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| Filter results6 paper(s) found. |
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1. Tomato Development Monitoring In An Open Field, Using A Two-Camera Acquisition SystemIntroduction Optimal harvesting date and predicted yield are valuable information when farming open field tomatoes, making harvest planning and work at the processing plant much easier. Monitoring growth during tomato?s early stages is also interesting to assess plant stress or abnormal development. Yet, it is very challenging due to the colours and the high degree of occlusion... F. Rossant, I. Bloch, J. Orensanz, D. Boisgontier, U. Verma, M. Lagarrigue |
2. Sound Based Detection Of Moths In Open FieldsIntroduction Open field farming of tomatoes suffers from the presence of harmful moths whose larvas are devastating. Detecting automatically the presence of moths allows regulating the use of pesticides, according to the actual population present in the field. Up to now, sex pheromone traps have been used, the number of captured insects giving some indication about the population. However, proper inspection of the traps is... F. Rossant, J. Orensanz, D. Boisgontier, N. Bouhlel, M. Lagarrigue |
3. AI-based Pollinator Using CoreXY RobotThe declining populations of natural pollinators pose a significant ecological challenge, often attributed to the adverse effects of pesticides and intensive farming practices. To address the critical issue of pollination in the face of diminishing natural pollinators, we are pioneering an AI-based pollinator that utilizes a CoreXY pollination system. This solution aims to augment pollination efforts in agriculture, increasing yields and crop quality while mitigating the adverse impacts of pesticide... H. Kulhandjian, M. Kulhandjian, D. Rocha, B. Bennett |
4. AI-based Precision Weed Detection and EliminationWeeds are a significant challenge in agriculture, competing with crops for resources and reducing yields. Addressing this issue requires efficient and sustainable weed elimination systems. This paper presents a comprehensive overview of recent advancements in weed elimination system development, focusing on innovative technologies and methodologies. Specifically, it details the development and integration of a weed detection and elimination system based on the CoreXY architecture, implemented... H. Kulhandjian, M. Kulhandjian, D. Rocha, B. Bennett |
5. 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 |
6. 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 |