An integrated farm management information system for the South African hydroponic industry

The world’s population is growing at an average of 1.2 percent per annum and forecasts see the global population reaching 9.6 billion by 2050. This places great demands on the sustained production capacity of agricultural organisations to meet the desperate need for nutrition. This problem will continue to persist if production methods do not evolve to improve production and quality. Hydroponics and Controlled Environment Agriculture (CEA) was first seen in Rome during the 1st Century. Then sixteen Centuries later Greenhouses were developed in France and England as experimental hydroponics for basic laboratory research. Rapid expansion took place from about the 1950’s in areas where traditional openenvironment agriculture was difficult or impossible such as the deserts of Iran, Abu Dhabi and California. Sixty-five years later in 2015 hydroponics and CEA are well established around the world with thousands of hectares under propagation. Hydroponics is a method of agricultural production that has been refined over the years to become an exact science. Through the application of technology and know-how the physiological processes within plants can be manipulated and controlled to produce superior results. These results require less land area and less water to accomplish. It can be seen, based on this development, that hydroponics is such an evolution that has the capacity to meet the needs of a growing global population and its nutritional needs. The challenge lies, though, in the scientific understanding and application of knowledge in growing and managing a hydroponics farm. This study seeks to determine the internal data and external information needs of farmers in the hydroponics industry. This data and information will be integrated into a Farm Management Information System (FMIS) model that will be used for decision making, report generation and documentation. The problem leading to this study is the dissemination of data and information sources that are currently underutilised and difficult to access. In determining the internal data and external information needs, an empirical study was conducted using structured interview. Thirty farm managers were interviewed to assess what their current information system consisted of, whether they had a need for an FMIS and what internal data and external information was needed which related to four functional components of hydroponic farming. The results of this study indicate that there is a need for an FMIS for the hydroponic industry in South Africa. The results also indicate that managers are not fully satisfied with the performance of their current information system and would be interested in considering alternative information systems. Data points relating to the four functional components were assessed and integrated into an FMIS model for the hydroponic industry. This model sets out to integrate internal data and external information for purposes of decision making, report generation and documentation

Smart greenhouses as the path towards precision agriculture in the food-energy and water nexus: case study of Qatar

Greenhouse farming is essential in increasing domestic crop production in countries with limited resources and a harsh climate like Qatar. Smart greenhouse development is even more important to overcome these limitations and achieve high levels of food security. While the main aim of greenhouses is to offer an appropriate environment for high-yield production while protecting crops from adverse climate conditions, smart greenhouses provide precise regulation and control of the microclimate variables by utilizing the latest control techniques, advanced metering and communication infrastructures, and smart management systems thus providing the optimal environment for crop development. However, due to the development of information technology, greenhouses are undergoing a big transformation. In fact, the new generation of greenhouses has gone from simple constructions to sophisticated factories that drive agricultural production at the minimum possible cost. The main objective of this paper is to present a comprehensive understanding framework of the actual greenhouse development in Qatar, so as to be able to support the transition to sustainable precision agriculture. Qatar’s greenhouse market is a dynamic sector, and it is expected to mark double-digit growth by 2025. Thus, this study may offer effective supporting information to decision and policy makers, professionals, and end-users in introducing new technologies and taking advantage of monitoring techniques, artificial intelligence, and communication infrastructure in the agriculture sector by adopting smart greenhouses, consequently enhancing the Food-Energy-Water Nexus resilience and sustainable development. Furthermore, an analysis of the actual agriculture situation in Qatar is provided by examining its potential development regarding the existing drivers and barriers. Finally, the study presents the policy measures already implemented in Qatar and analyses the future development of the local greenhouse sector in terms of sustainability and resource-saving perspective and its penetration into Qatar’s economy.Open Access funding provided by the Qatar National Library. The authors are grateful to Qatar National Research Fund (QNRF) for funding and supporting the M-NEX Project (Grant No. BFSUGI01-1120-170005) in Qatar. The M-NEX is a project of the Collaborative Research Area Belmont Forum (Grant No. 11314551)

A Neutrosophic Clinical Decision-Making System for Cardiovascular Diseases Risk Analysis

Cardiovascular diseases are the leading cause of death worldwide. Early diagnosis of heart disease can reduce this large number of deaths so that treatment can be carried out. Many decision-making systems have been developed, but they are too complex for medical professionals. To target these objectives, we develop an explainable neutrosophic clinical decision-making system for the timely diagnose of cardiovascular disease risk. We make our system transparent and easy to understand with the help of explainable artificial intelligence techniques so that medical professionals can easily adopt this system. Our system is taking thirtyfive symptoms as input parameters, which are, gender, age, genetic disposition, smoking, blood pressure, cholesterol, diabetes, body mass index, depression, unhealthy diet, metabolic disorder, physical inactivity, pre-eclampsia, rheumatoid arthritis, coffee consumption, pregnancy, rubella, drugs, tobacco, alcohol, heart defect, previous surgery/injury, thyroid, sleep apnea, atrial fibrillation, heart history, infection, homocysteine level, pericardial cysts, marfan syndrome, syphilis, inflammation, clots, cancer, and electrolyte imbalance and finds out the risk of coronary artery disease, cardiomyopathy, congenital heart disease, heart attack, heart arrhythmia, peripheral artery disease, aortic disease, pericardial disease, deep vein thrombosis, heart valve disease, and heart failure. There are five main modules of the system, which are neutrosophication, knowledge base, inference engine, de-neutrosophication, and explainability. To demonstrate the complete working of our system, we design an algorithm and calculates its time complexity. We also present a new de-neutrosophication formula, and give comparison of our the results with existing methods

The 11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2020)

 Ensuring food security has become a challenge in Sub-Saharan Africa (SSA) due to combined effects of climate change, high population growth, and relying on rainfed farming. Governments are establishing shared irrigation infrastructure for smallholder farmers as part of the solutions for food security. However, the irrigated farms often failed to achieve the expected crop yield. This is partly due to lack of water management system in the irrigation infrastructure. In this work, IoT-based irrigation management system is proposed after investigating problems of irrigated farmlands in three SSA countries, Ethiopia, Kenya, and South Africa as case studies. Resource-efficient IoT architecture is developed that monitors soil, microclimate and water parameters and performs appropriate irrigation management. Indigenous farming and expert knowledge, regional weather information, crop and soil specific characteristics are also provided to the system for informed-decision making and efficient operation of the irrigation management system. In SSA, broadband connectivity and cloud services are either unavailable or expensive. To tackle these limitations, data processing, network management and irrigation decisions and communication to the farmers are carried out locally, without the involvement of any back-end servers. Furthermore, the use of green energy sources and resource-aware intelligent data analysis algorithm is studied. The intelligent data analysis helps to discover new knowledge that support further development of agricultural expert knowledge. The proposed IoT-based irrigation management system is expected to contribute towards long term and sustainable high crop yield with minimum resource consumption and impact to the biodiversity around the case farmlands.</p

Research Trends on Greenhouse Engineering Using a Science Mapping Approach

Horticultural protected cultivation has spread throughout the world as it has proven to be extremely effective. In recent years, the greenhouse engineering research field has become one of the main research topics within greenhouse farming. The main objectives of the current study were to identify the major research topics and their trends during the last four decades by analyzing the co-occurrence network of keywords associated with greenhouse engineering publications. A total of 3804 pertinent documents published, in 1981-2021, were analyzed and discussed. China, the United States, Spain, Italy and the Netherlands have been the most active countries with more than 36% of the relevant literature. The keyword cluster analysis suggested the presence of five principal research topics: energy management and storage; monitoring and control of greenhouse climate parameters; automation of greenhouse operations through the internet of things (IoT) and wireless sensor network (WSN) applications; greenhouse covering materials and microclimate optimization in relation to plant growth; structural and functional design for improving greenhouse stability, ventilation and microclimate. Recent research trends are focused on real-time monitoring and automatic control systems based on the IoT and WSN technologies, multi-objective optimization approaches for greenhouse climate control, efficient artificial lighting and sustainable greenhouse crop cultivation using renewable energy

An economic and environmental analysis of greenhouse tomato production in Norway using a model-based technique

The growing global population levels and the resulting increasing demands for food has put a lot of pressure on the food production systems and made the agricultural sector highly energy-intensive. The intensification in global food production has led to the need to adapt production systems according to the local climatic conditions, making food production possible in areas where it was di cult before and also making the production process environmentally sustainable. One way to adapt food production systems is through protected cultivation techniques, such as greenhouses, that enable controlled indoor climate, crop protection from extreme climate conditions, pests and diseases and the possibility to extend production seasons for certain crops. Yet these techniques a ect the investments, economic performance, used resources and have certain environmental consequences. Norway, for instance, is one such region in which one of the biggest challenges associated with protected cultivation systems is the issue of low availability of natural light and heat, especially during the cold winter months. Production in such regions requires high levels of energy, yet some of these regions also have significant availability of renewable energy resources. The challenge of low light and heat can be overcome by bringing about changes in the production techniques, including greenhouse design elements, production seasons and energy sources. However, this also in turn raises the issue of environmental impact of greenhouse vegetable production in high latitude regions and especially from the use of renewable energy that is present in significant amounts in many regions with considerable greenhouse vegetable production. While there exist several studies on the di erent aspects of greenhouse vegetable production in various regions, and their resulting environmental effects, works related to the use of renewable energy sources, especially in high latitude regions such as Norway are limited. Moreover, studies regarding the environmental impact of greenhouse production of vegetables often show that there is a trade-off between the economic performance and the environmental impact. Local climate and light variability call for regionally adapted greenhouse production techniques. Moreover, the impact of a certain greenhouse design on the economic performance may not always be correlated to the environmental impact. Thus, there is a need to evaluate the impact of various production strategies on the economic potential, resource use and the environment in instances where the traditional fossil fuel is supplemented and/or replaced by energy from renewable resources. In the present work, an attempt has been made to provide a broad picture of greenhouse tomato production at high latitude regions as a result of adapting production strategies in line with the local climates in Norway, with a particular emphasis on renewable energy sources in order to evaluate the environmental impact of locally produced tomatoes that are also economically profitable. The study has been divided into three stages. In the first part, an economic evaluation of seasonal (mid-March to mid-October) greenhouse tomato production in southestern, southwestern, central and northern Norway was performed. In the second part, an economic evaluation and energy use of extended season (from 20th January to 20th November) and year-round production of greenhouse tomatoes in the selected locations in Norway was performed. Sets of plausible design elements, greenhouse climate management, different artificial lighting strategies were assessed to evaluate the impact of the greenhouse design on the Net Financial Return (NFR), energy use and CO2 emissions of the production process. In the third part, a life cycle impact assessment was conducted for a selected number of designs from the first two stages that yielded high NFR or was associated with low energy use in order to assess whether the designs that performed well economically are also environmentally sustainable. The study found clear region-dependent differences in the NFR, its underlying elements, energy use and the resulting environmental impact of different greenhouse designs with differing energy-saving and internal climate control equipment. Our results show that economic profitability can be combined with a low environmental impact under certain regions and production techniques. It was found that Kise (southeastern) was the most favorable location for seasonal greenhouse tomato production in Norway, while Orre (southwestern) was the most favorable location in terms of the economic performance and environmental impact during the extended and year-round production seasons. Moreover, our results show that night energy screens, electric heat pumps and light sources had the most impacts of the elements that were investigated on the NFR and the resulting environmental impact across the three production seasons and need to be considered while constructing greenhouses for tomato production in regions having similar climate as that of Norway. The results of this study provide interesting insights on works related to the greenhouse vegetable production and energy resources in high latitude regions with considerable supplies of renewable energy. The findings can enable local producers across Norway to design greenhouses keeping in mind the local climate, the economic profitability and the environmental sustainability and can help policymakers in devising policies that encourage local growers to adapt production strategies aimed at increasing local production that is both economically profitable and environmentally sustainable

Efficiency analysis of alternative production systems in Kosovo - an ecosystem services approach

The efficiency estimation and the interpretation of its behavior are of extreme interest for primary producer in agriculture as well as for policy makers. In Kosovo one of the main objectives of Agriculture and Rural Development Plan 2007-2013 and 2014-2020 is to improve competitiveness and the efficiency of primary agricultural producers and to attain sustainable land use. Regardless of this, there was a lack of studies on farm efficiency estimation and the productivity changes of the agriculture sector in Kosovo. Therefore, the conducted study of this thesis focuses on estimation and the analysis of efficiency at farm level. More specifically, the study aimed estimation of technical, economic, and environmental efficiency of the farms oriented on tomato, grape and apple production. In addition, identification of the factors that extensively explain the variation of the efficiency scores among farms was sought. The study was based entirely on primary data, collected in three different stages. In the first stage, a survey using structured questionnaire was conducted with 120 farms which were distributed equally for each selected production system in the study. Farm efficiency scores were obtained using a Data Envelopment Analysis, which is a linear programming optimization technique that measures relative efficiency of a set of comparable units. In general, the efficiency scores for three different production systems were high, showing that there was little space for efficiency improvement. On average, tomato farms tend to be more technical efficient, followed by scale, revenue, and cost allocative efficiency. Farmers oriented in grape production were very scale efficient, followed by technical, revenue and cost allocative efficiency. Apple farms on average were performing relatively well in terms of technical efficiency which was the highest on average, followed by revenue efficiency and scale efficiency. Factors which were proved to be statistically important in explaining the variation of the efficiency scores among the farms were household size, farm size and number of cultivated crops, number of land plots, farmer´s education and experience in farming. In terms of the position in ranking between technical and environmental efficiency estimation, three different groups of farms were found. The group of farms which showed increase in ranking at environmental efficiency when compared to the technical one. Farms with no difference in ranking, and a group of farms showing a decrease in ranking at environmental efficiency compared to the technical efficiency. Farms which displayed an increase in ranking were mostly farms that improved or maintained good quality of soil at farm land and good level of agro-biodiversity provision. The second group of farms showed no difference in ranking, as they were fully efficient in technical and environmental efficiency estimation. The third group of farms which showed a decrease in ranking were those farms performing weakly in both technical and environmental efficiency. This group of farms were also having lower soil quality at farm land and lower agro-biodiversity when compared to the averages of total sample

Artificial Intelligence for detection and prevention of mold contamination in tomato processing

openIl presente elaborato si propone di analizzare l'uso dell'intelligenza artificiale attraverso il riconoscimento di immagini per rilevare la presenza di muffa nei pomodori durante il processo di essiccazione. La muffa nei pomodori rappresenta un rischio sia per la salute umana sia per l'industria alimentare, comportando, anche, una serie di problemi che vanno oltre l'aspetto estetico. Essa è causata principalmente da funghi che si diffondono rapidamente sulla superficie dei pomodori. Tale processo compromette così la qualità con la conseguente produzione di tossine che possono influire sulla salute umana. L'obiettivo sperimentale di questo lavoro è il problema dello spreco e della perdita di prodotto nell'industria alimentare. Quando i pomodori sono colpiti da muffe, infatti, diventano inadatti al consumo, con conseguente perdita di cibo. Lo spreco di pomodori a causa delle muffe rappresenta anche la perdita di preziose risorse, utili alla produzione, come terra, acqua, energia e tempo. Il proposito è testare, anche nella fase iniziale, la capacità di un algoritmo di rilevamento degli oggetti per identificare la muffa, e adottare misure preventive. L'analisi sperimentale ha previsto l'addestramento dell'algoritmo con un'ampia serie di foto, tra cui pomodori sani e rovinati di diversi tipi, forme e consistenze. Per etichettare le immagini e creare le epoche di addestramento è stato quindi utilizzato YOLOv7, l'algoritmo di rilevamento degli oggetti scelto, basato su reti neurali. Per valutare le prestazioni sono state utilizzate metriche di valutazione, tra cui “Precision” e “Recall”. L'ipotesi di applicazione dell'intelligenza artificiale in futuro sarà un grande potenziale per migliorare i processi di produzione alimentare, facilitando, così, l'identificazione delle muffe. Il rilevamento rapido delle muffe faciliterebbe la separazione tempestiva dei prodotti contaminati, riducendo così il rischio di diffusione delle tossine e preservando la qualità degli alimenti non contaminati. Questo approccio contribuirebbe a ridurre al minimo gli sprechi alimentari e le inefficienze delle risorse associate allo scarto di grandi quantità di prodotto. Inoltre, l'integrazione della computer vision nel contesto dell'HACCP (Hazard Analysis Critical Control Points) potrebbe migliorare i protocolli di sicurezza alimentare grazie a un rilevamento accurato e tempestivo. Questa tecnologia potrà offrire, dando priorità alla prevenzione, una promettente opportunità per migliorare la qualità, l'efficienza e la sostenibilità dei futuri processi di produzione alimentare.This study investigates the use of computer vision couples with artificial intelligence to detect mold in tomatoes during the drying process. Mold presence in tomatoes poses threats to human health and the food industry as it leads to several issues beyond appearance. It is primarily caused by fungi that spread rapidly over the tomato surface, compromising their quality, and potentially producing toxins that can harm human health. The experimental aim of this work focused on the issue of wastage and loss within the food industry. When tomatoes succumb to mold, they become unsuitable for consumption, resulting in a loss of food and resources. Considering that tomato production requires resources such as land, water, energy, and time, wasting tomatoes due to mold also represents a waste of these valuable resources. The goal was to evaluate the mold detection capabilities of an object detection algorithm, particularly in its early stages, to facilitate preventative measures. This experimental analysis entailed training the algorithm with an extensive array of images, encompassing a variety of healthy and spoiled tomatoes of different shapes, types, textures and drying stages. The chosen object detection algorithm, YOLOv7, is convolutional neural network-based and was utilized for image labeling and training epochs. Evaluation metrics, including precision and recall, were utilized to assess the algorithm's performance. The implementation of artificial intelligence in the future has significant potential for enhancing food production processes by streamlining mold identification. Prompt mold detection would expedite segregation of contaminated products, thus reducing the risk of toxin dissemination and preserving the quality of uncontaminated food. This approach could minimize food waste and resource inefficiencies linked to discarding significant product amounts. Furthermore, integrating computer vision in the HACCP (Hazard Analysis Critical Control Points) context could enhance food safety protocols via accurate and prompt detection. By prioritizing prevention, this technology offers a promising chance to optimize quality, efficiency, and sustainability of future food production processes

An Overview of Soil and Soilless Cultivation Techniques—Chances, Challenges and the Neglected Question of Sustainability

Resources such as fertile soil and clean water are already limited in many parts of the world. Additionally, the conventional use of arable land is becoming increasingly difficult, which is further exacerbated by climate change. Soilless cultivation systems do not only offer the opportunity to save water and cultivate without soil but also the chance to open up urban areas such as residential rooftops for food production in close proximity to consumers. In this review, applications of soilless farming systems are identified and compared to conventional agriculture. Furthermore, aspects of economic viability, sustainability and current developments are investigated. An insight into the most important soilless farming systems—hydroponics, aquaponics and vertical farming—is provided. The systems are then differentiated from each other and, as far as possible, evaluated in terms of their environmental impact and compared with conventional cultivation methods. Comparing published data analyzing the yield of hydroponic cultivation systems in comparison to soil-based cultivation methods enables a basic overview of the profitability of both methods and, thus, lays the foundation for future research and practical applications. The most important inert substrates for hydroponic applications are presented, and their degree of sustainability is compared in order to emphasize environmental impacts and affect substrate selections of future projects. Based on an assessment of the most important soilless cultivation systems, the challenges and developments of current techniques are highlighted and discussed