Applications of deep learning in precision weed management: A review

Computers and Electronics in Agriculture, Год журнала: 2023, Номер 206, С. 107698 - 107698

Опубликована: Фев. 10, 2023

Deep Learning (DL) has been described as one of the key subfields Artificial Intelligence (AI) that is transforming weed detection for site-specific management (SSWM). In last demi-decade, DL techniques have integrated with ground well aerial-based technologies to identify weeds in still image context and real-time setting. After observing current research trend DL-based detection, are advancing by assisting precision weeding make smart decisions. Therefore, objective this paper was present a systematic review study involves available SSWM. To accomplish study, comprehensive literature survey performed consists 60 closest technical papers on detection. The findings summarized follows, a) transfer learning approach widely adopted technique address majority work, b) less focus navigated towards custom designed neural networks task, c) based pretrained models deployed test dataset, no specific model can be attributed achieved high accuracy multiple field images pertaining several studies, d) inferencing resource-constrained edge devices limited number dataset lagging, e) different versions YOLO (mostly v3) detecting scenario, f) SegNet U-Net semantic segmentation task multispectral aerial imagery, g) open-source acquired using drones, h) lack exploring optimization generalization identification images, i) ways design consume training hours, low-power consumption parameters during or inferencing, j) slow-moving advances optimizing domain adaptation approach. conclusion, will help researchers, experts, scientists, farmers, technology extension specialist gain updates area

Язык: Английский

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Lightweight object detection algorithm for robots with improved YOLOv5

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 123, С. 106217 - 106217

Опубликована: Апрель 5, 2023

Язык: Английский

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Deformable convolution and coordinate attention for fast cattle detection

Computers and Electronics in Agriculture, Год журнала: 2023, Номер 211, С. 108006 - 108006

Опубликована: Июль 4, 2023

Язык: Английский

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Deep learning based weed detection and target spraying robot system at seedling stage of cotton field

Computers and Electronics in Agriculture, Год журнала: 2023, Номер 214, С. 108317 - 108317

Опубликована: Окт. 19, 2023

Язык: Английский

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Comparing YOLOv8 and Mask R-CNN for instance segmentation in complex orchard environments

Artificial Intelligence in Agriculture, Год журнала: 2024, Номер 13, С. 84 - 99

Опубликована: Июль 17, 2024

Instance segmentation, an important image processing operation for automation in agriculture, is used to precisely delineate individual objects of interest within images, which provides foundational information various automated or robotic tasks such as selective harvesting and precision pruning. This study compares the one-stage YOLOv8 two-stage Mask R-CNN machine learning models instance segmentation under varying orchard conditions across two datasets. Dataset 1, collected dormant season, includes images apple trees, were train multi-object delineating tree branches trunks. 2, early growing canopies with green foliage immature (green) apples (also called fruitlet), single-object only apples. The results showed that performed better than R-CNN, achieving good near-perfect recall both datasets at a confidence threshold 0.5. Specifically, achieved 0.90 0.95 all classes. In comparison, demonstrated 0.81 same dataset. With 0.93 0.97. this single-class scenario, 0.85 0.88. Additionally, inference times 10.9 ms multi-class (Dataset 1) 7.8 2), compared 15.6 12.8 by R-CNN's, respectively. These findings show YOLOv8's superior accuracy efficiency applications models, specifically Mask-R-CNN, suggests its suitability developing smart operations, particularly when real-time are necessary cases fruit thinning.

Язык: Английский

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Smart solutions for capsicum Harvesting: Unleashing the power of YOLO for Detection, Segmentation, growth stage Classification, Counting, and real-time mobile identification

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 219, С. 108832 - 108832

Опубликована: Март 15, 2024

Язык: Английский

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Comparative performance of YOLOv8, YOLOv9, YOLOv10, YOLOv11 and Faster R-CNN models for detection of multiple weed species

Smart Agricultural Technology, Год журнала: 2024, Номер 9, С. 100648 - 100648

Опубликована: Ноя. 9, 2024

Язык: Английский

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YOLO-WDNet: A lightweight and accurate model for weeds detection in cotton field

Computers and Electronics in Agriculture, Год журнала: 2024, Номер 225, С. 109317 - 109317

Опубликована: Авг. 10, 2024

Язык: Английский

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PG-YOLO: An efficient detection algorithm for pomegranate before fruit thinning

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 134, С. 108700 - 108700

Опубликована: Май 30, 2024

Язык: Английский

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Detection of White Leaf Disease in Sugarcane Crops Using UAV-Derived RGB Imagery with Existing Deep Learning Models

Remote Sensing, Год журнала: 2022, Номер 14(23), С. 6137 - 6137

Опубликована: Дек. 3, 2022

White leaf disease (WLD) is an economically significant in the sugarcane industry. This work applied remote sensing techniques based on unmanned aerial vehicles (UAVs) and deep learning (DL) to detect WLD fields at Gal-Oya Plantation, Sri Lanka. The established methodology consists of UAV red, green, blue (RGB) image acquisition, pre-processing dataset, labelling, DL model tuning, prediction. study evaluated performance existing models such as YOLOv5, YOLOR, DETR, Faster R-CNN recognize crops. experimental results indicate that YOLOv5 network outperformed other selected models, achieving a precision, recall, mean average [email protected] ([email protected]), [email protected] ([email protected]) metrics 95%, 92%, 93%, 79%, respectively. In contrast, DETR exhibited weakest detection performance, values 77%, 69%, 41% for [email protected], [email protected], recommended architecture using data not only because its but this was also determined size (14 MB), which smallest one among models. proposed provides technical guidelines researchers farmers conduct accurate treatment fields.

Язык: Английский

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