Identifying eco-efficient year-round crop combinations for rooftop greenhouse agriculture

Unidad de excelencia María de Maeztu CEX2019-000940-MPurpose: Rooftop greenhouses (RTGs) are agricultural systems that can improve the food supply chain by producing vegetables in unused urban spaces. However, to date, environmental assessments of RTGs have only focused on specific crops, without considering the impacts resulting from seasonality, combinations of crops and nonoperational time. We analyze vegetable production in an RTG over 4 years to determine the crop combinations that minimize yearly environmental impacts while diversifying food supply. Methods: The system under study consists of an integrated RTG (i-RTG) with a hydroponic system in Barcelona, in the Mediterranean region. By using life cycle assessment (LCA), we evaluate the environmental performance of 25 different crop cycles and 7 species cultivated during the period 2015-2018. Three functional units are used: 1 kg of edible fresh production, 1 unit of economic value (€) in the wholesale market and 1 kcal of nutritional value. The system boundaries consider two subsystems: infrastructure (greenhouse structure, rainwater harvesting system and auxiliary equipment) and operation (fertilizers and their emissions into water and substrate). In addition, we perform an eco-efficiency analysis, considering the carbon footprint of the crop cycles and their value at the wholesale market during their harvesting periods. Results and discussion: Spring tomato cycles exert the lowest impacts in all categories, considering all three functional units, due to the high yields obtained. In contrast, spinach and arugula have the highest impacts. Regarding relative impact, the greenhouse structure presented a large impact, while fertilizer production had notable relative contributions in tomato cycles. Moreover, nitrogen and phosphorus emissions from fertigation are the main causes of freshwater and marine eutrophication. By combining the most eco-efficient cycles, we can see that growing two consecutive tomato cycles is the best alternative with the functional unit of yield (0.49 kg CO2 eq./kg), whereas a long spring tomato cycle combined with bean and lettuce cycles in the autumn/winter is the best scenario when using market (0.70 kg CO2 eq./€) and nutritional value (3.18·10−3 kg CO2/ kcal). Conclusions: This study shows that increasing the diversity of the system leads to better environmental performance of greenhouse urban agriculture if suitable crops are selected for the autumn/winter season. The functional unit involving the economic value and the eco-efficiency analysis are useful to demonstrate the capability of the growing system to produce added-value vegetables under harsher conditions while categorizing and classifying the crops to select the most suitable combinations based on economic and environmental parameters

Application of Artificial Intelligence for Modeling the Internal Environment Condition of Polyethylene Greenhouses

Accurate temperature prediction and modeling are critical for effective management of agricultural greenhouses. By optimizing control and minimizing energy waste, farmers can maintain optimal environmental conditions, leading to improved crop yields and reduced financial losses. In this study, multiple models, including Multiple Linear Regression (MLR), Radial Basis Function (RBF), and Support Vector Machine (SVM), were compared to predict greenhouse air temperature. External parameters, such as air temperature (Tout), relative humidity (Hout), wind speed (W), and solar radiation (S), were used as inputs for these models, and the output was the inside temperature. The results showed that the RBF model with the LM (Levenberg–Marquardt) learning algorithm outperformed the other models, achieving the lowest error and the highest coefficient of determination (R2) value. The RBF model produced RMSE, MAPE, and R2 values of 1.32 °C, 3.23%, and 0.931, respectively. These results demonstrate that the RBF model with the LM learning algorithm can reliably predict greenhouse air temperatures for the next two hours. The ANN model can be applied to optimize time management and reduce energy losses, improving the overall efficiency of greenhouse operations

Neural - fuzzy approach for system identification

Most real-world processes have nonlinear and complex dynamics. Conventional methods of constructing nonlinear models from first principles are time consuming and require a level of knowledge about the internal functioning of the system that is often not available. Consequently, in such cases a nonlinear system identification procedure from observational data is a more attractive alternative. If the model structures to be investigated are purely chosen from a set of mathematically convenient structures, without incorporation of knowledge about the internal functioning, this is called black-box modeling. In case that some qualitative a priori information can be used in the above modeling procedure, it is sometimes referred to as gray-box modeling.Artificial neural network models and fuzzy models are typical examples of black-box and gray-box modeling, respectively. They have the same property of parallel processing and both serve as universal function approximators to perform nonlinear mapping. Each of them has its own weak and strong points. The fuzzy model has a transparent knowledge representation but has restricted learning ability. A neural network model can easily learn from new data, but it is difficult to interpret the information contained in its internal configuration.This thesis investigates how to construct an integrated neural-fuzzy model that can perform approximation of an unknown system via a set of given input-output observations. The result is the integrated neural-fuzzy model NUFZY, which combines the advantages of the above two paradigms, and concurrently compensates for their weaknesses. Thus, it has a transparent network structure and a self-explanatory representation of fuzzy rules.The NUFZY system is a special type of neural network, which is characterized by partial connections in its first and second layers. Through its network connections the NUFZY system carries out a particular type of fuzzy reasoning. Also, the NUFZY system is functionally equivalent to a zero th -order Takagi-Sugeno fuzzy model, so that it is an universal function approximator as well.Two existing learning methods, i.e., the orthogonal least squares and the prediction error algorithms, can be applied directly to the developed NUFZY model. The former method, referred to as batch learning, can be used to detect redundant fuzzy rules from the prototype rule base and to find the weight parameters of the NUFZY model by one-pass estimation. The latter, referred to as recursive learning, allows a fast adaptation of parameters of the NUFZY model. Several practical examples with real data of agricultural problems, which address the tomatoes growth and the greenhouse temperature, have been presented in this thesis, showing the capability of the NUFZY system for modeling nonlinear dynamic systems.Two questions concerning the integrated neural-fuzzy model are addressed by studying the equivalent T-S fuzzy model: how to obtain a linguistic interpretation of fuzzy rules deduced by learning from training examples, and how to incorporate a priori knowledge into the T-S fuzzy model.It is found out that it is possible to have linguistic interpretations of the crisp consequent of the T-S fuzzy rules by transforming them into Mamdani - like fuzzy rules. A new parameter set, the consequent significance level, is associated to the consequent of each Mamdani fuzzy rule to form an extended Mamdani fuzzy model. This model has a more flexible modeling ability than the ordinary Mamdani fuzzy model and has a comparable model accuracy as that of the T-S fuzzy model.Regarding the second question, an optimization approach is employed to systematically incorporate the a priori knowledge into the T-S fuzzy model. If the knowledge about the system behavior outside the identification data range is expressed in the form of a qualitative Mamdani fuzzy model, then this model can be incorporated in the objective function of the parameter estimation problem as an additional penalty term. Thus, the estimation of the parameters of the T-S fuzzy model from the identification data is constrained by the involvement of a priori knowledge. As a consequence, the resultant fuzzy model becomes more robust in the extrapolation domain. This approach can be extended to neural -fuzzy modeling without difficulty.To conclude, the beauty of the integrated neural-fuzzy model, NUFZY, developed in this thesis is that it is a neural network, enabling the implementation of efficient learning algorithms in an easy way, and that it is a fuzzy model at the same time, allowing incorporation of priori knowledge and transparent interpretation of its internal network structure. So, among the various methods of nonlinear system identification, the NUFZY model can serve as an attractive alternative.See alsohttp://www.math.utwente.nl/disc/dissertations/tien.html</A

Linear matrix inequalities tool to design predictive model control for greenhouse climate

Modeling and regulating the internal climate of a greenhouse have been a challenge as it is a complex and time variant system. The main goal is to regulate the internal climate considering the difference between nighttime and diurnal phases of the day. To depict the comportment of the greenhouse, a multi model approach based on two multivariable black box models have been proposed representing the diurnal and nighttime phases of the day. The least-squares method is utilized to identify the parameters of these two models based on an experimental collected data. We have shown that these two models are more representative than one model to describe the dynamic behavior of the greenhouse. The second contribution is to control the internal temperature and hygrometry respecting constraints on actuators and controlled variables. For this purpose, a constrained model predictive control scheme based on the multi-modeling approach have been developed. The optimization problem of the control law is transformed to a convex optimization problem includes linear matrix inequalities (LMI). The simulation results show that the adopted control method of indoor climate allows rapid and precise tracking of set points and rejects effectively the external disturbances affecting the greenhouse

Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives

Phenotyping plants is an essential component of any effort to develop new crop varieties. As plant breeders seek to increase crop productivity and produce more food for the future, the amount of phenotype information they require will also increase. Traditional plant phenotyping relying on manual measurement is laborious, time-consuming, error-prone, and costly. Plant phenotyping robots have emerged as a high-throughput technology to measure morphological, chemical and physiological properties of large number of plants. Several robotic systems have been developed to fulfill different phenotyping missions. In particular, robotic phenotyping has the potential to enable efficient monitoring of changes in plant traits over time in both controlled environments and in the field. The operation of these robots can be challenging as a result of the dynamic nature of plants and the agricultural environments. Here we discuss developments in phenotyping robots, and the challenges which have been overcome and others which remain outstanding. In addition, some perspective applications of the phenotyping robots are also presented. We optimistically anticipate that autonomous and robotic systems will make great leaps forward in the next 10 years to advance the plant phenotyping research into a new era

Actuators and sensors for application in agricultural robots: A review

In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future

Segmentation of thermal infrared images of cucumber leaves using K-means clustering for estimating leaf wetness duration

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Developing an economic estimation system for vertical farms

The concept of vertical farming is nearly twenty years old, however, there are only a few experimental prototypes despite its many advantages compared to conventional agriculture. Significantly, financial uncertainty has been identified as the largest barrier to the realization of a ‘real’ vertical farm. Some specialists have provided ways to calculate costs and return on investment, however, most of them are superficial with calculations based on particular contextual circumstances. To move the concept forwards a reliable and flexible estimating tool, specific to this new building typology, is clearly required. A computational system, software named VFer, has therefore been developed by the authors to provide such a solution. This paper examines this highly flexible, customised system and results from several typical vertical farm configurations in three mega-cities (Shanghai, London and Washington DC) are used to elucidate the potential economic return of vertical farms

Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed