Related papers: Machine Vision System for 3D Plant Phenotyping
In forest industry, mechanical site preparation by mounding is widely used prior to planting operations. One of the main problems when planning planting operations is the difficulty in estimating the number of mounds present on a planting…
Plant diseases significantly threaten global food security by reducing crop yields and undermining agricultural sustainability. AI-driven automated classification has emerged as a promising solution, with deep learning models demonstrating…
Agriculture plays an important role in the food and economy of Bangladesh. The rapid growth of population over the years also has increased the demand for food production. One of the major reasons behind low crop production is numerous…
Our paper introduces a robust framework for the automated identification of diseases in plant leaf images. The framework incorporates several key stages to enhance disease recognition accuracy. In the pre-processing phase, a thumbnail…
We present feature finding and tracking algorithms in 3D in living cells, and demonstrate their utility to measure metrics important in cell biological processes. We developed a computational imaging hybrid approach that combines automated…
The paper describes an open source computer vision-based hardware structure and software algorithm, which analyzes layer-wise the 3-D printing processes, tracks printing errors, and generates appropriate printer actions to improve…
Monitoring plant health is crucial for maintaining agricultural productivity and food safety. Disruptions in the plant's normal state, caused by diseases, often interfere with essential plant activities, and timely detection of these…
Plants need regular and the appropriate amount of watering to thrive and survive. While agricultural robots exist that can spray water on plants and crops such as the , they are expensive and have limited mobility and/or functionality. We…
Performing tasks in agriculture, such as fruit monitoring or harvesting, requires perceiving the objects' spatial position. RGB-D cameras are limited under open-field environments due to lightning interferences. So, in this study, we state…
Street-level imagery holds a significant potential to scale-up in-situ data collection. This is enabled by combining the use of cheap high quality cameras with recent advances in deep learning compute solutions to derive relevant thematic…
Semantic reconstruction of agricultural scenes plays a vital role in tasks such as phenotyping and yield estimation. However, traditional approaches that rely on manual scanning or fixed camera setups remain a major bottleneck in this…
Monitoring the health and vigor of grasslands is vital for informing management decisions to optimize rotational grazing in agriculture applications. To take advantage of forage resources and improve land productivity, we require knowledge…
This paper presents a novel metric to evaluate the robustness of deep learning based semantic segmentation approaches for crop row detection under different field conditions encountered by a field robot. A dataset with ten main categories…
High efficiency in precision farming depends on accurate tools to perform weed detection and mapping of crops. This allows for precise removal of harmful weeds with a lower amount of pesticides, as well as increase of the harvest's yield by…
In many agricultural applications one wants to characterize physical properties of plants and use the measurements to predict, for example biomass and environmental influence. This process is known as phenotyping. Traditional collection of…
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…
In precision agriculture, one of the most important tasks when exploring crop production is identifying individual plant components. There are several attempts to accomplish this task by the use of traditional 2D imaging, 3D…
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…
We present an approach that learns to synthesize high-quality, novel views of 3D objects or scenes, while providing fine-grained and precise control over the 6-DOF viewpoint. The approach is self-supervised and only requires 2D images and…
Reliable and automated 3D plant shoot segmentation is a core prerequisite for the extraction of plant phenotypic traits at the organ level. Combining deep learning and point clouds can provide effective ways to address the challenge.…