Crop Protection, Journal Year: 2024, Volume and Issue: 188, P. 107006 - 107006
Published: Oct. 31, 2024
Language: Английский
Crop Protection, Journal Year: 2024, Volume and Issue: 188, P. 107006 - 107006
Published: Oct. 31, 2024
Language: Английский
Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 222, P. 109032 - 109032
Published: May 29, 2024
Language: Английский
Citations
20Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 219, P. 108792 - 108792
Published: March 2, 2024
Deep learning (DL) inspired models have achieved tremendous success in locating target weed species through bounding-box approach (single-stage models) or pixel-wise semantic segmentation (two-stage models), but not both. Therefore, the goal of this research study was to develop a single-stage DL architecture that only locate presence bounding-boxes also achieves instance on unmanned aerial system (UAS) acquired remote sensing images. Moreover, developed experiments integrating novel C3 and C3x module within its backbone for dense feature extraction, as well ProtoNet (Prototypical network) head component masking. Furthermore, proposed has been trained five categories dataset exported using multiple combinations various augmentation techniques, namely, C1, C2, C3, C4, C5, which metrics were assessed desktop graphical processing unit (GPU) palm-sized edge device (AGX Xavier). Results suggest category combination six data outperformed remaining by achieving precision scores 85.4 % (bounding-boxes) 82.8 (masking) GPU. Whereas, same model converted TorchScript able achieve 79.1 77 masking accuracy an device, respectively. The two potential applications when integrated with site-specific management technologies. First, it enables real-time detection, allowing immediate identification weeds spot-spraying applications. Second, facilitates masking, aiding estimation growth extent actual field conditions. combines both computer vision - detection – provide comprehensive information about growth, eliminating need algorithm.
Language: Английский
Citations
18Agriculture, Journal Year: 2024, Volume and Issue: 14(9), P. 1624 - 1624
Published: Sept. 17, 2024
Amidst escalating global populations and dwindling arable lands, enhancing agricultural productivity sustainability is imperative. Addressing the inefficiencies of traditional agriculture, which struggles to meet demands large-scale production, this paper introduces a highly configurable smart robotic arm system (CARA), engineered using convolutional neural networks multilayer perceptron. CARA integrates arm, an image acquisition module, deep processing center, embodying convergence advanced robotics artificial intelligence facilitate precise efficient tasks including harvesting, pesticide application, crop inspection. Rigorous experimental validations confirm that significantly enhances operational efficiency, adapts seamlessly diverse contexts, bolsters precision farming practices. This study not only underscores vital role intelligent automation in modern agriculture but also sets precedent for future innovations.
Language: Английский
Citations
12Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 220, P. 108861 - 108861
Published: April 4, 2024
Language: Английский
Citations
10Emerging Science Journal, Journal Year: 2024, Volume and Issue: 8(2), P. 496 - 512
Published: April 1, 2024
In recent years, the accurate identification of chili maturity stages has become essential for optimizing cultivation processes. Conventional methodologies, primarily reliant on manual assessments or rudimentary detection systems, often fall short reflecting plant’s natural environment, leading to inefficiencies and prolonged harvest periods. Such methods may be imprecise time-consuming. With rise computer vision pattern recognition technologies, new opportunities in image have emerged, offering solutions these challenges. This research proposes an affordable solution object classification, specifically through version 5 You Only Look Once (YOLOv5) model, determine location state rocoto peppers cultivated Ecuador. To enhance model’s efficacy, we introduce a novel dataset comprising images their authentic states, spanning both immature mature stages, all while preserving settings potential environmental impediments. methodology ensures that closely replicates real-world conditions encountered by system. Upon testing model with this dataset, it achieved accuracy 99.99% classification task 84% rate crops. These promising outcomes highlight potential, indicating game-changing technique small-scale farmers, especially Ecuador, prospects broader applications agriculture. Doi: 10.28991/ESJ-2024-08-02-08 Full Text: PDF
Language: Английский
Citations
10Remote Sensing Applications Society and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 101459 - 101459
Published: Jan. 1, 2025
Language: Английский
Citations
2Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 232, P. 110028 - 110028
Published: Feb. 12, 2025
Language: Английский
Citations
2Frontiers in Plant Science, Journal Year: 2024, Volume and Issue: 15
Published: Aug. 20, 2024
Precision weed management (PWM), driven by machine vision and deep learning (DL) advancements, not only enhances agricultural product quality optimizes crop yield but also provides a sustainable alternative to herbicide use. However, existing DL-based algorithms on detection are mainly developed based supervised approaches, typically demanding large-scale datasets with manual-labeled annotations, which can be time-consuming labor-intensive. As such, label-efficient methods, especially semi-supervised learning, have gained increased attention in the broader domain of computer demonstrated promising performance. These methods aim utilize small number labeled data samples along great unlabeled develop high-performing models comparable counterpart trained large amount samples. In this study, we assess effectiveness framework for multi-class detection, employing two well-known object frameworks, namely FCOS (Fully Convolutional One-Stage Object Detection) Faster-RCNN (Faster Region-based Networks). Specifically, evaluate generalized student-teacher an improved pseudo-label generation module produce reliable pseudo-labels data. To enhance generalization, ensemble student network is employed facilitate training process. Experimental results show that proposed approach able achieve approximately 76% 96% accuracy as 10% CottonWeedDet3 CottonWeedDet12, respectively. We offer access source code (https://github.com/JiajiaLi04/SemiWeeds), contributing valuable resource ongoing research beyond.
Language: Английский
Citations
7Computer Standards & Interfaces, Journal Year: 2025, Volume and Issue: unknown, P. 104005 - 104005
Published: March 1, 2025
Language: Английский
Citations
1International Journal of Remote Sensing, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 34
Published: Jan. 16, 2025
Cultivation of medicinal plants (CMPs) plays a crucial role in sustaining the production resources (MPs). In light depletion wild plant (MPRs), CMPs have become primary source for meeting market demand. However, traditional methods are often limited, subjective, and time-sensitive. recent years, remote sensing (RS) has emerged as an important tool obtaining information on MPs, addressing many limitations inherent conventional techniques. This paper first highlights challenges faced provides comprehensive review main applications RS field. Subsequently, it summarizes existing analysing data, organizing findings previous studies according to types tasks methodologies employed. Approaches data analysis that could be applied Traditional Chinese Medicine (TCM) planning generalized compared. Finally, discusses potential difficulties cultivation process outlines future prospects technologies. latest research application can serve valuable resource both researchers practitioners. Additionally, offers curated selection those interested leveraging technologies precision agriculture plants.
Language: Английский
Citations
0