Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review

Huzaifah, M. R. M.; Juraimi, Abdul Shukor; Che’Ya, Nik Norasma; Sulaiman, Nasri; Manaf, Muhammad Noor Hazwan Abd; Ramli, Zaid; Motmainna, Mst.

doi:10.3390/agronomy11091809

Cited by 68 publications

(61 citation statements)

References 97 publications

(124 reference statements)

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“…In computer vision, images with medium and high accuracy have become the data types commonly used by researchers, especially in species identification and classification in agriculture, such as agricultural vegetation classification, land use classification, crop classification, tree species identification, etc., ( Roslim et al, 2021 ; Chen Z. et al, 2021 ; Li et al, 2021 ; Yan et al, 2021 ). To solve the common problems of zoom sensing image segmentation algorithms, such as poor robustness, easy loss of edge information and narrow scope of application, the core task of zoom sensing image target detection is to judge whether there is a target in zoom sensing images and to detect, segment, extract and classify it.…”

Section: Related Workmentioning

confidence: 99%

Biomimetic Vision for Zoom Object Detection Based on Improved Vertical Grid Number YOLO Algorithm

Shen

Shi

Ren

et al. 2022

Front. Bioeng. Biotechnol.

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With the development of bionic computer vision for images processing, researchers have easily obtained high-resolution zoom sensing images. The development of drones equipped with high-definition cameras has greatly increased the sample size and image segmentation and target detection are important links during the process of image information. As biomimetic remote sensing images are usually prone to blur distortion and distortion in the imaging, transmission and processing stages, this paper improves the vertical grid number of the YOLO algorithm. Firstly, the light and shade of a high-resolution zoom sensing image were abstracted, and the grey-level cooccurrence matrix extracted feature parameters to quantitatively describe the texture characteristics of the zoom sensing image. The Simple Linear Iterative Clustering (SLIC) superpixel segmentation method was used to achieve the segmentation of light/dark scenes, and the saliency area was obtained. Secondly, a high-resolution zoom sensing image model for segmenting light and dark scenes was established to made the dataset meet the recognition standard. Due to the refraction of the light passing through the lens and other factors, the difference of the contour boundary light and dark value between the target pixel and the background pixel would make it difficult to detect the target, and the pixels of the main part of the separated image would be sharper for edge detection. Thirdly, a YOLO algorithm with an improved vertical grid number was proposed to detect the target in real time on the processed superpixel image array. The adjusted aspect ratio of the target in the remote sensing image modified the number of vertical grids in the YOLO network structure by using 20 convolutional layers and five maximum aggregation layers, which was more accurately adapted to “short and coarse” of the identified object in the information density. Finally, through comparison with the improved algorithm and other mainstream algorithms in different environments, the test results on the aid dataset showed that in the target detection of high spatial resolution zoom sensing images, the algorithm in this paper showed higher accuracy than the YOLO algorithm and had real-time performance and detection accuracy.

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Section: Related Workmentioning

confidence: 99%

Biomimetic Vision for Zoom Object Detection Based on Improved Vertical Grid Number YOLO Algorithm

Shen

Shi

Ren

et al. 2022

Front. Bioeng. Biotechnol.

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“…These UAVs can also be equipped with multispectral cameras, which provide more information than an RGB digital image because they record spectral bands not visible to the human eye, such as near infrared (NIR), and provide data on things such as visible light reflectance and vegetation indices [15]. Multispectral imaging initiated by sensors compute reflected energy within several specific sections or are recognized as bands of the electromagnetic spectrum and utilized about tens of discrete spectral bands for image processing [17]. This multispectral image can be taken from a UAV at usually 60 and 70 m height, depending on the type of crops and location [18].…”

Section: Unmanned Aerial Vehicle (Uav) and Weed Detection Using Multi...mentioning

confidence: 99%

Weed Detection in Rice Fields Using UAV and Multispectral Aerial Imagery

Rosle

Sulaiman

Che’Ya

et al. 2022

The 1st International Online Conference on Agriculture—Advances in Agricultural Science and Technology

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“…al. [80] found that the artificial intelligent (AI) can be used to detect the weeds patches in the rice field. Thus, the used of UAV can help to gain the highest spatial and spectral resolution in the field.…”

Section: Effect Of Spatial and Spectral Resolutions On Weed Detectionmentioning

confidence: 99%

How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?

et al. 2021

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Weeds are among the most harmful abiotic factors in agriculture, triggering significant yield loss worldwide. Remote sensing can detect and map the presence of weeds in various spectral, spatial, and temporal resolutions. This review aims to show the current and future trends of UAV applications in weed detection in the crop field. This study systematically searched the original articles published from 1 January 2016 to 18 June 2021 in the databases of Scopus, ScienceDirect, Commonwealth Agricultural Bureaux (CAB) Direct, and Web of Science (WoS) using Boolean string: “weed” AND “Unmanned Aerial Vehicle” OR “UAV” OR “drone”. Out of the papers identified, 144 eligible studies did meet our inclusion criteria and were evaluated. Most of the studies (i.e., 27.42%) on weed detection were carried out during the seedling stage of the growing cycle for the crop. Most of the weed images were captured using red, green, and blue (RGB) camera, i.e., 48.28% and main classification algorithm was machine learning techniques, i.e., 47.90%. This review initially highlighted articles from the literature that includes the crops’ typical phenology stage, reference data, type of sensor/camera, classification methods, and current UAV applications in detecting and mapping weed for different types of crop. This study then provides an overview of the advantages and disadvantages of each sensor and algorithm and tries to identify research gaps by providing a brief outlook at the potential areas of research concerning the benefit of this technology in agricultural industries. Integrated weed management, coupled with UAV application improves weed monitoring in a more efficient and environmentally-friendly way. Overall, this review demonstrates the scientific information required to achieve sustainable weed management, so as to implement UAV platform in the real agricultural contexts.

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Using Remote Sensing and an Unmanned Aerial System for Weed Management in Agricultural Crops: A Review

Cited by 68 publications

References 97 publications

Biomimetic Vision for Zoom Object Detection Based on Improved Vertical Grid Number YOLO Algorithm

Biomimetic Vision for Zoom Object Detection Based on Improved Vertical Grid Number YOLO Algorithm

Weed Detection in Rice Fields Using UAV and Multispectral Aerial Imagery

How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?

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