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Background: Controlled environment agriculture, particularly vertical farms (VF), also called plant factories, is often claimed as a solution for global food security due to its ability to produce crops unaffected by weather or pests. In principle, essential macronutrients of the human diet, like protein, could technically be produced in VF. This aspect becomes relevant in the era of protein transition, marked by an increasing consumer interest in plant‐based protein and environmental challenges faced by conventional farming. However, the real question is: what does the cultivation of protein crops in VF imply in terms of resource use? To address this, a study was conducted using a VF experiment focusing on two soybean cultivars. Results: With a variable plant density to optimize area use, and because of the ability to have more crop cycles per year, protein yield per square metre of crop was about eight times higher than in the open field. Assuming soy as the only protein source in the diet, the resources needed to get total yearly protein requirement of a reference adult would be 20 m2 of crop area, 2.4 m3 of water and 16 MWh of electricity, versus 164 m2, 111 m3 and 0.009 MWh in the field. Conclusions: The study's results inform the debate on protein production and the efficiency of VF compared to conventional methods. With current electricity prices, it is unlikely to justify production of simple protein crops in VF or promote it as a solution to meet global protein needs. © 2024 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The rapid urban growth seen globally in recent years has not been supported by a simultaneous increase in agricultural land and/or crop productivity. Producing crops in (peri‐)urban areas shows good potential to provide the vegetable products for a healthy and balanced diet for the growing population, but it has to deal with the local availability of resources. Thus, meeting the food requirements of the urban population as efficiently and robustly as possible is a challenge. This study developed a methodology to estimate the use of resources of urban farming systems to produce energy‐ and nutrient‐dense vegetables capable of meeting human dietary needs. The method was applied to two extremely different cultivation systems (an open field farm and a plant factory with artificial lighting) for the production of seven crops. The results on the resource efficiencies to meet the annual per‐capita vegetable requirements are discussed in relation to crop type, local climate and cultivation system. The application of this methodology can support farmers' decisions on the choice of crops and the type of urban farming systems that are most efficient in contributing to a plant‐based diet. The results can also be translated into water, energy, and surface area needed to meet the nutritional requirements at a city‐regional level. [ABSTRACT FROM AUTHOR]

Greenhouse horticulture grows increasingly important due to its ability to provide a controlled microclimate which is optimizable for highly efficient crop growth and resource use, although it may come at a significant energy and investment cost. One of the most crucial inputs in any greenhouse is sunlight, giving free energy and light for greenhouse crop growth. However, it is enormously variable, both geographically and seasonally. This review discusses materials and technologies usable in greenhouse cover and screen materials which can passively manipulate the incident sunlight to transmit a light spectrum that is ideal for crop growth, thereby improving the yield, and for greenhouse microclimate management, thereby reducing the energy usage of greenhouses. The current status of spectrum‐manipulating technology in greenhouses, developments over the last few years, some potential innovations adaptable from diverse fields to greenhouse horticulture, and the associated challenges, are discussed. [ABSTRACT FROM AUTHOR]

Recent studies indicate that food demand will increase by 35–56% over the period 2010–2050 due to population increase, economic development, and urbanization. Greenhouse systems allow for the sustainable intensification of food production with demonstrated high crop production per cultivation area. Breakthroughs in resource-efficient fresh food production merging horticultural and AI expertise take place with the international competition "Autonomous Greenhouse Challenge". This paper describes and analyzes the results of the third edition of this competition. The competition's goal is the realization of the highest net profit in fully autonomous lettuce production. Two cultivation cycles were conducted in six high-tech greenhouse compartments with operational greenhouse decision-making realized at a distance and individually by algorithms of international participating teams. Algorithms were developed based on time series sensor data of the greenhouse climate and crop images. High crop yield and quality, short growing cycles, and low use of resources such as energy for heating, electricity for artificial light, and CO2 were decisive in realizing the competition's goal. The results highlight the importance of plant spacing and the moment of harvest decisions in promoting high crop growth rates while optimizing greenhouse occupation and resource use. In this paper, images taken with depth cameras (RealSense) for each greenhouse were used by computer vision algorithms (Deepabv3+ implemented in detectron2 v0.6) in deciding optimum plant spacing and the moment of harvest. The resulting plant height and coverage could be accurately estimated with an R2 of 0.976, and a mIoU of 98.2, respectively. These two traits were used to develop a light loss and harvest indicator to support remote decision-making. The light loss indicator could be used as a decision tool for timely spacing. Several traits were combined for the harvest indicator, ultimately resulting in a fresh weight estimation with a mean absolute error of 22 g. The proposed non-invasively estimated indicators presented in this article are promising traits to be used towards full autonomation of a dynamic commercial lettuce growing environment. Computer vision algorithms act as a catalyst in remote and non-invasive sensing of crop parameters, decisive for automated, objective, standardized, and data-driven decision making. However, spectral indexes describing lettuces growth and larger datasets than the currently accessible are crucial to address existing shortcomings between academic and industrial production systems that have been encountered in this work. [ABSTRACT FROM AUTHOR]