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Automating the detection of fruits and vegetables using computer vision is essential for modernizing agriculture, improving efficiency, ensuring food quality, and contributing to technologically advanced and sustainable farming practices.…
The LifeCLEF plant identification challenge aims at evaluating plant identification methods and systems at a very large scale, close to the conditions of a real-world biodiversity monitoring scenario. The 2016-th edition was actually…
Monitoring plant health increasingly relies on leaf-mounted sensors that provide real-time physiological data, yet efficiently locating and sampling these sensors in complex agricultural environments remains a major challenge. We present an…
Biotic stress consists of damage to plants through other living organisms. Efficient control of biotic agents such as pests and pathogens (viruses, fungi, bacteria, etc.) is closely related to the concept of agricultural sustainability.…
Contemporary robots in precision agriculture focus primarily on automated harvesting or remote sensing to monitor crop health. Comparatively less work has been performed with respect to collecting physical leaf samples in the field and…
In this research, we develop machine learning models to predict future sensor readings of a waste-to-fuel plant, which would enable proactive control of the plant's operations. We developed models that predict sensor readings for 30 and 60…
Plant traits such as leaf carbon content and leaf mass are essential variables in the study of biodiversity and climate change. However, conventional field sampling cannot feasibly cover trait variation at ecologically meaningful spatial…
Monitoring the responses of plants to environmental changes is essential for plant biodiversity research. This, however, is currently still being done manually by botanists in the field. This work is very laborious, and the data obtained…
Automatic classification of pests and plants (both healthy and diseased) is of paramount importance in agriculture to improve yield. Conventional deep learning models based on convolutional neural networks require thousands of labeled…
Rapid environmental change and advances in data-driven analysis highlight the need not only to use computational tools, but also to foster understanding of the natural world and inspire creativity. Photosynthesis, the process that fuels…
Not only does cold climate pose a problem for outdoor plants during winter in the northern hemisphere, but for indoor plants as well: low sunlight, low humidity, and simultaneous cold breezes from windows and heat from radiators all cause…
Salinity stress poses a significant challenge to global agriculture, necessitating efficient and scalable approaches for early detection and management. This review examines advanced optical spectroscopic imaging techniques, including…
Air quality monitoring in automotive workshops is crucial for occupational health and regulatory compliance. This study presents the development of an environmental monitoring system based on Internet of Things (IoT) and Artificial…
Accurate monitoring of the hydrogen concentration is critical for optimizing fuel cell performance, minimizing purge losses, and reducing long-term degradation. Conventional hydrogen sensors often rely on catalytic materials and face…
Availability of an explainable deep learning model that can be applied to practical real world scenarios and in turn, can consistently, rapidly and accurately identify specific and minute traits in applicable fields of biological sciences,…
Accompanying rapid industrialization, humans are suffering from serious air pollution problems. The demand for air quality prediction is becoming more and more important to the government's policy-making and people's daily life. In this…
With rich annotation information, object detection-based automated plant disease diagnosis systems (e.g., YOLO-based systems) often provide advantages over classification-based systems (e.g., EfficientNet-based), such as the ability to…
Recently, the EAGL-I system was developed to rapidly create massive labeled datasets of plants intended to be commonly used by farmers and researchers to create AI-driven solutions in agriculture. As a result, a publicly available plant…
Plant diseases pose significant challenges to farmers and the agricultural sector at large. However, early detection of plant diseases is crucial to mitigating their effects and preventing widespread damage, as outbreaks can severely impact…
As global warming intensifies, increased attention is being paid to monitoring fugitive methane emissions and detecting gas plumes from landfills. We have divided methane emission monitoring into three subtasks: methane concentration…