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The structure of the profitability module in ifarma (partially functioning within the PreFer module).
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Profitability is not given the necessary attention in contemporary precision agriculture. In this work, a new tool, namely ProFit, is developed within a pre-existing farm management system, namely ifarma, to assess the profitability of precision agriculture applications in extended crops, as most of the current solutions available on the market res...
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Porang or Amphophallus, locally known as porang or suweg, is a plant widely cultivated using traditional ecological knowledge in East Lombok Regency, West Nusa Tenggara Province. However, the increasing number of farmers in several villages in East Lombok Regency is still not followed by knowledge and skills in accordance with good agricultural pra...
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... The adaptation of new irrigation techniques is usually refused by farmers for many reasons, such as the complexity, the cost, and less expertise of this new technique and other elements related to the social and economic. The providers utilize a measure of profitability as the key whole-farm performance indicator [1]. ...
... • If x is greater than or equal to the largest observed value x(n), then 1 Averaging Probabilities: Compute the average probability for each factor or concept by summing the individual probabilities . and dividing by the total number of factors nf, denoted as . ...
The aim of this study is to construct and validate an expert system to predict the adaptation of irrigation technologies, water-saving strategies, and monitoring tools by small-scale farmers in Egypt. The research investigates the impact of economic, educational, environmental, and social factors on adaptation rates. To build the expert system, extensive knowledge was collected from experts, key concepts were identified, and production rules were created to generate tailored scenarios. These scenarios utilize the empirical cumulative distribution function (ECDF), selecting the scenario with the highest ECDF as the optimal irrigation technology. This approach ensures well-informed, data-driven decisions that are tailored to specific conditions. The expert system was evaluated under the conditions of ten small farms in Egypt. The results indicate that water cost and availability are significant drivers of technology adaptation. Specifically, subsurface drip irrigation (SDI) demonstrated an adaptation percentage of 75% at high water costs, with probabilities of 0.67 and 0.33, while soil mulching (SM) showed a 75% adaptation rate with a probability of 0.33 in high-cost scenarios. Conversely, when water availability was high, the adaptation percentage for all techniques was zero, but it reached 100% adaptation with a probability of 0.76 for SM and SDI and a probability of 1 for variable number of drippers (VND) and the use of sensors as monitoring tools during water shortages. Educational attainment and professional networks enhance the adaptation of advanced technologies and monitoring tools, emphasizing the role of knowledge and community engagement. Environmental conditions, including soil texture and salinity levels, directly affect the choice of irrigation methods and water-saving practices, highlighting the need for localized solutions. The source of irrigation water, whether groundwater or surface water, influences the preference for water-saving technologies. The study underscores the importance of tailored approaches to address the challenges and opportunities faced by small farmers in Egypt, promoting sustainable agriculture and efficient water management. The evaluation findings reveal that SDI is the most favored irrigation technology, with a probability of 0.55, followed by variable number of drippers (VND) at 0.38 and ultralow drip irrigation (ULDI) at 0.07 across various scenarios for small farmers. Regulated deficit irrigation (RDI) and SM are equally preferred water-saving strategies, each with a probability of 0.50. Sensors emerged as the preferred monitoring tool, boasting a high probability of 0.94. The analysis reveals the critical roles of economic pressures, educational levels, environmental conditions, and social networks in shaping the adaptation of sustainable agricultural practices.
... Produces final predictions, such as the expected crop performance. (Duarte-Carvajalino et al., 2021;Karydas et al., 2023). The network iteratively adapts its weights and biases to minimize the disparity between forecasts and actual performance values. ...
Modeling the intricate interactions between fruit trees, their environments, soils, and economic factors continues to be a significant challenge in agricultural research globally, requiring a multidisciplinary approach. Despite advances in agricultural technology and algorithms, significant knowledge gaps persist in understanding and modeling these interactions. This review explores basic concepts related to modeling for tropical fruit production. It explains modeling development from sensor technologies, image analysis, databases, and algorithms for decision support systems while considering climate changes or edaphoclimatic limitations. We report the current fruit modeling tendencies showing a significant increase in publications on these topics starting in 2021, driven by the need for sustainable solutions and access to large agricultural databases. This study emphasizes inherent challenges in tropical fruit modeling, such as fruit tree cycles, costly and time-consuming experimentation, and the lack of standardized data. These limitations are evident in tropical fruit, where few models have been reported or validated for cocoa, avocado, durian, dragonfruit, banana, mango, or passion fruit. This study analyzes the classification of the algorithms related to tropical fruit into three main categories: supervised, unsupervised, and reinforcement learning, each with specific applications in agricultural management optimization. Crop classification and yield prediction use supervised models like neural networks and decision trees. Unsupervised models, like K-Means clustering, allow pattern identification without prior labels, which is useful for area segmentation and pest detection. Automation of irrigation and fertilization systems employs reinforcement learning algorithms to maximize efficiency. This multidisciplinary review discusses recent approaches to 1) Modeling Soil health and plant-soil interaction, 2) Yield prediction in tropical fruit orchards, 3) Integrating meteorological models for enhanced tropical fruit production, and 4) Economics of tropical fruit business through modeling. Furthermore, this review illustrates the complexity and multidisciplinary research on models for tropical fruit and platforms using agricultural models. Further opportunities to advance fruit modeling frameworks are indicated, requiring technical knowledge about the fruit crop requirements with user-friendly platforms to collect and access fruit tree data and site-specific agroecological conditions.
... Precision agriculture, as a tool enabling farmers to enhance land management efficiency, exerts a significant and diverse influence on farm management practices. Global trends indicate a projected surge in the adoption of precision agriculture over the next four years, resulting in a doubling in the market value from USD 17.41 billion in 2022 to USD 34.1 billion by 2026 [2]. ...
The purpose of this paper is to conduct an empirical investigation of the theoretical and literature-based constructs related to the adoption of precision agriculture (PA) practices by young farmers. For this research, primary and secondary data are used. The sample includes 220 young farmers. Among the results of this research, farmers are aware of the positive effects of technology systems in agriculture. Also, young farmers seem to be familiar with precision agriculture and have already adopted some of its methods, but the high cost of investment prevents farmers from adopting such technology. Innovative technologies and production methods can help young farmers to be competitive in the worldwide market.
In this research, an innovative design is set up for regular provision of precision agriculture services, within the framework of fertilization consultancy. The central node of the chain is a geographic information system (GIS), while a 5x5m point grid is the information carrier. Decision-making is supported by machine learning algorithms developed with training of the collected big data. Potential data sources include soil samples, satellite data, yield maps, and agronomic information; while the produced maps are directed to a commercial farm management information system (FMIS) for visualization and storage and -in parallel- to variable-rate technologies (VRT) for the applications. In a large degree, the process chain is automated with Python programming language. The service has been tested exhaustively under true field conditions before is has entered the market.
Sustainable agriculture aims to increase agricultural productivity while minimising negative environmental impacts [...]
Precision agriculture has revolutionized modern farming practices by integrating cutting-edge technologies such as sensors, drones, and data analytics to optimize crop management. While these advancements offer unprecedented benefits in terms of yield optimization and resource efficiency, they also raise significant concerns regarding the security and privacy of sensitive agricultural data. This research paper delves into the intricate landscape of precision agriculture security and privacy, aiming to identify potential threats, vulnerabilities, and the corresponding measures necessary for safeguarding farmers' data and ensuring the sustainability of precision farming practices. The study employs a multidisciplinary approach, combining insights from computer science, agriculture, and privacy studies to provide a comprehensive overview. Through an analysis of existing security frameworks and privacy regulations, this paper proposes recommendations for the development and implementation of robust security protocols and privacy-enhancing technologies tailored to the unique challenges of precision agriculture. The findings of this research contribute to the ongoing dialogue on responsible technology adoption in agriculture, emphasizing the importance of striking a balance between innovation and the protection of farmers' sensitive information in the era of precision farming.
Precision agriculture has revolutionized modern farming practices by integrating cutting-edge technologies such as sensors, drones, and data analytics to optimize crop management. While these advancements offer unprecedented benefits in terms of yield optimization and resource efficiency, they also raise significant concerns regarding the security and privacy of sensitive agricultural data. This research paper delves into the intricate landscape of precision agriculture security and privacy, aiming to identify potential threats, vulnerabilities, and the corresponding measures necessary for safeguarding farmers' data and ensuring the sustainability of precision farming practices. The study employs a multidisciplinary approach, combining insights from computer science, agriculture, and privacy studies to provide a comprehensive overview. Through an analysis of existing security frameworks and privacy regulations, this paper proposes recommendations for the development and implementation of robust security protocols and privacy-enhancing technologies tailored to the unique challenges of precision agriculture. The findings of this research contribute to the ongoing dialogue on responsible technology adoption in agriculture, emphasizing the importance of striking a balance between innovation and the protection of farmers' sensitive information in the era of precision farming.
