Cherry Tomato Production in Intelligent Greenhouses—Sensors and AI for Control of Climate, Irrigation, Crop Yield, and Quality

Abstract

Greenhouses and indoor farming systems play an important role in providing fresh and nutritious food for the growing global population. Farms are becoming larger and greenhouse growers need to make complex decisions to maximize production and minimize resource use while meeting market requirements. However, highly skilled labor is increasingly lacking in the greenhouse sector. Moreover, extreme events such as the COVID-19 pandemic, can make farms temporarily less accessible. This highlights the need for more autonomous and remote-control strategies for greenhouse production. This paper describes and analyzes the results of the second “Autonomous Greenhouse Challenge”. In this challenge, an experiment was conducted in six high-tech greenhouse compartments during a period of six months of cherry tomato growing. The primary goal of the greenhouse operation was to maximize net profit, by controlling the greenhouse climate and crop with AI techniques. Five international teams with backgrounds in AI and horticulture were challenged in a competition to operate their own compartment remotely. They developed intelligent algorithms and use sensor data to determine climate setpoints and crop management strategy. All AI supported teams outperformed a human-operated greenhouse that served as reference. From the results obtained by the teams and from the analysis of the different climate-crop strategies, it was possible to detect challenges and opportunities for the future implementation of remote-control systems in greenhouse production.

Contribution to the Sustainability of Agricultural Production in Greenhouses Built on Slope Soils: A Numerical Study of the Microclimatic Behavior of a Typical Colombian Structure

“…Cultivation systems in greenhouses are becoming one of the most widely used alternatives for the sustainable intensification of food production such as fruit and vegetables in many countries [1,2]. These agricultural production systems allow the increase of crop yields per unit area and optimization of water management per unit of product, compared to open field crops [3]. In addition, the use of these structures allows food production in regions where climatic conditions are adverse for agricultural production, ensuring the supply of fresh food and increasing the levels of food security for these territories.…”

iGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control

“…• Automatoes strategy: Automatoes is the champion of the Autonomous Greenhouse Challenge. The organizer briefly introduces their algorithm in [17]. They combine a predictive control algorithm with expert knowledge.…”

“…However, only six trajectories are far from satisfactory for training a robust model. WUR developed a greenhouse tomato simulator based on empirical formula to approximate the real greenhouse planting process [17]. We randomly simulate thousands of control strategies to record their rollouts, and collect these trajectories as the training set of our model.…”

“…With global challenges in food, energy, and water, the digital transformation of the agricultural industry is extremely urgently in need. In order to meet these challenges, smart agriculture solutions, based on automation and intelligent decision-making, have attracted more and more attention [7,17,31].…”

Greenhouse Crop Simulation Models and Microclimate Control Systems, A Review

“…Whereas black-box models are more used for applications that involve control, optimization, and design of the greenhouse system [7]. Mechanistic models are based on physical Equations [9] and give the opportunity to be used for intelligent decision support on climate control actions [10]. They enable a quantitative approach of the greenhouse system as transparent mechanistic models, allowing for optimization algorithms to find an optimal control, and they are physically interpretable.…”