Optimizing Onion Crop Management: A Smart Agriculture Framework with IoT Sensors and Cloud Technology

Smart agriculture, fueled by the integration of Internet of Things (IoT) and cloud technology, has revolutionized modern farming practices. In this study, we propose a step-by-step framework for optimizing onion crop management using IoT sensors and cloud-based solutions. By deploying various IoT sensors, including soil moisture, temperature, humidity, and aerial drones, essential data about the onion crops is collected and transmitted to a central data hub. Optional edge computing devices enable real-time data processing, minimizing latency and bandwidth usage.The collected data is aggregated and stored securely on a cloud platform, which facilitates advanced data analysis and insights. Utilizing machine learning algorithms, the cloud platform can provide valuable information about the onion's growth patterns, health status, and growth trajectory. Farmers can easily access this information through a user-friendly dashboard, accessible via web or mobile applications.Automated alerts and notifications enable timely intervention, notifying farmers about any deviations from optimal conditions, such as low moisture levels or pest infestations. The system's predictive capabilities allow for precision irrigation and nutrient management, optimizing resource usage and improving crop health.The accumulated historical data offers a wealth of information, enabling the identification of trends and the prediction of growth patterns for future planting seasons. Throughout this process, data security and privacy measures are prioritized, with encrypted data transmission and storage to protect farmers' sensitive information.The integration of IoT and cloud technology provides an efficient and effective solution for monitoring onion crop growth. The proposed framework offers farmers valuable insights, improves productivity, and promotes sustainable agricultural practices

Exploring the adoption of precision agriculture for irrigation in the context of agriculture 4.0: The key role of internet of things

In recent years, the concept of Agriculture 4.0 has emerged as an evolution of precision agriculture (PA) through the diffusion of the Internet of things (IoT). There is a perception that the PA adoption is occurring at a slower pace than expected. Little research has been carried out about Agriculture 4.0, as well as to farmer behavior and operations management. This work explores what drives the adoption of PA in the Agriculture 4.0 context, focusing on farmer behavior and operations management. As a result of a multimethod approach, the factors explaining the PA adoption in the Agriculture 4.0 context and a model of irrigation operations management are proposed. Six simulation scenarios are performed to study the relationships among the factors involved in irrigation planning. Empirical findings contribute to a better understanding of what Agriculture 4.0 is and to expand the possibilities of IoT in the PA domain. This work also contributes to the discussion on Agriculture 4.0, thanks to multidisciplinary research bringing together the different perspectives of PA, IoT and operations management. Moreover, this research highlights the key role of IoT, considering the farmer’s possible choice to adopt several IoT sensing technologies for data collection

Agricultural mitigation and adaptation to climate change in Yolo County, CA

This place‐based case study in an agricultural county in California’s Central Valley focused on the period of 2010–2050, and dealt with biophysical and socioeconomic issues related to both mitigation of greenhouse gas (GHG) emissions and to adaptation to an uncertain climate. In the past 100 years, changes in crop acreage has been more related to crop price and availability of irrigation water than to growing degree days during summer, and in fact, summer temperatures have increased less than winter temperatures. Econometric analysis indicated that warmer winters, as projected by Geophysical Fluid Dynamics Laboratory‐Bias Corrected Constructed Analog during 2035–2050, could result in less wheat acreage, more alfalfa and tomato acreage, and slight effects on tree and vine crops. The Water Evaluation and Planning (WEAP) model showed that these econometric projections did not reduce irrigation demand under either the B1 or A2 scenarios, but a diverse, water‐efficient cropping pattern combined with improved irrigation technology reduced demand to 12 percent below the historic mean. Collaboration during development of Yolo County’s Climate Action Plan showed that nitrous oxide (mainly from nitrogen fertilizers) was the main source (≅40 percent) of agricultural emissions. Emissions from cropland and rangeland were several orders of magnitude lower than urbanized land per unit area. A survey distributed to 570 farmers and ranchers achieved a 34 percent response rate. Farmers concerned about climate change were more likely to implement water conservation practices, and adopt voluntary GHG mitigation practices. Use of the urban growth model (UPlan) showed that channeling much or all future urban development into existing urban areas will increase ecosystem services by preserving agricultural land and open space, immensely reducing the Yolo County’s GHG emissions, and greatly enhancing agricultural sustainability

Decision Support Tool for the Optimal Sizing of Solar Irrigation Systems

Solar photovoltaic (PV) irrigation is increasingly used in agriculture, driven by its low operation cost and virtually zero emissions, providing electricity access in rural areas. However, the high investment cost requires an optimal design. The objective of this work was to develop a user-friendly tool to optimally size a PV generator that satisfies crop irrigation needs under local constrictions. The ODSIS (Optimal Design of Solar Irrigation System) tool, was organized in three calculation modules, preceded by two complements, which determine the daily crop irrigation needs and power demand of the pumping system. Then, the first module sizes the PV plant, considering a multiplication factor, and provides the PV production potential throughout each day of the season. The second and third modules evaluate the total investment cost and equivalent greenhouse gas emissions avoided by comparison with traditional energy sources. This tool was applied to a case study in Senegal for which a multiplication factor of 1.4 was obtained for the optimal PV plant size. Between 22% to 64% of the investment cost corresponded to the PV pumping system, depending on the irrigation technique. The use of PV energy in the case study would represent an annual economic saving for the farmer after 5 to 8 years of payback period, avoiding the emission of between 29.8 and 37.9 tCO2eq/year for the case study area

SMART - IWRM - Sustainable Management of Available Water Resources with Innovative Technologies - Integrated Water Resources Management in the Lower Jordan Rift Valley : Final Report Phase II (KIT Scientific Reports ; 7698)

SMART was a multi-lateral research project with partners from Germany, Israel, Jordan and the Palestinian Territories. The overall goal was to develop a transferable approach for Integrated Water Resources Management (IWRM) in the water shortage region of the Lower Jordan Valley. The innovative aspect addressed all available water resources: groundwater and surface waters, but also wastewater, brackish water and flood water that need to be treated for use

Integrated Water Resources Management Karlsruhe 2010 : IWRM, International Conference, 24 - 25 November 2010 conference proceedings

In dieser Arbeit werden dual-orthogonal, linear polarisierte Antennen für die UWB-Technik konzipiert. Das Prinzip zur Realisierung der Strahler wird vorgestellt, theoretisch und simulativ untersucht, sowie messtechnisch verifiziert. Danach werden Konzepte zur Miniaturisierung der Strahler dargelegt, die anschließend zum Aufbau von Antennengruppen verwendet werden. Die Vorteile der entwickelten Antennen werden praktisch anhand des bildgebenden Radars und des Monopuls-Radars gezeigt

Vertical Farming- is a Sustainable and Efficient Method for Food Production

The goal of this thesis is to evaluate the concept of vertical farming methods as sustainable for effective food production and analyse the case of AeroFarms, a leading vertical farming company. In this research, vertical farming's future as a viable food production strategy is investigated. This thesis aims to examine the pros and cons of vertical farming as a possible answer to critical agricultural and environmental problems. This study gives insights into the broader implications of vertical farming by investigating AeroFarms' innovative methods and their influence on sustainability, resource efficiency, and local food production. The study aims to understand the effects of vertical farming on sustainable food production, efficiency, and profitability. To analyze the benefits, limitations, and opportunities connected with vertical farming and its implementation in the AeroFarms business model, the analysis employs a combination of literature research, value chain analysis, business canvas model, and SWOT analysis. Finally, this study looks at the environmental impact of each agricultural method, taking into consideration factors like pesticide use, soil deterioration, and carbon emissions. It explores how the regulated environment and pesticide-free methods of Aero Farms lead to a lower environmental effect when compared to traditional farming. The study also assesses the potential benefits of vertical farming in terms of water conservation, soil deterioration reduction, and greenhouse gas emissions reduction