Key Intelligent Pesticide Prescription Spraying Technologies for the Control of Pests, Diseases, and Weeds: A Review

Agriculture, Journal Year: 2025, Volume and Issue: 15(1), P. 81 - 81

Published: Jan. 1, 2025

In modern agriculture, plant protection is the key to ensuring crop health and improving yields. Intelligent pesticide prescription spraying (IPPS) technologies monitor, diagnose, make scientific decisions about pests, diseases, weeds; formulate personalized precision control plans; prevent pests through use of intelligent equipment. This study discusses IPSS from four perspectives: target information acquisition, processing, spraying, implementation control. acquisition section, identification based on images, remote sensing, acoustic waves, electronic nose are introduced. processing methods such as pre-processing, feature extraction, pest disease identification, bioinformatics analysis, time series data addressed. impact selection, dose calculation, time, method resulting effect formulation in a certain area explored. implement vehicle automatic technology, droplet characteristic technology their applications studied. addition, this future development prospectives IPPS technologies, including multifunctional systems, decision-support systems generative AI, sprayers. The advancement these will enhance agricultural productivity more efficient, environmentally sustainable manner.

Language: Английский

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Unmanned Aerial Vehicles for Real-Time Vegetation Monitoring in Antarctica: A Review

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(2), P. 304 - 304

Published: Jan. 16, 2025

The unique challenges of polar ecosystems, coupled with the necessity for high-precision data, make Unmanned Aerial Vehicles (UAVs) an ideal tool vegetation monitoring and conservation studies in Antarctica. This review draws on existing Antarctic UAV mapping, focusing their methodologies, including surveyed locations, flight guidelines, specifications, sensor technologies, data processing techniques, use indices. Despite potential established Machine-Learning (ML) classifiers such as Random Forest, K Nearest Neighbour, Support Vector Machine, gradient boosting semantic segmentation UAV-captured images, there is a notable scarcity research employing Deep Learning (DL) models these extreme environments. While initial suggest that DL could match or surpass performance classifiers, even small datasets, integration advanced into real-time navigation systems UAVs remains underexplored. paper evaluates feasibility deploying equipped adaptive path-planning capabilities, which significantly enhance efficiency safety mapping missions discusses technological logistical constraints observed previous proposes directions future to optimise autonomous drone operations harsh conditions.

Language: Английский

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Intelligent segmentation of wildfire region and interpretation of fire front in visible light images from the viewpoint of an unmanned aerial vehicle (UAV)

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2025, Volume and Issue: 220, P. 473 - 489

Published: Jan. 9, 2025

Language: Английский

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AGSPNet: A framework for parcel-scale crop fine-grained semantic change detection from UAV high-resolution imagery with agricultural geographic scene constraints

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 231, P. 109973 - 109973

Published: Jan. 25, 2025

Language: Английский

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Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images Using SegFormer

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 108 - 122

Published: Jan. 1, 2025

Language: Английский

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RSM-Optimizer: Branch Optimization for Dual- or Multi-Branch Semantic Segmentation Networks

Electronics, Journal Year: 2025, Volume and Issue: 14(6), P. 1109 - 1109

Published: March 11, 2025

Semantic segmentation is a crucial task in the field of computer vision, with important applications areas such as autonomous driving, medical image analysis, and remote sensing analysis. Dual-branch multi-branch semantic networks that leverage deep learning technologies can enhance both accuracy speed. These typically contain branch context branch. However, feature maps detail are limited to single type receptive field, which limits models’ abilities perceive objects at different scales. During map fusion process, low-resolution from upsampled large factor match Unfortunately, these upsampling operations inevitably introduce noise. To address issues, we propose several improvements optimize branches. We first design field-driven enhancement module enrich fields Then, stepwise reduce noise introduced during process fusion. Finally, pyramid mixed pooling (PMPM) improve shapes. Considering diversity terms scale, shape, category urban street scene data, carried out experiments on Cityscapes CamVid datasets. The experimental results datasets validate effectiveness efficiency proposed improvements.

Language: Английский

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Research into the Application of ResNet in Soil: A Review

Agriculture, Journal Year: 2025, Volume and Issue: 15(6), P. 661 - 661

Published: March 20, 2025

With the rapid advancement of deep learning technology, residual networks technique (ResNet) has made significant strides in field image processing, and its application soil science been steadily increasing. ResNet outperforms traditional methods by effectively mitigating vanishing gradient problem, enabling deeper network training, enhancing feature extraction, improving accuracy complex pattern recognition tasks. ResNet, as an efficient model, can automatically extract features from data, accurate classification assessment health. Recent research is increasingly applying to various fields, including type health assessment. Firstly, this manuscript outlines for collecting highlighting significance employing diverse data sources comprehensively understand characteristics. These include acquisition microscopic images, which provide high-resolution insights into soil’s particulate structure at cellular level; remote sensing offer valuable information regarding large-scale properties spatial variations through satellite or drone-based technologies; high-definition capture fine-scale details features, more precise detailed analysis. By integrating these techniques, a solid foundation established subsequent analysis, thereby classification, assessments, environmental impact evaluations. Furthermore, approach contributes advancements precision agriculture, land use planning, erosion monitoring, contamination detection, ultimately supporting sustainable management ecological conservation efforts. Then, advantages using are analyzed, performance across different processing tasks explored. Finally, potential future development directions proposed.

Language: Английский

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DM_CorrMatch: A Semi-Supervised Semantic Segmentation Framework for Rapeseed Flower Coverage Estimation Using UAV Imagery

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 28, 2025

Background Rapeseed(Brassica napus L.) inflorescence coverage is a crucial phenotypic parameter for assessing crop growth and estimating yield. Accurate cover assessment typically performed using Unmanned Aerial Vehicles (UAVs) in combination with semantic segmentation methods. However, the irregular variable morphology of rapeseed inflorescences presents significant challenges segmentation. To address these challenges, advanced methods that can improve accuracy, particularly under limited data conditions, are needed. Results In this study, we propose cost-effective high-throughput approach semi-supervised learning framework, DM_CorrMatch. This method enhances input images through strong weak augmentation techniques, while leveraging Denoising Diffusion Probabilistic Model (DDPM) to generate additional samples data-scarce scenarios.We an automatic update strategy labeled dilute proportion erroneous labels manual Furthermore, novel network architecture, Mamba-Deeplabv3+, proposed, combining strengths Mamba Convolutional Neural Networks (CNNs) both global local feature extraction. architecture effectively captures key features, even varying poses, reducing influence complex backgrounds. The proposed validated on Rapeseed Flower Segmentation Dataset (RFSD), which consists 720 UAV from Yangluo experimental station Oil Crops Research Institute Chinese Academy Agricultural Sciences (CAAS). results showed our outperforms four traditional eleven deep methods, achieving Intersection over Union (IoU) 0.886, Precision 0.942, Recall 0.940. Conclusions The learning-based method, combined Mamba-Deeplabv3+ demonstrates superior performance accurately segmenting challenging conditions. Our handles backgrounds various poses inflorescences, providing reliable tool flower estimation. aid development high-yield cultivars monitoring UAV-based technologies.

Language: Английский

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Integrating unsupervised domain adaptation and SAM technologies for image semantic segmentation: a case study on building extraction from high-resolution remote sensing images

International Journal of Digital Earth, Journal Year: 2025, Volume and Issue: 18(1)

Published: April 15, 2025

Language: Английский

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A survey on Machine learning algorithms in autonomous multiple unmanned aerial vehicles (UAVs) in wireless networks

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 117677 - 117677

Published: April 1, 2025

Language: Английский

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