Proceedings
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| Filter results3 paper(s) found. |
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1. Automated Southern Leaf Blight Severity Grading of Corn Leaves in RGB Field ImageryPlant stress phenotyping research has progressively addressed approaches for stress quantification. Deep learning techniques provide a means to develop objective and automated methods for identifying abiotic and biotic stress experienced in an uncontrolled environment by plants comparable to the traditional visual assessment conducted by an expert rater. This work demonstrates a computational pipeline capable of estimating the disease severity caused by southern corn leaf blight in images of field-grown... C. Ottley, M. Kudenov, P. Balint-kurti, R. Dean, C. Williams |
2. Utilizing Hyperspectral Field Imagery for Accurate Southern Leaf Blight Severity Grading in CornCrop disease detection using traditional scouting and visual inspection approaches can be laborious and time-consuming. Timely detection of disease and its severity over large spatial regions is critical for minimizing significant yield losses. Hyperspectral imagery has been demonstrated as a useful tool for a broad assessment of crop health. The use of spectral bands from hyperspectral data to predict disease severity and progression has been shown to have the capability of enhancing early... G. Vincent, M. Kudenov, P. Balint-kurti, R. Dean, C.M. Williams |
3. Development of a Drone-mounted Device for Aerial Application of Mating Disruption Agents in AgricultureIn recent years, drones have been increasingly adopted to reduce workforce and improve the efficiency of pest control in agriculture. However, most drones are optimized for spraying low-viscosity liquid pesticides and thus have limitations in stably applying high-viscosity liquid or solid formulations. In particular, the mating disruption agent (MDA) used in this study, which contains pheromones, must be attached to the crown to maximize pheromone diffusion. It is necessary to develop a technology... S. Song |