Evaluation of Weed Infestations in Row Crops Using Aerial RGB Imaging and Deep Learning

Agriculture, Год журнала: 2025, Номер 15(4), С. 418 - 418

Опубликована: Фев. 16, 2025

One of the important factors negatively affecting yield row crops is weed infestations. Using non-contact detection methods allows for a rapid assessment infestations’ extent and management decisions practical control. This study aims to develop demonstrate methodology early evaluation infestations in maize using UAV-based RGB imaging pixel-based deep learning classification. An experimental was conducted determine on two tillage technologies, plowing subsoiling, tailored specific soil climatic conditions Southern Dobrudja. Based an with DeepLabV3 classification algorithm, it found that ResNet-34-backed model ensures highest performance compared different versions ResNet, DenseNet, VGG backbones. The achieved reached precision, recall, F1 score, Kappa, respectively, 0.986, 0.957. After applying field investigated higher level infestation observed subsoil deepening areas, where 4.6% area infested, 0.97% treatment. work contributes novel insights into during critical growth stages maize, providing robust framework optimizing control strategies this region.

Язык: Английский

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Detección Automática De Palmas Ceroxylon Mediante Aprendizaje Profundo En Un Área Protegida Del Amazonas (No Perú)

Опубликована: Янв. 1, 2025

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Three-Dimensional Crack Quantification Using Fused LiDAR Data and Optical Images

Journal of Computing in Civil Engineering, Год журнала: 2025, Номер 39(4)

Опубликована: Апрель 25, 2025

Язык: Английский

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Deploying UAV-Based Detection of Bridge Structural Deterioration with Pilgrimage Walk Optimization-Lite for Computer Vision

Case Studies in Construction Materials, Год журнала: 2024, Номер 21, С. e04048 - e04048

Опубликована: Ноя. 29, 2024

Язык: Английский

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