Deep Weed Detector/Classifier Network for Precision Agriculture

Abdulsalam, Mahmoud; Aouf, Nabil

doi:10.1109/med48518.2020.9183325

Cited by 21 publications

(11 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…YOLO models were applied in numerous applications where fast detection was needed, such as pedestrian detection [37], license plate recognition [38], and automatic detection of fabric defects [39]. In agriculture, YOLO application ranges from fruit detection [40][41][42], crop disease identification [43,44], and weed and pest identification [45,46]. The YOLO's fruit detection application was mainly based on apple orchards, and application in vine bunch detection is still missing.…”

Section: Yolo (You Only Look Once) and Frameworkmentioning

confidence: 99%

Automatic Bunch Detection in White Grape Varieties Using YOLOv3, YOLOv4, and YOLOv5 Deep Learning Algorithms

et al. 2022

View full text Add to dashboard Cite

Over the last few years, several Convolutional Neural Networks for object detection have been proposed, characterised by different accuracy and speed. In viticulture, yield estimation and prediction is used for efficient crop management, taking advantage of precision viticulture techniques. Convolutional Neural Networks for object detection represent an alternative methodology for grape yield estimation, which usually relies on manual harvesting of sample plants. In this paper, six versions of the You Only Look Once (YOLO) object detection algorithm (YOLOv3, YOLOv3-tiny, YOLOv4, YOLOv4-tiny, YOLOv5x, and YOLOv5s) were evaluated for real-time bunch detection and counting in grapes. White grape varieties were chosen for this study, as the identification of white berries on a leaf background is trickier than red berries. YOLO models were trained using a heterogeneous dataset populated by images retrieved from open datasets and acquired on the field in several illumination conditions, background, and growth stages. Results have shown that YOLOv5x and YOLOv4 achieved an F1-score of 0.76 and 0.77, respectively, with a detection speed of 31 and 32 FPS. Differently, YOLO5s and YOLOv4-tiny achieved an F1-score of 0.76 and 0.69, respectively, with a detection speed of 61 and 196 FPS. The final YOLOv5x model for bunch number, obtained considering bunch occlusion, was able to estimate the number of bunches per plant with an average error of 13.3% per vine. The best combination of accuracy and speed was achieved by YOLOv4-tiny, which should be considered for real-time grape yield estimation, while YOLOv3 was affected by a False Positive–False Negative compensation, which decreased the RMSE.

show abstract

Section: Yolo (You Only Look Once) and Frameworkmentioning

confidence: 99%

Automatic Bunch Detection in White Grape Varieties Using YOLOv3, YOLOv4, and YOLOv5 Deep Learning Algorithms

et al. 2022

View full text Add to dashboard Cite

show abstract

“…18,37 Most of the time, weeds have about 90% resemblance with the main plant. To contain this, a DNN is used to detect the weeds, similar to our previous work 27 but with modifications to suit this peculiar problem. The network is a cascade of a classification network ResNet-50 and a detection network YOLOv4.…”

Section: Methodsmentioning

confidence: 99%

“…12 Similarly, in our previous work, we combined a classification and detection network for weed detection. 27 This way, we can categorically tell the type of weed and identify a region of interest (ROI) for further processing.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel UAV-integrated deep network detection and relative position estimation approach for weeds

Abdulsalam

Ahiska

Aouf

2023

Proceedings of the Institution of Mechanical Engineers, Part G:

Self Cite

View full text Add to dashboard Cite

This paper aims at presenting a novel monocular vision–based approach for drones to detect multiple type of weeds and estimate their positions autonomously for precision agriculture applications. The methodology is based on classifying and detecting the weeds using a proposed deep neural network architecture, named fused-YOLO on the images acquired from a monocular camera mounted on the unmanned aerial vehicle (UAV) following a predefined elliptical trajectory. The detection/classification is complemented by a new estimation scheme adopting unscented Kalman filter (UKF) to estimate the exact location of the weeds. Bounding boxes are assigned to the detected targets (weeds) such that the centre pixels of the bounding box will represent the centre of the target. The centre pixels are extracted and converted into world coordinates forming azimuth and elevation angles from the target to the UAV, and the proposed estimation scheme is used to extract the positions of the weeds. Experiments were conducted both indoor and outdoor to validate this integrated detection/classification/estimation approach. The errors in terms of misclassification and mispositioning of the weeds estimation were minimum, and the convergence of the position estimation results was short taking into account the affordable platform with cheap sensors used in the experiments.

show abstract

“…In [ 96 ], the authors indicate the limitations of detecting weeds with real-life images in that whole image content has to be fed into deep learning architectures, which sometimes makes it difficult to distinguish weeds from their background like soil. Hence, the authors propose using pre-trained deep learning models, particularly ResNet-50 for classification and YOLO for performance speed-up to achieve an accuracy of 99%.…”

Section: Summary Of Identified Articles In Slrmentioning

confidence: 99%

Weed Detection Using Deep Learning: A Systematic Literature Review

Murad

Mahmood

Forkan

et al. 2023

Sensors

View full text Add to dashboard Cite

Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area.

show abstract

Deep Weed Detector/Classifier Network for Precision Agriculture

Cited by 21 publications

References 12 publications

Automatic Bunch Detection in White Grape Varieties Using YOLOv3, YOLOv4, and YOLOv5 Deep Learning Algorithms

Automatic Bunch Detection in White Grape Varieties Using YOLOv3, YOLOv4, and YOLOv5 Deep Learning Algorithms

A novel UAV-integrated deep network detection and relative position estimation approach for weeds

Weed Detection Using Deep Learning: A Systematic Literature Review

Contact Info

Product

Resources

About