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SKU/Artículo: AMZ-B0G94Y7FNL
ADVANCED MODELLING-BASED APPROACH FOR AGRICULTURAL REMOTE SENSING, PYTHON APPLICATIONS, IOT, CONTROLLED ENVIRONMENT PROTOCOLS, AND SOLAR ENERGY INTEGRATION (Page 1 to 615)
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1.66 kg
1.66 kg
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Nuevo
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Producto de:
Amazon
Amazon
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USA
USA
Sobre este producto
- This research proposes an advanced, integrated modeling framework that synergizes high-resolution agricultural remote sensing, scalable Python-based analytics, Internet of Things (IoT) networks, precise controlled environment protocols, and solar energy integration. The approach addresses critical challenges in resource optimization, yield prediction, and energy autonomy within agricultural operations. The methodology employs a multi-layered architecture. First, it utilizes satellite and UAV-derived remote sensing data, processed through custom Python pipelines for machine learning and computer vision tasks, to monitor crop health, soil conditions, and biomass at scale. Second, a dense IoT sensor network collects real-time in-situ data on microclimatic parameters, soil moisture, and plant physiology, feeding a central analytics engine. Third, these data streams inform dynamic control algorithms for managed environment agriculture (MEA) systems, enabling automated adjustments to irrigation, lighting, and nutrient delivery. A core innovation of this work is the seamless integration of a solar photovoltaic (PV) system model. This model not only aims to meet the energy demands of the IoT infrastructure and controlled environment systems but also optimizes energy use and storage based on both agricultural operational needs and solar availability. The entire framework is validated through a case study in a semi-controlled agro-photovoltaic setting, demonstrating significant improvements in water-use efficiency, a reduction in synthetic input dependency, and a pathway towards net-zero energy farm operations. Results indicate that the integrated model enhances decision-making accuracy by over 30% compared to siloed systems, reduces operational energy costs by up to 40% through solar integration and smart scheduling, and improves crop yield predictability. The study concludes that the proposed interdisciplinary framework is a robust and scalable blueprint for precision agriculture, contributing to the resilience, sustainability, and productivity of food production systems in the face of climate variability and resource constraints. Keywords: Precision Agriculture, Remote Sensing Analytics, Python Machine Learning, Agricultural IoT, Controlled Environment Agriculture, Agro-photovoltaics, Energy-Water-Food Nexus, Sustainable Farming, Digital Twins, Systems Modeling.
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