Improved Detection and Location of Small Crop Organs by Fusing UAV Orthophoto Maps and Raw Images

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(5), P. 906 - 906

Published: March 4, 2025

Extracting the quantity and geolocation data of small objects at organ level via large-scale aerial drone monitoring is both essential challenging for precision agriculture. The quality reconstructed digital orthophoto maps (DOMs) often suffers from seamline distortion ghost effects, making it difficult to meet requirements organ-level detection. While raw images do not exhibit these issues, they pose challenges in accurately obtaining detected objects. detection was improved this study through fusion with using EasyIDP tool, thereby establishing a mapping relationship data. Small object conducted by Slicing-Aided Hyper Inference (SAHI) framework YOLOv10n on accelerate inferencing speed farmland. As result, comparing directly DOM, accelerated accuracy improved. proposed SAHI-YOLOv10n achieved mean average (mAP) scores 0.825 0.864, respectively. It also processing latency 1.84 milliseconds 640×640 resolution frames application. Subsequently, novel crop canopy dataset (CCOD-Dataset) created interactive annotation SAHI-YOLOv10n, featuring 3986 410,910 annotated boxes. method demonstrated feasibility detecting three in-field farmlands, potentially benefiting future wide-range applications.

Language: Английский

A Plug Seedling Growth-Point Detection Method Based on Differential Evolution Extra-Green Algorithm

Agronomy, Journal Year: 2025, Volume and Issue: 15(2), P. 375 - 375

Published: Jan. 31, 2025

To produce plug seedlings with uniform growth and which are suitable for high-speed transplanting operations, it is essential to sow seeds precisely at the center of each plug-tray hole. For accurately determining position seed covered by substrate within individual holes, a novel method detecting points has been proposed. It employs an adaptive grayscale processing algorithm based on differential evolution extra-green extract contour features during early stages cotyledon emergence. The pixel overlay curve peak binary image plug-tray’s background utilized delineate boundaries holes. Each hole containing single seedling identified analyzing area perimeter seedling’s connectivity domains. midpoint shortest line between these domains designated as point seedling. laboratory-grown tomato, pepper, Chinese kale, highest detection accuracy was achieved third-, fourth-, second-days’ post-cotyledon emergence, respectively. identification rate missing exceeded 97.57% 99.25%, respectively, growth-point error less than 0.98 mm. tomato broccoli cultivated in nursery greenhouse three days after greater 95.78%, 2.06 These results validated high broad applicability proposed various types appropriate stages.

Language: Английский