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<center>PDT: Uav Target Detection Dataset for Pests and Diseases Tree</center>

Abstract

UAVs emerge as the optimal carriers for visual weed identification and integrated pest and disease management in crops. However, the absence of specialized datasets impedes the advancement of model development in this domain. To address this, we have developed the Pests and Diseases Tree dataset (PDT dataset). PDT dataset represents the first high-precision UAV-based dataset for targeted detection of tree pests and diseases, which is collected in real-world operational environments and aims to fill the gap in available datasets for this field. Moreover, by aggregating public datasets and network data, we further introduced the Common Weed and Crop dataset (CWC dataset) to address the challenge of inadequate classification capabilities of test models within datasets for this field. Finally, we propose the YOLO-Dense Pest (YOLO-DP) model for high-precision object detection of weed, pest, and disease crop images. We re-evaluate the state-of-the-art detection models with our proposed PDT dataset and CWC dataset, showing the completeness of the dataset and the effectiveness of the YOLO-DP.

Download Dataset

Hugging Face: PDT dataset v2 (Improve the quality 2024.10.4), CWC dataset

Code

GitHub: YOLO-DP Model

Datasets

PDT dataset

Class: unhealthy

(a) is a healthy goal and (b) is a unhealthy goal. The PDT dataset takes (b) as the category.

Double Resolution:

Dataset Structure:

Edition	Classes	Structure	Targeted images	Untargeted images	Image size	Instances	Target Amount <br> S(Small) M(Medium) L(Large)
Sample	unhealthy	Train	81	1	640×640	2569	1896 548 179
		Val	19	1	640×640	691	528 138 25
LL	unhealthy	Train	3166	1370	640×640	90290	70418 16342 3530
		Val	395	172	640×640	12523	9926 2165 432
		Test	390	177	640×640	11494	8949 2095 450
LH	unhealthy	-	105	0	5472×3648	93474	93474 0 0

CWC dataset

class: bluegrass, chenopodium_album, cirsium_setosum, corn, sedge, cotton, nightshade, tomato, velvet, lettuce, radish

Dataset Sources:

Datasets	Corn weed datastes	lettuce weed datastes	radish weed datastes	Fresh-weed-data
Classes	bluegrass, corn, sedge, chenopodium_album, cirsium_setosum	lettuce	radish	nightshade, tomato, cotton, velvet
Number	250	200	201	115, 116, 24, 38
Image Size	800×600	800×600	800×600	800×600, 800×600, 586×444, 643×500

Dataset Structure:

Classes		bluegrass	chenopodium_album	cirsium_setosum	corn	sedge	lettuce	radish	nightshade	tomato	cotton	velvet
Targeted Images	Train	200	200	200	200	200	200	200	200	200	200	200
	Val	40	40	40	40	40	40	40	40	40	40	40
	Test	10	10	10	10	10	10	10	10	10	10	10
Targeted Amount	S	1	0	0	5	0	0	0	0	0	0	0
	M	0	0	0	9	0	0	0	0	0	0	0
	L	249	250	250	236	250	444	326	250	210	268	248
Image Size	800×600	800×600	800×600	800×600	800×600	800×600	800×600	800×600	800×600	800×600	586×444	643×500

Models

Network Structure:

Experiment

Dataset Validation:

Method	Gflops	Rank (F1) <br> PDT (LL) (our), MPP	Rank (P) <br> CWC (our), Weeds	Rank (R) <br> SugarBeet2017, CAW, RI
YOLO-DP	11.7	1 (0.89), 1 (0.42)	2 (92.9%), 2 (76.5%)	1 (73.8%), 2 (89.4%), 1 (82.3%)
YOLOv3	155.3	5 (0.88), 5 (0.38)	6 (86.6%), 1 (83.0%)	6 (46.2%), 6 (73.1%), 6 (74.0%)
YOLOv4s	20.8	3 (0.88), 2 (0.42)	5 (87.3%), 3 (75.6%)	3 (60.3%), 3 (86.5%), 2 (81.7%)
YOLOv5s	16.0	2 (0.89), 3 (0.38)	4 (88.6%), 4 (75.3%)	4 (58.3%), 5 (82.5%), 3 (81.5%)
YOLOv7	105.1	6 (0.85), 6 (0.24)	1 (93.1%), 5 (74.4%)	5 (48.1%), 4 (83.3%), 4 (80.3%)
YOLOv8s	28.6	4 (0.88), 4 (0.38)	3 (92.0%), 6 (70.4%)	2 (65.0%), 1 (90.1%), 5 (79.3%)

Based on the dataset's characteristics, we choose different metrics for the ranking model (Rank (Metrics)). We sort models with the same metrics score again using Gflops.

Comparative Experiment:

Datasets	Approach	P	R	mAP@.5	mAP@.5:.95	F1	Gflops	Parameters	FPS	Pre-training
PDT dataset (LL)	SSD	84.5%	87.7%	85.1%	-	0.86	273.6	23.7M	37	✓
	EfficientDet	92.6%	73.4%	72.3%	-	0.82	11.5	6.7M	12	✓
	RetinaNet	93.3%	65.3%	64.2%	-	0.79	109.7	32.7M	32	✓
	CenterNet	95.2%	67.4%	66.5%	-	0.79	109.7	32.7M	32	✓
	Faster-RCNN	57.8%	70.5%	61.7%	-	0.64	401.7	136.7M	13	✓
	YOLOv3	88.5%	88.1%	93.4%	65.7%	0.88	155.3	61.5M	41	-
	YOLOv4s	88.8%	88.2%	94.7%	66.1%	0.88	20.8	9.1M	51	-
	YOLOv5s_7.0	88.9%	88.5%	94.2%	67.0%	0.89	16.0	7.0M	93	-
	YOLOv6s	-	-	91.4%	63.2%	-	44.1	17.2M	43	-
	YOLOv7	87.4%	82.6%	90.1%	55.5%	0.85	105.1	37.2M	32	-
	YOLOv8s	88.7%	87.5%	94.0%	67.9%	0.88	28.6	11.1M	60	-
	WeedNet-R	87.7%	48.1%	70.4%	-	0.62	19.0	25.6M	0.5	-
	YOLO-DP (our)	90.2%	88.0%	94.5%	67.5%	0.89	11.7	5.2M	109	-

CWC dataset	SSD	97.7%	77.6%	85.7%	-	0.91	426.9	23.7M	29	✓
	EfficientDet	97.2%	98.6%	98.6%	-	0.90	11.5	6.7M	13	✓
	RetinaNet	95.1%	98.3%	98.0%	-	0.97	261.3	36.4M	24	✓
	CenterNet	96.6%	73.8%	73.3%	-	0.80	171.4	32.7M	27	✓
	YOLOv3	86.8%	89.4%	93.2%	82.3%	0.88	154.7	61.5M	30	-
	YOLOv4s	87.3%	87.9%	91.9%	81.5%	0.88	20.8	9.1M	43	-
	YOLOv5s_7.0	88.6%	88.7%	93.0%	81.2%	0.89	16.0	7.0M	65	-
	YOLOv6s	-	-	92.7%	84.3%	-	68.9	17.2M	31	-
	YOLOv7	93.1%	76.4%	88.1%	75.6%	0.84	105.1	37.2M	21	-
	YOLOv8s	92.0%	89.1%	94.0%	86.2%	0.91	28.6	11.1M	38	-
	WeedNet-R	86.1%	51.8%	71.6%	-	0.65	19.0	25.6M	0.5	-
	YOLO-DP (our)	92.9%	87.5%	91.8%	81.0%	0.90	11.7	5.2M	72	-

Sugar-Beet2017	SSD	85.0%	83.6%	79.3%	-	0.85	1120	23.7M	19	✓
	EfficientDet	93.3%	79.8%	77.8%	-	0.86	11.5	6.7M	16	✓
	RetinaNet	91.7%	78.8%	76.6%	-	0.84	256.4	36.4M	23	✓
	CenterNet	97.9%	51.2%	51.0%	-	0.62	117.4	32.7M	41	✓
	Faster-RCNN	63.6%	87.4%	80.0%	-	0.73	546.9	136.7M	25	✓
	YOLOv3	34.8%	46.2%	39.4%	25.6%	0.40	155.3	61.5M	28	-
	YOLOv4s	28.1%	60.3%	41.1%	26.4%	0.38	20.8	9.1M	28	-
	YOLOv5s_7.0	25.0%	58.3%	40.6%	26.7%	0.35	16.0	7.0M	50	-
	YOLOv6s	-	-	24.6%	15.0%	-	185.2	17.2M	49	-
	YOLOv7	34.2%	48.1%	38.6%	24.9%	0.40	105.1	37.2M	18	-
	YOLOv8s	23.9%	65.0%	39.1%	26.1%	0.35	28.6	11.1M	33	-
	WeedNet-R	90.1%	68.4%	84.8%	-	0.78	19.0	25.6M	0.5	-
	YOLO-DP (our)	23.1%	73.8%	38.3%	25.0%	0.35	11.7	5.2M	62	-

Ablation Experiment:

Datasets	Approach	P	R	mAP@.5	mAP@.5:.95	F1	Gflops	Parameters
PDT dataset (LL)	v5s_C1	88.2%	88.5%	93.9%	67.1%	0.88	25.3	10.0M
	v5s_C2	88.8%	88.4%	94.1%	67.0%	0.88	17.2	7.3M
	v5s_C2f	88.6%	88.5%	93.8%	67.1%	0.88	17.4	7.5M
	v5s_C3	88.9%	88.5%	94.2%	67.0%	0.89	16.0	7.0M
	v5s_C3x	88.7%	81.5%	87.4%	63.2%	0.85	14.5	6.5M
	v5s_C3TR	88.3%	89.0%	94.1%	67.1%	0.89	15.7	7.0M
	v5s_C3Ghost	88.9%	88.2%	94.2%	66.7%	0.88	12.5	5.9M
	v5s_SE	88.8%	88.3%	94.4%	66.6%	0.88	10.6	5.1M
	v5s_CBAM	89.8%	87.5%	94.4%	66.5%	0.89	10.9	5.6M
	v5s_GAM	89.2%	87.7%	94.0%	67.1%	0.88	16.4	7.5M
	v5s_ECA	89.7%	87.0%	94.3%	66.2%	0.88	10.5	5.1M
	v5s_our	89.1%	88.5%	94.4%	67.2%	0.89	12.2	6.1M

Visualization Research

Detect of PDT dataset

Training of CWC dataset

Paper

PDT: Uav Target Detection Dataset for Pests and Diseases Tree. Mingle Zhou, Rui Xing, Delong Han, Zhiyong Qi, Gang Li*. ECCV 2024.