GSE-YOLO: A Lightweight and High-Precision Model for Identifying the Ripeness of Pitaya (Dragon Fruit) Based on the YOLOv8n Improvement DOI Creative Commons
Qiu Zhi, Zhiyuan Huang, Deyun Mo

et al.

Horticulturae, Journal Year: 2024, Volume and Issue: 10(8), P. 852 - 852

Published: Aug. 12, 2024

Pitaya fruit is a significant agricultural commodity in southern China. The traditional method of determining the ripeness pitaya by humans inefficient, it therefore utmost importance to utilize precision agriculture and smart farming technologies order accurately identify fruit. In achieve rapid recognition targets natural environments, we focus on maturity as research object. During growth process, undergoes changes its shape color, with each stage exhibiting characteristics. Therefore, divided into four stages according different levels, namely Bud, Immature, Semi-mature Mature, have designed lightweight detection classification network for recognizing based YOLOv8n algorithm, GSE-YOLO (GhostConv SPPELAN-EMA-YOLO). specific methods include replacing convolutional layer backbone model, incorporating attention mechanisms, modifying loss function, implementing data augmentation. Our improved model achieved accuracy 85.2%, recall rate 87.3%, an F1 score 86.23, mAP50 90.9%, addressing issue false or missed intricate environments. experimental results demonstrate that our enhanced has attained commendable level discerning ripeness, which positive impact advancement technologies.

Language: Английский

Detection and tracking of oestrus dairy cows based on improved YOLOv8n and TransT models DOI
Zheng Wang, Hongxing Deng, Shujin Zhang

et al.

Biosystems Engineering, Journal Year: 2025, Volume and Issue: 252, P. 61 - 76

Published: March 1, 2025

Language: Английский

Citations

1
Field-Deployed Spectroscopy from 350 to 2500 nm: A Promising Technique for Early Identification of Powdery Mildew Disease (Erysiphe necator) in Vineyards DOI Creative Commons
Sergio Vélez, Enrique Barajas, J.A. Rubio

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(3), P. 634 - 634

Published: March 21, 2024

This study explores spectroscopy in the 350 to 2500 nm range for detecting powdery mildew (Erysiphe necator) grapevine leaves, crucial precision agriculture and sustainable vineyard management. In a controlled experimental setting, spectral reflectance on leaves with varying infestation levels was measured using FieldSpec 4 spectroradiometer during July September. A detailed assessment conducted following guidelines recommended by European Mediterranean Plant Protection Organization (EPPO) quantify level of infestation; categorising into five distinct grades based percentage leaf surface area affected. Subsequently, data were collected contact probe tungsten halogen bulb connected spectroradiometer, taking three measurements across different areas each leaf. Partial Least Squares Regression (PLSR) analysis yielded coefficients determination R2 = 0.74 0.71, Root Mean Square Errors (RMSEs) 12.1% 12.9% calibration validation datasets, indicating high accuracy early disease detection. Significant differences noted between healthy infected especially around 450 700 visible light, 1050 nm, 1425 1650 2250 near-infrared spectrum, likely due tissue damage, chlorophyll degradation water loss. Finally, Powdery Mildew Vegetation Index (PMVI) introduced, calculated as PMVI (R755 − R675)/(R755 + R675), where R755 R675 are reflectances at 755 (NIR) 675 (red), effectively estimating severity (R2 0.7). The demonstrates that spectroscopy, combined PMVI, provides reliable, non-invasive method managing promoting healthier vineyards through practices.

Language: Английский

Citations

7
Speeding up UAV-based crop variability assessment through a data fusion approach using spatial interpolation for site-specific management DOI Creative Commons
Sergio Vélez, Mar Ariza-Sentís, Marko Panić

et al.

Smart Agricultural Technology, Journal Year: 2024, Volume and Issue: 8, P. 100488 - 100488

Published: June 15, 2024

Innovations in precision agriculture enhance complex tasks, reduce environmental impact, and increase food production cost efficiency. One of the main challenges is ensuring rapid information availability for autonomous vehicles standardizing processes across platforms to maximize interoperability. The lack drone technology standardisation, communication barriers, high costs, post-processing requirements sometimes hinder their widespread use agriculture. This research introduces a standardized data fusion framework creating real-time spatial variability maps using images from different Unmanned Aerial Vehicles (UAVs) Site-Specific Crop Management (SSM). Two interpolation methods were used (Inverse Distance Weight, IDW, Triangulated Irregular Networks, TIN), selected computational efficiency input flexibility. proposed can UAV image sources offers versatility, speed, efficiency, consuming up 98 % less time, energy, computing than standard photogrammetry techniques, providing field information, allowing edge incorporation into acquisition phase. Experiments conducted Spain, Serbia, Finland 2022 under H2020 FlexiGroBots project demonstrated strong correlation between results this method those techniques (up r = 0.93). In addition, with Sentinel 2 satellite was as that obtained photogrammetry-based orthomosaics 0.8). approach could support irrigation leak detection, soil parameter estimation, weed management, integration

Language: Английский

Citations

7
Research Progress on Autonomous Operation Technology for Agricultural Equipment in Large Fields DOI Creative Commons
Wenbo Wei, Maohua Xiao, Weiwei Duan

et al.

Agriculture, Journal Year: 2024, Volume and Issue: 14(9), P. 1473 - 1473

Published: Aug. 29, 2024

Agriculture is a labor-intensive industry. However, with the demographic shift toward an aging population, agriculture increasingly confronted labor shortage. The technology for autonomous operation of agricultural equipment in large fields can improve productivity and reduce intensity, which help alleviate impact population on agriculture. Nevertheless, significant challenges persist practical application this technology, particularly concerning adaptability, operational precision, efficiency. This review seeks to systematically explore advancements unmanned operations, focus onboard environmental sensing, full-coverage path planning, control technologies. Additionally, discusses future directions key technologies fields. aspires serve as foundational reference development large-scale equipment.

Language: Английский

Citations

5
Swin-Roleaf: A new method for characterizing leaf azimuth angle in large-scale maize plants DOI
Weilong He, Joseph L. Gage, Rubén Rellán‐Álvarez

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 224, P. 109120 - 109120

Published: June 13, 2024

Language: Английский

Citations

4
A new quantitative analysis method for the fish behavior under ammonia nitrogen stress based on pruning strategy DOI
Wenkai Xu, Jiaxuan Yu, Ying Xiao

et al.

Aquaculture, Journal Year: 2025, Volume and Issue: unknown, P. 742192 - 742192

Published: Jan. 1, 2025

Language: Английский

Citations

0
Enhancing optical nondestructive methods for food quality and safety assessments with machine learning techniques: A survey DOI Creative Commons
Xinhao Wang,

Yihang Feng,

Yi Wang

et al.

Journal of Agriculture and Food Research, Journal Year: 2025, Volume and Issue: unknown, P. 101734 - 101734

Published: Feb. 1, 2025

Language: Английский

Citations

0
Comparative Study of YOLOv8, YOLOv9 and YOLOv10 by Their Ability to Detect Mangoes DOI
Moustapha Bikienga,

Manegaouindé Roland Tougma,

Sanguirè Pascal Somda

et al.

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 77 - 88

Published: Jan. 1, 2025

Language: Английский

Citations

0
Systematic review on machine learning and computer vision in precision agriculture: Applications, trends, and emerging techniques DOI
Yean‐Der Kuan,

K. M. Goh,

Lee‐Ling Lim

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 148, P. 110401 - 110401

Published: March 13, 2025

Language: Английский

Citations

0
Challenges and Solution Directions for the Integration of Smart Information Systems in the Agri-Food Sector DOI Creative Commons
Emmanuel Ahoa, Ayalew Kassahun,

C.N. Verdouw

et al.

Sensors, Journal Year: 2025, Volume and Issue: 25(8), P. 2362 - 2362

Published: April 8, 2025

Traditional farming has evolved from standalone computing systems to smart farming, driven by advancements in digitalization. This led the proliferation of diverse information (IS), such as IoT and sensor systems, decision support farm management (FMISs). These often operate isolation, limiting their overall impact. The integration IS into connected is widely addressed a key driver tackle these issues. However, it complex, multi-faceted issue that not easily achievable. Previous studies have offered valuable insights, but they focus on specific cases, individual certain aspects, lacking comprehensive overview various dimensions. systematic review 74 scientific papers addresses this gap providing an digital technologies involved, levels types, barriers hindering integration, available approaches overcoming challenges. findings indicate primarily relies point-to-point approach, followed cloud-based integration. Enterprise service bus, hub-and-spoke, semantic web are mentioned less frequently gaining interest. study identifies discusses 27 challenges three main areas: organizational, technological, data governance-related Technologies blockchain, spaces, AI, edge microservices, service-oriented architecture methods solutions for governance interoperability insights can help enhance interoperability, leading data-driven increases food production, mitigates climate change, optimizes resource usage.

Language: Английский

Citations

0