Proceedings
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| Filter results4 paper(s) found. |
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1. Sectioning And Assessment Remote Images For Precision Agriculture: The Case Of Orobanche Crenate In Pea CropThe software SARI® has been developed to implement precision agriculture strategies through remote sensing imagery. It is written in IDL® and works as an add-on of ENVI®. It has been designed to divide remotely sensed imagery into “micro-images”, each corresponding to a small area (“micro-plot”), and to determine the quantitative agronomic and/or environmental biotic (i.e. weeds, pathogens) and/or non-biotic (i.e. nutrient levels) indicator/s... L. Garcia-torres, D. Gomez-candon, J.J. Caballero-novella, M. Gomez-casero, J.M. Pe, M. Jurado-exp, F. Lopez-granados, I. Castillejo-gonz, A. Garc |
2. Management Of Remote Imagery For Precision AgricultureSatellite and airborne remotely sensed images cover large areas, which normally include dozens of agricultural plots. Agricultural operations such as sowing, fertilization, and pesticide applications are designed for the whole plot area, i.e. 5 to 20 ha, or through precision agriculture. This takes into account the spatial variability of biotic and of abiotic factors and uses diverse technologies to apply inputs at variable rates, fitted to the needs of each small defined area, i.e. 25 to 200... L. Garcia-torres, D. Gomez-candon, J.J. Caballero-novella, J.M. Pe, M. Jurado-exp, I. Castillejo-gonz, A. Garc, F. Lopez-granados, L. Prassack |
3. Performance Study of Triboelectric Nanogenerator with Laser-induced Graphene ElectrodesAs wearable electronics increasingly demand a continuous power supply, conventional batteries—requiring frequent recharging or replacement—pose both user inconvenience and environmental risks. This study develops a wristwatch‐ shaped triboelectric nanogenerator that employs solid‐ state semiconductor laser‐ induced graphene electrodes patterned directly onto a polyimide (PI) film and utilizes an independent sliding interface to harvest 1 to 3 Hz low‐frequency... C. Wu |
4. Application of Deep Learning for Symptom Detection and Localization in Phalaenopsis PlantletsPhalaenopsis plantlets in dense greenhouses are vulnerable to diseases like soft rot, which spreads rapidly. This study compares YOLOv11 with enhanced architectures (FasterNet, MambaVision) for symptom detection and localization. Single- and multi-model strategies were evaluated for disease recognition, plant segmentation, and keypoint localization, enabling robotic removal and efficient automated disease management. ... Y. Huang |