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With the advancement of deep learning methods it is imperative that autonomous systems will increasingly become intelligent with the inclusion of advanced machine learning algorithms to execute a variety of autonomous operations. One such…
Traditional mechanized chestnut harvesting is too costly for small producers, non-selective, and prone to damaging nuts. Accurate, reliable detection of chestnuts on the orchard floor is crucial for developing low-cost, vision-guided…
This study addresses the escalating threat of branched broomrape (Phelipanche ramosa) to California's tomato industry, which supplies over 90 percent of U.S. processing tomatoes. The parasite's largely underground life cycle makes early…
Rapid urbanization in megacities around the world, like Dhaka, has caused numerous transportation challenges that need to be addressed. Emerging technologies of deep learning and artificial intelligence can help us solve these problems to…
Recent advances in diffusion-based generative models have demonstrated significant potential in augmenting scarce datasets for object detection tasks. Nevertheless, most recent models rely on resource-intensive full fine-tuning of…
Electric scooters (e-scooters) have rapidly emerged as a popular mode of transportation in urban areas, yet they pose significant safety challenges. In the United States, the rise of e-scooters has been marked by a concerning increase in…
Image-based machine learning models can be used to make the sorting and grading of agricultural products more efficient. In many regions, implementing such systems can be difficult due to the lack of centralization and automation of…
The alfalfa crop is globally important as livestock feed, so highly efficient planting and harvesting could benefit many industries, especially as the global climate changes and traditional methods become less accurate. Recent work using…
Precision agriculture aims to use technological tools for the agro-food sector to increase productivity, cut labor costs, and reduce the use of resources. This work takes inspiration from bees vision to design a remote sensing system…
Accurate detection of nutrient deficiency in plant leaves is essential for precision agriculture, enabling early intervention in fertilization, disease, and stress management. This study presents a deep learning framework for leaf anomaly…
Apple orchards require timely disease detection, fruit quality assessment, and yield estimation, yet existing UAV-based systems address such tasks in isolation and often rely on costly multispectral sensors. This paper presents a unified,…
Manual pruning of radiata pine trees presents significant safety risks due to their substantial height and the challenging terrains in which they thrive. To address these risks, this research proposes the development of a drone-based…
Computer vision, particularly vehicle and pedestrian identification is critical to the evolution of autonomous driving, artificial intelligence, and video surveillance. Current traffic monitoring systems confront major difficulty in…
The expanding applications, utilized by more users, enhance hardware performance and further develop cloud systems for big data processing. This leads to numerous unexplored deep learning applications, especially in advanced computer vision…
Artificial intelligence has significantly advanced the automation of diagnostic processes, benefiting various fields including agriculture. This study introduces an AI-based system for the automatic diagnosis of urban street plants using…
This paper presents multi-vision-based localisation strategies for harvesting robots. Identifying picking points accurately is essential for robotic harvesting because insecure grasping can lead to economic loss through fruit damage and…
Modern robotics has enabled the advancement in yield estimation for precision agriculture. However, when applied to the olive industry, the high variation of olive colors and their similarity to the background leaf canopy presents a…
Crop yield prediction typically involves the utilization of either theory-driven process-based crop growth models, which have proven to be difficult to calibrate for local conditions, or data-driven machine learning methods, which are known…
Instance segmentation is an important image processing operation for agricultural automation, providing precise delineation of individual objects within images and enabling tasks such as selective harvesting and precision pruning. This…
Process refinement to consistently produce high-quality material over a large area of the grown crystal, enabling various applications from optics crystals to quantum detectors, has long been a goal for diamond growth. Machine learning…