structures to advanced controlled environments for optimizing plant's productivity and producing high yields at low expenses. The higher costs of greenhouse operation due to the initial investments on structure, equipment, and energy necessitate practicing advanced techniques of automation for efficient control of the microenvironment. Research trends in this field are directed toward developing innovative solutions for shifting from energy-consuming to energy neutral greenhouses with the ultimate objective of increasing profits. This is, however, challenging due to the lack of accurate information about interactions between crops and environment at different growth stages, as well as the complexity of the dynamic system that is subjected to changes with internal and external factors. Plant-based engineering has helped researchers with proper management policies to embrace these uncertainties through modeling and integrated-learning approaches. Several uncertainties with greenhouse cultivation include climate variability, expected yield, optimum references of microclimate parameters, comfort ratios, insecurity of resources, complexity of the system states, lack of accurate information about interactions between plants and

Greenhouse microclimate control has been a large field of study for many years. Much work has been done for moderate cold climate conditions as opposed to tropical lowlands. In contrast to cold arid climate, the main objective of a greenhouse in hot and humid regions such as lowlands of south-east Asia (Figure 1) is to protect crop against fluctuations of external conditions such as extreme winds, heavy seasonal rainfalls, typhoons, extreme solar radiation, occasional water shortage, high air temperature, high humidity, and invasion of pests and diseases. The major concern with greenhouses in these regions is the crop stress due to the adverse microclimate that reduces plant evapotranspiration rate and causes production failure. Evaporative cooling systems by means of misting, pad-and-fan, and swamp cooling are widely used in tropical greenhouses of south-east Asia for manipulating crop growth microclimate; however, these systems have not reached their optimum potential due to their conventional automation and control methods. If properly managed, tropical greenhouses can provide suitable growth condition for tomato cultivation by maintaining inside microclimate close to the outside, with an expected yield that varies between 30 and over 100 tons/ha (vs. open-field yield of 15–30 tons/ha) depending on soil culture or hydroponics medium.

Profitability and investment returns of commercial greenhouses are tightly linked to management decisions. One of the main factors to be considered in this context is the sustainability of

Figure 1. Outside and inside view of tropical greenhouses in the lowlands of Malaysia.

environment, and the relationships between biological and ecological system.

168 Plant Engineering

operations through proper management of available resources. Modern greenhouses are required to exhibit integration of automation, cultural practices, and environmental control using object-oriented analysis of the subsystems. The primary concepts and methods of automation-culture-environment system analysis (ACESYS) in controlled environments plant production (CEPP) have been introduced and expanded in the works of [1–3]. Some of the earliest examples of object-oriented analysis and modeling applications including optimal control strategies and decision-support software in advanced CEPP systems can be found in the works of [2, 4–6]. The purpose of object-oriented system analysis approach according to Ref. [3] is to develop a set of foundation classes that can be used to effectively describe the components of the automation system. This, however, requires a comprehensive understanding of the interaction between crop's growth response and environment characteristics. Some of the specific applications and benefits of system analysis in greenhouse production includes integrated energy-efficient strategies, extracting unique and new knowledge that provides valuable insight to local growers and beyond, understanding limitations of resources and balancing between input and output expectancies, improving technology and increasing returns, providing business attraction for local stakeholders, minimizing energy requirements and eliminating tedious operations, increasing production quality and quantity to satisfy market demands, and technology adaptation by balancing between fixed and flexible automation for various crop production. With this perspective, the convolution of several possible scenarios and combination of culture classes (i.e., climate control parameters) and objects (i.e., tomato crop at different growth stages) necessitates computer-based analysis program within the concepts of a systematic framework approach such as adaptive management.

Adaptive management was initially introduced at the University of Florida [7] as an iterative method for managing natural resource in the systems with wide range of responses to management choices and to help manager's difficulty in understanding the systems' dynamics [8] and plant's responses [9]. It is defined as "a systematic process for continually improving management policies and practices by learning from the outcomes of operational programs" [10]. Adaptive management was created based on the needs of environment and ecosystem managers with an iterative processing tool that acknowledged complexity and uncertainty, with a focus on learning and for continuous inputs [11]. It has been widely used as a new design technique for large database that manages and assists the immense data collection, data analysis, and data storage of distributed sets of experiments associated with environmental, meteorological, biological, and medical research problems or other technical and experimental assessments that utilize large-scale data sources within multiple and separate engineering or laboratory facilities. Examples include the work of Refs. [12–15]. The principles of adaptive management according to Ref. [16] suggest using the best available knowledge to design and implement management plans, while establishing an institutional structure that enables learning from outcomes to adjust and improve future decision making. This structured approach is an efficient method in developing decision-support tools for systems design, management, and operation by recognizing the importance of natural variability in contributing to ecological resilience and productivity.

This chapter provides a systematic process of incorporating new and existing knowledge that can be used in developing management decisions for achieving optimum microclimate. It describes design and concepts of an adaptive management framework for evaluating optimality degrees and comfort ratios of air temperature (T), relative humidity (RH), and vapor pressure deficit (VPD), as well as prediction of the expected yield in greenhouse cultivation of tomato. The presented framework was designed to allow production managers to ask "what-if" type of questions for further quantitative inclusion to avoid possible detriment decisions. It also provides an in-depth rigorous analysis tool for decision making or decision procrastination when facing uncertainties. It can assist in enhancing scheduling efficiency and guiding investments through different simulated scenarios that are based on information analysis to support optimal restoration strategies. In the rest of this chapter, we refer to the term "microclimate parameters T, RH, or VPD" by M. We also use Optð Þ¼ M α, and Cf tð Þ¼ M, t, α β, to refer to the terms "optimality degree" and "comfort ratio," respectively, defined in Sections 3.1 and 3.2.
