亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整的填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Deep Object Detection of Crop Weeds: Performance of YOLOv7 on a Real Case Dataset from UAV Images

杂草 计算机科学 人工智能 精准农业 目标检测 鉴定(生物学) 机器学习 模式识别(心理学) 农业 生态学 农学 植物 生物
作者
Ignazio Gallo,Anwar Ur Rehman,Ramin Heidarian Dehkordi,Nicola Landro,Riccardo La Grassa,Mirco Boschetti
出处
期刊:Remote Sensing [Multidisciplinary Digital Publishing Institute]
卷期号:15 (2): 539-539 被引量:103
标识
DOI:10.3390/rs15020539
摘要

Weeds are a crucial threat to agriculture, and in order to preserve crop productivity, spreading agrochemicals is a common practice with a potential negative impact on the environment. Methods that can support intelligent application are needed. Therefore, identification and mapping is a critical step in performing site-specific weed management. Unmanned aerial vehicle (UAV) data streams are considered the best for weed detection due to the high resolution and flexibility of data acquisition and the spatial explicit dimensions of imagery. However, with the existence of unstructured crop conditions and the high biological variation of weeds, it remains a difficult challenge to generate accurate weed recognition and detection models. Two critical barriers to tackling this challenge are related to (1) a lack of case-specific, large, and comprehensive weed UAV image datasets for the crop of interest, (2) defining the most appropriate computer vision (CV) weed detection models to assess the operationality of detection approaches in real case conditions. Deep Learning (DL) algorithms, appropriately trained to deal with the real case complexity of UAV data in agriculture, can provide valid alternative solutions with respect to standard CV approaches for an accurate weed recognition model. In this framework, this paper first introduces a new weed and crop dataset named Chicory Plant (CP) and then tests state-of-the-art DL algorithms for object detection. A total of 12,113 bounding box annotations were generated to identify weed targets (Mercurialis annua) from more than 3000 RGB images of chicory plantations, collected using a UAV system at various stages of crop and weed growth. Deep weed object detection was conducted by testing the most recent You Only Look Once version 7 (YOLOv7) on both the CP and publicly available datasets (Lincoln beet (LB)), for which a previous version of YOLO was used to map weeds and crops. The YOLOv7 results obtained for the CP dataset were encouraging, outperforming the other YOLO variants by producing value metrics of 56.6%, 62.1%, and 61.3% for the mAP@0.5 scores, recall, and precision, respectively. Furthermore, the YOLOv7 model applied to the LB dataset surpassed the existing published results by increasing the mAP@0.5 scores from 51% to 61%, 67.5% to 74.1%, and 34.6% to 48% for the total mAP, mAP for weeds, and mAP for sugar beets, respectively. This study illustrates the potential of the YOLOv7 model for weed detection but remarks on the fundamental needs of large-scale, annotated weed datasets to develop and evaluate models in real-case field circumstances.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
SimonShaw发布了新的文献求助10
1分钟前
blenx完成签到,获得积分10
1分钟前
赎罪完成签到 ,获得积分10
1分钟前
1分钟前
2分钟前
李洁发布了新的文献求助10
2分钟前
寒冷的如容完成签到,获得积分20
2分钟前
碳酸芙兰完成签到,获得积分10
2分钟前
tutu完成签到,获得积分10
2分钟前
海盐芝士发布了新的文献求助20
2分钟前
2分钟前
SimonShaw发布了新的文献求助10
2分钟前
科研通AI2S应助科研通管家采纳,获得10
2分钟前
科研通AI5应助科研通管家采纳,获得10
2分钟前
酷波er应助重要纸飞机采纳,获得10
2分钟前
海盐芝士完成签到,获得积分10
3分钟前
3分钟前
小郭完成签到 ,获得积分10
3分钟前
nanfang完成签到 ,获得积分10
3分钟前
田様应助李洁采纳,获得10
3分钟前
3分钟前
李洁完成签到,获得积分10
3分钟前
azuzuzu发布了新的文献求助10
3分钟前
SimonShaw发布了新的文献求助10
3分钟前
SimonShaw完成签到,获得积分10
4分钟前
情怀应助科研通管家采纳,获得10
4分钟前
乾坤侠客LW完成签到,获得积分10
4分钟前
4分钟前
HR_GR应助kento采纳,获得200
5分钟前
大模型应助Silence采纳,获得10
5分钟前
5分钟前
我爱科研发布了新的文献求助10
5分钟前
Lin完成签到 ,获得积分10
5分钟前
我爱科研完成签到,获得积分10
5分钟前
5分钟前
Silence发布了新的文献求助10
5分钟前
Ytgl发布了新的文献求助10
6分钟前
情怀应助科研通管家采纳,获得10
6分钟前
萝卜特乐发布了新的文献求助10
7分钟前
7分钟前
高分求助中
Technologies supporting mass customization of apparel: A pilot project 600
Izeltabart tapatansine - AdisInsight 500
Chinesen in Europa – Europäer in China: Journalisten, Spione, Studenten 500
Arthur Ewert: A Life for the Comintern 500
China's Relations With Japan 1945-83: The Role of Liao Chengzhi // Kurt Werner Radtke 500
Two Years in Peking 1965-1966: Book 1: Living and Teaching in Mao's China // Reginald Hunt 500
Epigenetic Drug Discovery 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3815770
求助须知:如何正确求助?哪些是违规求助? 3359317
关于积分的说明 10402144
捐赠科研通 3077173
什么是DOI,文献DOI怎么找? 1690198
邀请新用户注册赠送积分活动 813659
科研通“疑难数据库(出版商)”最低求助积分说明 767713