Related papers: AI driven shadow model detection in agropv farms
Solar photovoltaic (PV) farms represent a major source of global renewable energy generation, yet their true operational efficiency often remains unknown at scale. In this paper, we present a comprehensive, data-driven framework for…
Agrivoltaics (APV) is the dual use of land by combining agricultural crop production and photovoltaic (PV) systems. In this work, we have analyzed three different APV configurations: static with optimal tilt, vertically-mounted bifacial,…
Agrivoltaic systems are becoming more popular as a critical technology for attaining several sustainable development goals such as affordable and clean energy, zero hunger, clean water and sanitation, and climate action. However,…
As global challenges of population growth, climate change, and resource scarcity intensify, the agricultural landscape is at a critical juncture. Sustainable vertical farming emerges as a transformative solution to address these challenges…
Agrivoltaics (AV) is a dual land-use approach to collocate solar energy generation with agriculture for preserving the terrestrial ecosystem and enabling food-energy-water synergies. Here, we present a systematic approach to model the…
The success of deep learning in visual recognition tasks has driven advancements in multiple fields of research. Particularly, increasing attention has been drawn towards its application in agriculture. Nevertheless, while visual pattern…
The agricultural sector is facing mounting demands to enhance energy efficiency within farm enterprises, concurrent with a steady escalation in electricity costs. This paper focuses on modelling the adoption rate of photovoltaic (PV) energy…
The increasing worldwide population leads to a constant increase in energy and food demand. These increasing demands have led to fierce land-use conflicts as we need agricultural land for food production while striving towards renewable…
The intermittent nature of photovoltaic (PV) solar energy, driven by variable weather, leads to power losses of 10-70% and an average energy production decrease of 25%. Accurate loss characterization and fault detection are crucial for…
Solar photovoltaic (PV) modules are prone to damage during manufacturing, installation and operation which reduces their power conversion efficiency. This diminishes their positive environmental impact over the lifecycle. Continuous…
Agrivoltaics (AV) is an emerging technology having symbiotic benefits for food-energy-water needs of the growing world population and an inherent resilience against climate vulnerabilities. An agrivoltaic system must optimize…
Advances in AI and Robotics have accelerated significant initiatives in agriculture, particularly in the areas of robot navigation and 3D digital twin creation. A significant bottleneck impeding this progress is the critical lack of…
Solar Photovoltaic (PV) is increasingly being used to address the global concern of energy security. However, hot spot and snail trails in PV modules caused mostly by crakes reduce their efficiency and power capacity. This article presents…
Shadows significantly hinder computer vision tasks in outdoor environments, particularly in field robotics, where varying lighting conditions complicate object detection and localisation. We present FieldNet, a novel deep learning framework…
We consider the problem of automatically detecting small-scale solar photovoltaic arrays for behind-the-meter energy resource assessment in high resolution aerial imagery. Such algorithms offer a faster and more cost-effective solution to…
Over the past decade, unprecedented progress in the development of neural networks influenced dozens of different industries, including weed recognition in the agro-industrial sector. The use of neural networks in agro-industrial activity…
Unmanned Aerial vehicles (UAV) are a promising technology for smart farming related applications. Aerial monitoring of agriculture farms with UAV enables key decision-making pertaining to crop monitoring. Advancements in deep learning…
This article investigates the use of Deep Q-Networks (DQNs) to optimize decision-making for photovoltaic (PV) systems installations in the agriculture sector. The study develops a DQN framework to assist agricultural investors in making…
Unmanned Ground Vehicles (UGVs) are emerging as a crucial tool in the world of precision agriculture. The combination of UGVs with machine learning allows us to find solutions for a range of complex agricultural problems. This research…
In agricultural image analysis, optimal model performance is keenly pursued for better fulfilling visual recognition tasks (e.g., image classification, segmentation, object detection and localization), in the presence of challenges with…