Deep Learning-Based Object Detection System for Identifying Weeds Using UAS Imagery

نویسندگان

چکیده

Current methods of broadcast herbicide application cause a negative environmental and economic impact. Computer vision methods, specifically those related to object detection, have been reported aid in site-specific weed management procedures for targeted within field. However, major challenge developing detection system is the requirement properly annotated database differentiate between weeds crops under field conditions. This research involved creating an 374 red, green, blue (RGB) color images organized into monocot dicot classes. The were acquired from corn soybean plots located north-central Indiana using unmanned aerial (UAS) flown at 30 10 m heights above ground level (AGL). A total 25,560 individual instances manually annotated. consisted four different subsets (Training Image Sets 1–4) train You Only Look Once version 3 (YOLOv3) deep learning model five separate experiments. best results observed with Training Set 4, consisting AGL. For weeds, respectively, average precision (AP) score 91.48 % 86.13% was 25% IoU threshold (AP @ T = 0.25), as well 63.37% 45.13% 50% 0.5). has demonstrated need develop large, databases evaluate models identification It also affirms findings other limited studies utilizing

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Deep learning for class-generic object detection

We investigate the use of deep neural networks for the novel task of class-generic object detection. We show that neural networks originally designed for image recognition can be trained to detect objects within images, regardless of their class, including objects for which no bounding box labels have been provided. In addition, we show that bounding box labels yield a 1% performance increase o...

متن کامل

Using UAS Hyperspatial RGB Imagery for Identifying Beach Zones along the South Texas Coast

Shoreline information is fundamental for understanding coastal dynamics and for implementing environmental policy. The analysis of shoreline variability usually uses a group of shoreline indicators visibly discernible in coastal imagery, such as the seaward vegetation line, wet beach/dry beach line, and instantaneous water line. These indicators partition a beach into four zones: vegetated land...

متن کامل

Concept drift detection in business process logs using deep learning

Process mining provides a bridge between process modeling and analysis on the one hand and data mining on the other hand. Process mining aims at discovering, monitoring, and improving real processes by extracting knowledge from event logs. However, as most business processes change over time (e.g. the effects of new legislation, seasonal effects and etc.), traditional process mining techniques ...

متن کامل

Oil spill detection using in Sentinel-1 satellite images based on Deep learning concepts

Awareness of the marine area is very important for crisis management in the event of an accident. Oil spills are one of the main threats to the marine and coastal environments and seriously affect the marine ecosystem and cause political and environmental concerns because it seriously affects the fragile marine and coastal ecosystem. The rate of discharge of pollutants and its related effects o...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Remote Sensing

سال: 2021

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs13245182