State of the Art of Urban Smart Vertical Farming Automation System: Advanced Topologies, Issues and Recommendations

Applications of optical sensing and imaging spectroscopy in indoor farming: A systematic review

As demand for food continues to rise, innovative methods are needed to sustainably and efficiently meet the growing pressure on agriculture. Indoor farming and controlled environment agriculture have emerged as promising approaches to address this challenge. However, optimizing fertilizer usage, ensuring homogeneous production, and reducing agro-waste remain substantial challenges in these production systems. One potential solution is the use of optical sensing technology, which can provide real-time data to help growers make informed decisions and enhance their operations. optical sensing can be used to analyze plant tissues, evaluate crop quality and yield, measure nutrients, and assess plant responses to stress. This paper presents a systematic literature review of the current state of using spectral-optical sensors and hyperspectral imaging for indoor farming, following the PRISMA 2020 guidelines. The study surveyed existing studies from 2017 to 2023 to identify gaps in knowledge, provide researchers and farmers with current trends, and offer recommendations and inspirations for possible new research directions. The results of this review will contribute to the development of sustainable and efficient methods of food production.

Environmental parameters factors exploration on lettuce seed germination with hydrogel

Lettuce (Lactuca sativa) germination is sensitive to environmental conditions. Recently, hydrogel has received increased attention as an alternative media to soil for seed germination. Compared to soil seeding, hydrogel-aided germination provides more controlled seeding environments. However, there are still challenges preventing hydrogel-aided seed germination from being widely used in industry production or academic studies, such as hydrogel formulation variations, seeding operation standardization, and germination evaluation. In this study, we tested how the combination of multiple environmental conditions affect lettuce seed germination time, which is measured as the time needed for the first pair of leaves to appear (leaf emergence) or, alternatively, the third leaf to appear (leaf development). We found that germination time and success rate of two lettuce varieties (Iceberg A and Butter Crunch) showed different sensitivities to pH, Hoagland formulations and concentrations, light intensity, and hydrogel content. We have conducted statistical analysis on the correlation between germination time and these environmental conditions.

Light Energy Efficiency in Lettuce Crop: Structural Indoor Designs Simulation

Indoor agricultural offers efficient alternatives for intensive food production through automation technologies and controlled environments. Light plays a crucial role in plant development; however, photons captured by the crop are often wasted in empty spaces, resulting in low light efficiency and high energy costs. This research aims to simulate eight structural designs for an indoor lettuce crop, exploring different planting systems and light and culture bed combinations (static and mobile) to identify the most effective mechanism for light efficiency during crop growth. The simulations were carried out with spreadsheets based on applying formulas of yield in dry biomass per photosynthetic photons, lighting costs, harvest, and production. The results indicate that Circular Moving Light and Mobile Culture Bed with Quincunx Planting (CML-QM) and Circular Moving Light and Mobile Culture Bed with Linear Planting (CML-LPM) exhibit higher photon capture percentages (85% and 80%, respectively) and lower electricity consumption compared to static designs. The simulation results demonstrate the potential for significant improvements in photon capture and cost savings through optimized system designs. This investigation provides valuable insights for designing more efficient systems and reducing electricity consumption to enhance the capture of photosynthetic photons in indoor lettuce cultivation.

Sensors on the Internet of Things Systems for Urban Disaster Management: A Systematic Literature Review

The occurrence of disasters has the potential to impede the progress of sustainable urban development. For instance, it has the potential to result in significant human casualties and substantial economic repercussions. Sustainable cities, as outlined in the United Nations Sustainable Development Goal 12, prioritize the objective of disaster risk reduction. According to the Gesi Smarter 2030, the Internet of Things (IoT) assumes a pivotal role in the context of smart cities, particularly in domains including smart grids, smart waste management, and smart transportation. IoT has emerged as a crucial facilitator for the management of disasters, contributing to the development of cities that are both resilient and sustainable. This systematic literature analysis seeks to demonstrate the sensors utilized in IoT for the purpose of urban catastrophe management. The review encompasses both the pre-disaster and post-disaster stages, drawing from a total of 72 articles. During each stage, we presented the characteristics of sensors employed in IoT. Additionally, we engaged in a discourse regarding the various communication technologies and protocols that can be utilized for the purpose of transmitting the data obtained from sensors. Furthermore, we have demonstrated the methodology for analyzing and implementing the data within the application layer of IoT. In conclusion, this study addresses the existing research deficiencies within the literature and presents potential avenues for future exploration in the realm of IoT-enabled urban catastrophe management, drawing upon the findings of the evaluated publications.

A Review of Successes and Impeding Challenges of IoT-Based Insect Pest Detection Systems for Estimating Agroecosystem Health and Productivity of Cotton

Using artificial intelligence (AI) and the IoT (Internet of Things) is a primary focus of applied engineering research to improve agricultural efficiency. This review paper summarizes the engagement of artificial intelligence models and IoT techniques in detecting, classifying, and counting cotton insect pests and corresponding beneficial insects. The effectiveness and limitations of AI and IoT techniques in various cotton agricultural settings were comprehensively reviewed. This review indicates that insects can be detected with an accuracy of between 70 and 98% using camera/microphone sensors and enhanced deep learning algorithms. However, despite the numerous pests and beneficial insects, only a few species were targeted for detection and classification by AI and IoT systems. Not surprisingly, due to the challenges of identifying immature and predatory insects, few studies have designed systems to detect and characterize them. The location of the insects, sufficient data size, concentrated insects on the image, and similarity in species appearance are major obstacles when implementing AI. Similarly, IoT is constrained by a lack of effective field distance between sensors when targeting insects according to their estimated population size. Based on this study, the number of pest species monitored by AI and IoT technologies should be increased while improving the system's detection accuracy.

Vertical Farming Monitoring: How Does It Work and How Much Does It Cost?

Climate change, resource scarcity, and a growing world population are some of the problems facing traditional agriculture. For this reason, new cultivation systems are emerging, such as vertical farming. This is based on indoor cultivation, which is not affected by climatic conditions. However, vertical farming requires higher consumption of water and light, since in traditional agriculture those resources are free. Vertical cultivation requires the use of new technologies and sensors to reduce water and energy consumption and increase its efficiency. The sensorization of these systems makes it possible to monitor and evaluate their performance in real time. In addition, vertical farming faces economic uncertainty since its profitability has not been studied in depth. This article studies the most important variables when monitoring a vertical farming system and proposes the sensors to be used in the data acquisition system. In addition, this study presents a cost model for the installation of this type of system. This cost model is applied to a case study to evaluate the profitability of installing this type of infrastructure. The results obtained suggest that the investment made in VF installations could be profitable in a period of three to five years.

Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

Controlled environment agriculture (CEA) is an unconventional production system that is resource efficient, uses less space, and produces higher yields. Deep learning (DL) has recently been introduced in CEA for different applications including crop monitoring, detecting biotic and abiotic stresses, irrigation, microclimate prediction, energy efficient controls, and crop growth prediction. However, no review study assess DL's state of the art to solve diverse problems in CEA. To fill this gap, we systematically reviewed DL methods applied to CEA. The review framework was established by following a series of inclusion and exclusion criteria. After extensive screening, we reviewed a total of 72 studies to extract the useful information. The key contributions of this article are the following: an overview of DL applications in different CEA facilities, including greenhouse, plant factory, and vertical farm, is presented. We found that majority of the studies are focused on DL applications in greenhouses (82%), with the primary application as yield estimation (31%) and growth monitoring (21%). We also analyzed commonly used DL models, evaluation parameters, and optimizers in CEA production. From the analysis, we found that convolutional neural network (CNN) is the most widely used DL model (79%), Adaptive Moment Estimation (Adam) is the widely used optimizer (53%), and accuracy is the widely used evaluation parameter (21%). Interestingly, all studies focused on DL for the microclimate of CEA used RMSE as a model evaluation parameter. In the end, we also discussed the current challenges and future research directions in this domain.

Growth parameter acquisition and geometric point cloud completion of lettuce

The plant factory is a form of controlled environment agriculture (CEA) which is offers a promising solution to the problem of food security worldwide. Plant growth parameters need to be acquired for process control and yield estimation in plant factories. In this paper, we propose a fast and non-destructive framework for extracting growth parameters. Firstly, ToF camera (Microsoft Kinect V2) is used to obtain the point cloud from the top view, and then the lettuce point cloud is separated. According to the growth characteristics of lettuce, a geometric method is proposed to complete the incomplete lettuce point cloud. The treated point cloud has a high linear correlation with the actual plant height (R ² = 0.961), leaf area (R ² = 0.964), and fresh weight (R ² = 0.911) with a significant improvement compared to untreated point cloud. The result suggests our proposed point cloud completion method have has the potential to tackle the problem of obtaining the plant growth parameters from a single 3D view with occlusion.

Vertical Farming Perspectives in Support of Precision Agriculture Using Artificial Intelligence: A Review

Vertical farming is a new agricultural system which aims to utilize the limited access to land, especially in big cities. Vertical agriculture is the answer to meet the challenges posed by land and water shortages, including urban agriculture with limited access to land and water. This research study uses the Preferred Reporting for Systematic Review and Meta-analysis (PRISMA) item as one of the literary approaches. PRISMA is one way to check the validity of articles for a literature review or a systematic review resulting from this paper. One of the aims of this study is to review a survey of scientific literature related to vertical farming published in the last six years. Artificial intelligence with machine learning, deep learning, and the Internet of Things (IoT) in supporting precision agriculture has been optimally utilized, especially in its application to vertical farming. The results of this study provide information regarding all of the challenges and technological trends in the area of vertical agriculture, as well as exploring future opportunities.

CEA Systems: the Means to Achieve Future Food Security and Environmental Sustainability?

As demand for food production continues to rise, it is clear that in order to meet the challenges of the future in terms of food security and environmental sustainability, radical changes are required throughout all levels of the global food system. Controlled Environment Agriculture (CEA) (a.k.a. indoor farming) has an advantage over conventional farming methods in that production processes can be largely separated from the natural environment, thus, production is less reliant on environmental conditions, and pollution can be better restricted and controlled. While output potential of conventional farming at a global scale is predicted to suffer due to the effects of climate change, technological advancements in this time will drastically improve both the economic and environmental performance of CEA systems. This article summarizes the current understanding and gaps in knowledge surrounding the environmental sustainability of CEA systems, and assesses whether these systems may allow for intensive and fully sustainable agriculture at a global scale. The energy requirements and subsequent carbon footprint of many systems is currently the greatest environmental hurdle to overcome. The lack of economically grown staple crops which make up the majority of calories consumed by humans is also a major limiting factor in the expansion of CEA systems to reduce the environmental impacts of food production at a global scale. This review introduces the concept of Integrated System CEA (ISCEA) in which multiple CEA systems can be deployed in an integrated localized fashion to increase efficiency and reduce environmental impacts of food production. We conclude that it is feasible that with sufficient green energy, that ISCEA systems could largely negate most forms of environmental damage associated with conventional farming at a global scale (e.g., GHGs, deforestation, nitrogen, phosphorus, pesticide use, etc.). However, while there is plenty of research being carried out into improving energy efficiency, renewable energy and crop diversification in CEA systems, the circular economy approach to waste is largely ignored. We recommend that industries begin to investigate how nutrient flows and efficiencies in systems can be better managed to improve the environmental performance of CEA systems of the future.