Lightweight Salix Cheilophila Recognition Method Based on Improved YOLOv8n DOI
Haotian Ma, Zhigang Liu,

C. C. Pei

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 11, 2024

Abstract Stumping is an important measure for the care and management of salix cheilophila during its growth. Rapid accurate detection in stumping period desert basis intelligent equipment. However, complex model needs high computing power hardware. It limits deployment application recognition Therefore, this study took areas Shierliancheng, Inner Mongolia Autonomous Region as research object, proposed improved YOLOv8 rapid identification method, named YOLOV8-VCAD. First, lightweight network VanillaNet was used to replace backbone lessen load complexity model. Coordinate attention mechanism embedded extract features by setting location information, which strengthened regression positioning abilities Second, introducing adaptive feature fusion pyramid significantly strengthens model's ability characterize integrate features, improving accuracy performance target detection. Finally, CIoU loss replaced DIoU quicken convergence The experimental results show method 95.4%, floating-point a second (Flops) parameters are 7.4G 5.46M, respectively. Compared traditional YOLOv8, precision algorithm increased 7.7%, recall 1.0%, computational reduced 16.8%, 7.9%. YOLOV8-VCAD obviously better than YOLOv8. paper can quickly accurately detect period. Besides, it reduce cost difficulty vision module equipment, provide technical support automatic intelligence

Language: Английский

A lightweight palm fruit detection network for harvesting equipment integrates binocular depth matching DOI
Jiehao Li, Tao Zhang,

Qunfei Luo

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 233, P. 110061 - 110061

Published: Feb. 27, 2025

Language: Английский

Citations

1
GVC-YOLO: A Lightweight Real-Time Detection Method for Cotton Aphid-Damaged Leaves Based on Edge Computing DOI Creative Commons
Zhenyu Zhang, Yunfan Yang, Xin Xu

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(16), P. 3046 - 3046

Published: Aug. 19, 2024

Cotton aphids (Aphis gossypii Glover) pose a significant threat to cotton growth, exerting detrimental effects on both yield and quality. Conventional methods for pest disease surveillance in agricultural settings suffer from lack of real-time capability. The use edge computing devices processing aphid-damaged leaves captured by field cameras holds practical research value large-scale control measures. mainstream detection models are generally large size, making it challenging achieve with limited resources. In response these challenges, we propose GVC-YOLO, method based computing. Building upon YOLOv8n, lightweight GSConv VoVGSCSP modules employed reconstruct the neck backbone networks, thereby reducing model complexity while enhancing multiscale feature fusion. network, integrate coordinate attention (CA) mechanism SimSPPF network increase model’s ability extract features leaves, balancing accuracy loss after becoming lightweight. experimental results demonstrate that size GVC-YOLO is only 5.4 MB, decrease 14.3% compared baseline reduction 16.7% number parameters 17.1% floating-point operations (FLOPs). [email protected] [email protected]:0.95 reach 97.9% 90.3%, respectively. optimized accelerated TensorRT then deployed onto embedded device Jetson Xavier NX detecting aphid damage video camera. Under FP16 quantization, speed reaches 48 frames per second (FPS). summary, proposed demonstrates good speed, its performance scenarios meets application needs. This provides convenient effective intelligent precise pests fields.

Language: Английский

Citations

6
Algorithms for Plant Monitoring Applications: A Comprehensive Review DOI Creative Commons
Giovanni Paolo Colucci, Paola Battilani, Marco Camardo Leggieri

et al.

Algorithms, Journal Year: 2025, Volume and Issue: 18(2), P. 84 - 84

Published: Feb. 5, 2025

Many sciences exploit algorithms in a large variety of applications. In agronomy, amounts agricultural data are handled by adopting procedures for optimization, clustering, or automatic learning. this particular field, the number scientific papers has significantly increased recent years, triggered scientists using artificial intelligence, comprising deep learning and machine methods bots, to process crop, plant, leaf images. Moreover, many other examples can be found, with different applied plant diseases phenology. This paper reviews publications which have appeared past three analyzing used classifying agronomic aims crops applied. Starting from broad selection 6060 papers, we subsequently refined search, reducing 358 research articles 30 comprehensive reviews. By summarizing advantages applying analyses, propose guide farming practitioners, agronomists, researchers, policymakers regarding best practices, challenges, visions counteract effects climate change, promoting transition towards more sustainable, productive, cost-effective encouraging introduction smart technologies.

Language: Английский

Citations

0
Efficient and accurate identification of maize rust disease using deep learning model DOI Creative Commons
Pei Wang, Jiajia Tan, Yuheng Yang

et al.

Frontiers in Plant Science, Journal Year: 2025, Volume and Issue: 15

Published: Feb. 6, 2025

Common corn rust and southern rust, two typical maize diseases during growth stages, require accurate differentiation to understand their occurrence patterns pathogenic risks. To address this, a specialized Maize-Rust model integrating SimAM module in the YOLOv8s backbone BiFPN for scale fusion, along with DWConv streamlined detection, was developed. The achieved an accuracy of 94.6%, average 91.6%, recall rate 85.4%, F1 value 0.823, outperforming Faster-RCNN SSD models by 16.35% 12.49% classification accuracy, respectively, detecting single image at 16.18 frames per second. Deployed on mobile phones, enables real-time data collection analysis, supporting effective detection management large-scale outbreaks field.

Language: Английский

Citations

0
Plantation Monitoring Using Drone Images: A Dataset and Performance Review DOI Creative Commons

Yashwanth Karumanchi,

G. Angeline Prasanna,

Snehasis Mukherjee

et al.

Published: Feb. 19, 2025

Automatic monitoring of tree plantations plays a crucial role in agriculture. Flawless health helps farmers make informed decisions regarding their management by taking appropriate action. Use drone images for automatic plantation can enhance the accuracy process, while still being affordable to small developing countries such as India. Small, low cost drones equipped with an RGB camera capture high-resolution agricultural fields, allowing detailed analysis well-being plantations. Existing methods automated are mostly based on satellite images, which difficult get farmers. We propose system using becoming easier dataset trees three categories: “Good health”, “Stunted”, and “Dead”. annotate CVAT annotation tool, use research purposes. experiment different well-known CNN models observe performance proposed dataset. The initial levels show complexity Further, our study revealed that, depth-wise convolution operation embedded deep model, model we apply state-of-the-art object detection identify individual better monitor them automatically. along annotations, codes, will be open sourced further research.

Language: Английский

Citations

0
Automated pipeline for leaf spot severity scoring in peanuts using segmentation neural networks DOI Creative Commons

Joshua Larsen,

Jeffrey C. Dunne, Robert Austin

et al.

Plant Methods, Journal Year: 2025, Volume and Issue: 21(1)

Published: Feb. 20, 2025

Late and early leaf spot in peanuts is a foliar disease contributing to significant amount of lost yield globally. Peanut breeding programs frequently focus on developing disease-resistant peanut genotypes. However, existing phenotyping protocols employ subjective rating scales, performed by human raters, who determine the severity infection. The objective this study was develop an end-to-end pipeline that can serve replace expert scorer field. This accomplished using image capture segmentation neural networks extracted lesion areas from plot-level images appropriate for infection severity. incorporated network accurately determined infected surface area identified dead leaves cellphone imagery. Image processing algorithms then convert these labels into quality metrics efficiently score based versus non-infected area. evaluated field data plots with varying severity, creating dataset thousands spanned conventional visual scores ranging 1-9. These predictions were presence defoliated surrounding We able demonstrate automated scoring, as compared root mean square error 0.996 scores, individual (one per plot), 0.800 when three captured each plot. Results indicated model alternative scoring. Eliminating subjectivity scoring will allow non-experts collect may enable drone-based collection. could reduce time needed obtain new lines or identify genes responsible resistance peanut.

Language: Английский

Citations

0
Drone‐based polarization imaging system for leaf spot severity determination in peanut plants DOI Creative Commons

Joshua Larsen,

Jeffrey C. Dunne, Robert Austin

et al.

The Plant Phenome Journal, Journal Year: 2025, Volume and Issue: 8(1)

Published: Feb. 21, 2025

Abstract In this study, we introduce a new approach for enhancing peanut phenotyping through polarization imaging platform. With leaf spot disease posing significant threats to ( Arachis hypogae L.) crops, our research addresses the need accurate and efficient detection methods. Polarization offers unique advantages over more traditional spectral solutions. correlates strongly with geometric properties of an object, such as surface roughness or its orientation relative sensor light source. Leveraging drone‐based system, conducted extensive field trials, collecting approximately 30,184 images two growing seasons locations. Images were processed panchromatic (400–800 nm wavelengths) degree linear (DOLP) compared conventional red, green, blue (RGB) imagery against visual severity scores (modified nine‐point scale). Results indicated that when attempting determine ground truth infection severity, DOLP alone provided 1.34 root mean square error, RGB 1.09 error accuracy, both modalities 1.03 indicating adding capability can enhance augment scoring pipelines. We expect may allow phenotypic models mitigate—or leverage—confounding factors related leaf's without 3D imaging.

Language: Английский

Citations

0
A cell P system with membrane division and dissolution rules for soybean leaf disease recognition DOI Creative Commons
Hongping Song, Yourui Huang, Tao Han

et al.

Plant Methods, Journal Year: 2025, Volume and Issue: 21(1)

Published: March 18, 2025

Rapid and accurate identification of soybean leaf diseases is crucial for optimizing crop health yield. We propose a cell P system with membrane division dissolution rules (DDC-P system) disease identification. Among them, the designed Efficient feature attention (EFA) lightweight sandglass structure efficient (SGEFA) can focus on disease-specific information while reducing environmental interference. A fuzzy controller was developed to manage SGEFA membranes, allowing adaptive adjustments model avoiding redundancy. Experimental results homemade dataset show that DDC-P achieves recognition rate 98.43% an F1 score 0.9874, size only 1.41 MB. On public dataset, accuracy 94.40% 0.9425. The average time edge device 0.042857 s, FPS 23.3. These outstanding demonstrate not excels in generalization but also ideally suited deployment devices, revolutionizing approach management.

Language: Английский

Citations

0
Excellent tomato detector based on pruning and distillation to balance accuracy and lightweight DOI
Lixiang Huang, Jiqing Chen,

Hongwei Li

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 227, P. 109520 - 109520

Published: Oct. 5, 2024

Language: Английский

Citations

3
Deep learning for plant stress detection: A comprehensive review of technologies, challenges, and future directions DOI Creative Commons

Nijhum Paul,

G C Sunil,

David J. Horvath

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 229, P. 109734 - 109734

Published: Dec. 13, 2024

Language: Английский

Citations

2