Abstract
Since the number of people worldwide is anticipated to reach 9 billion people by 2050, the agriculture production needs to be increased up to 70% to manage the anticipated increasing of human demand. However, weeds are one of the most harmful factors that negatively impact the crops production, quality, and cause economical loses. Accordingly, automating the weed detection, classification, and counting of weeds per their growth stages will help farmers to choose the appropriate weeds’ controlling techniques. In this paper, UAV was used for collecting a dataset, which consists of four weed (Consolida Regalis) growth stages. Additionally, a deep learning model (YOLOv5) was developed and trained for detecting weed, classifying weed’s growth stages, and counting the number of weeds occurrences in each part of the field. The results report that the best precision (82.7%) is generated by the Yolov5-Large model in detecting and classifying the weed’s growth stages. According to the best performance in terms of recall, Yolov5-sma11 model has the best recall of 79.4%. For counting the instances of weeds per the four growth stages in real-time, Yolov5-sma11 model showes counting time of 0.033 millisecond per frame.
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Claims 3
Claim · model-written summary
Yolov5-Large gave the best precision (82.7%) in detecting and classifying weed growth stages.
- Taxon
- Consolida Regalis
Consolida regalise0 - Outcome
- precisione1
- Direction
- beste1
- Value
- 82.7%e1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In this paper, UAV was used for collecting a dataset, which consists of four weed (Consolida Regalis) growth stages”
e1“The results report that the best precision (82.7%) is generated by the Yolov5-Large model in detecting and classifying the weed’s growth stages”
Claim · model-written summary
Yolov5-sma11 gave the best recall (79.4%) for weed growth stages.
- Taxon
- Consolida RegalisConsolida regalise0
- Outcome
- recalle2
- Direction
- beste2
- Value
- 79.4%e2
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In this paper, UAV was used for collecting a dataset, which consists of four weed (Consolida Regalis) growth stages”
e2“According to the best performance in terms of recall, Yolov5-sma11 model has the best recall of 79.4%”
Claim · model-written summary
Yolov5-sma11 counted weed instances per growth stage at 0.033 millisecond per frame in real time.
- Taxon
- Consolida RegalisConsolida regalise0
- Outcome
- counting timee3
- Value
- 0.033 millisecond per framee3
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In this paper, UAV was used for collecting a dataset, which consists of four weed (Consolida Regalis) growth stages”
e3“For counting the instances of weeds per the four growth stages in real-time, Yolov5-sma11 model showes counting time of 0.033 millisecond per frame”