Abstract
The use of UAVs to detect litchi in natural environments is imperative for rapid litchi yield estimation and automated harvesting systems. However, UAV-based lychee fruit detection bottlenecks arise from complex canopy architecture and leaf occlusion. This study proposed a collaborative optimization strategy integrating agronomic technique with deep learning. Three leaf thinning intensities (0, 6, and 12 compound leaves) were applied at the early stage of fruit to systematically evaluate their effects on fruit growth, canopy structure, and detection performance. Results indicated that moderate defoliation (six leaves) significantly enhanced canopy openness and light penetration without adversely impacting on yield and fruit quality. Subsequent UAV-based detection under moderate versus no defoliation treatment revealed that the YOLOv8-based model achieved significant performance gains: mean average precision (mAP) increased from 0.818 to 0.884, and the F1-score improved from 0.796 to 0.842. The study contributes a novel collaborative optimization strategy that effectively mitigates occlusion issues in fruit detection. This approach demonstrates that agronomic techniques can be strategically used to enhance AI perception, offering a significant step forward in the integration of agricultural machinery and agronomy for intelligent orchard systems.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 4
Claim · model-written summary
Six-leaf defoliation significantly increased canopy openness and light penetration.
- Treatment
- moderate defoliation (six leaves)e0
- Outcome
- canopy openness and light penetratione0
- Direction
- enhancede0
LitchiNot reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“moderate defoliation (six leaves) significantly enhanced canopy openness and light penetration without adversely impacting on yield and fruit quality”
t0“Litchi”
Claim · model-written summary
Six-leaf defoliation did not adversely affect yield and fruit quality.
- Treatment
- moderate defoliation (six leaves)e0
- Outcome
- yield and fruit qualitye0
- Direction
- without adversely impactinge0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“moderate defoliation (six leaves) significantly enhanced canopy openness and light penetration without adversely impacting on yield and fruit quality”
t0“Litchi”
Claim · model-written summary
Under moderate defoliation, YOLOv8 fruit-detection mAP increased from 0.818 to 0.884 relative to no defoliation.
- Treatment
- moderatee0
- Outcome
- mean average precision (mAP)e0
- Direction
- increasede0
- Value
- 0.818 to 0.884e0
- Comparator
- no defoliatione0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Subsequent UAV-based detection under moderate versus no defoliation treatment revealed that the YOLOv8-based model achieved significant performance gains: mean average precision (mAP) increased from 0.818 to 0.884”
t0“Litchi”
Claim · model-written summary
Under moderate defoliation, YOLOv8 fruit-detection F1-score improved from 0.796 to 0.842.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the F1-score improved from 0.796 to 0.842”
t0“Litchi”
The same questions in other papers
pruning · quality: grain · LitchiResults agree
1 paper in the same direction as this paper
- no effect
pruning · growth · LitchiResults agree
3 papers in the same direction as this paper
- positive
- positive
- positive
Claims are grouped by organism, treatment and what was measured or targeted. A paper counts as positive when any of its claims in the group is positive.