Опубликована: Янв. 10, 2025
Язык: Английский
Agronomy, Год журнала: 2024, Номер 14(3), С. 634 - 634
Опубликована: Март 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.
Язык: Английский
Процитировано
7
Smart Agricultural Technology, Год журнала: 2024, Номер 8, С. 100488 - 100488
Опубликована: Июнь 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
Язык: Английский
Процитировано
7
Journal of Agriculture and Food Research, Год журнала: 2025, Номер unknown, С. 101734 - 101734
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
1
Biosystems Engineering, Год журнала: 2025, Номер 252, С. 61 - 76
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
1
Agriculture, Год журнала: 2024, Номер 14(9), С. 1473 - 1473
Опубликована: Авг. 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.
Язык: Английский
Процитировано
6
Horticulturae, Год журнала: 2024, Номер 10(9), С. 1006 - 1006
Опубликована: Сен. 22, 2024
The accurate identification of tomato maturity and picking positions is essential for efficient picking. Current deep-learning models face challenges such as large parameter sizes, single-task limitations, insufficient precision. This study proposes MTS-YOLO, a lightweight model detecting fruit bunch stem positions. We reconstruct the YOLOv8 neck network propose high- low-level interactive screening path aggregation (HLIS-PAN), which achieves excellent multi-scale feature extraction through alternating fusion information while reducing number parameters. Furthermore, utilize DySample upsampling, bypassing complex kernel computations with point sampling. Moreover, context anchor attention (CAA) introduced to enhance model’s ability recognize elongated targets bunches stems. Experimental results indicate that MTS-YOLO an F1-score 88.7% [email protected] 92.0%. Compared mainstream models, not only enhances accuracy but also optimizes size, effectively computational costs inference time. precisely identifies foreground need be harvested ignoring background objects, contributing improved efficiency. provides technical solution intelligent agricultural
Язык: Английский
Процитировано
5
Computers and Electronics in Agriculture, Год журнала: 2024, Номер 224, С. 109120 - 109120
Опубликована: Июнь 13, 2024
Язык: Английский
Процитировано
4
Crop Protection, Год журнала: 2024, Номер unknown, С. 106992 - 106992
Опубликована: Окт. 1, 2024
Язык: Английский
Процитировано
3
Smart Agricultural Technology, Год журнала: 2024, Номер unknown, С. 100684 - 100684
Опубликована: Ноя. 1, 2024
Язык: Английский
Процитировано
3
Aquaculture, Год журнала: 2025, Номер unknown, С. 742192 - 742192
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0