Classification of weed using machine learning techniques: a review—challenges, current and future potential techniques

Al-Badri, Ahmed Husham; Ismail, Nor Azman; Al-Dulaimi, Khamael; Salman, Ghalib Ahmed; Khan, Azmat Ullah; Al-Sabaawi, Aiman; Salam, Md. Sah

doi:10.1007/s41348-022-00612-9

Cited by 30 publications

(18 citation statements)

References 111 publications

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“…Food loss due to weeds amounts to approximately 13.2% annually [9]. Therefore, the accurate and rapid identification of weeds using machine vision enables accurate pesticide application and reduces the dosage of pesticides [7].…”

Section: Weeds Identification and Detectionmentioning

confidence: 99%

“…Existing scholarly investigations have primarily focused on two main areas within the domain of crop phenotyping: the utilization of Multiscale-Deep-Learning [2,3] and the advancements in agriculture IoT technologies [4]. Moreover, specific tasks such as unmanned aerial vehicle (UAV) applications [5], crop yield measurement [6], and weed identification [7] have been the subject of comprehensive analysis. Nevertheless, it is crucial to recognize the interconnectedness of different stages throughout the crop growth cycle.…”

Section: Introductionmentioning

confidence: 99%

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Progress in the Application of CNN-Based Image Classification and Recognition in Whole Crop Growth Cycles

et al. 2023

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The categorization and identification of agricultural imagery constitute the fundamental requisites of contemporary farming practices. Among the various methods employed for image classification and recognition, the convolutional neural network (CNN) stands out as the most extensively utilized and swiftly advancing machine learning technique. Its immense potential for advancing precision agriculture cannot be understated. By comprehensively reviewing the progress made in CNN applications throughout the entire crop growth cycle, this study aims to provide an updated account of these endeavors spanning the years 2020 to 2023. During the seed stage, classification networks are employed to effectively categorize and screen seeds. In the vegetative stage, image classification and recognition play a prominent role, with a diverse range of CNN models being applied, each with its own specific focus. In the reproductive stage, CNN’s application primarily centers around target detection for mechanized harvesting purposes. As for the post-harvest stage, CNN assumes a pivotal role in the screening and grading of harvested products. Ultimately, through a comprehensive analysis of the prevailing research landscape, this study presents the characteristics and trends of current investigations, while outlining the future developmental trajectory of CNN in crop identification and classification.

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Section: Weeds Identification and Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Progress in the Application of CNN-Based Image Classification and Recognition in Whole Crop Growth Cycles

et al. 2023

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“…Sensor-based technologies have been adapted for managing intra-row weeds in crops with wider rows (Kumar et al, 2022) [138] . Cutting-edge technologies, including image segmentation, plant height measurement, machine vision systems, and sensor-based methods, show promising potential in accurately distinguishing crops from weeds (Al-Badri et al, 2022; Hasan et al, 2021 and Teja et al, 2022) [5,40,110] . ~ 16 ~ The integration of sensors, microcontrollers, and computing technologies has provided the way for autonomous guidance systems in agriculture (Pallottino et al, 2019) [140] .…”

Section: Introductionmentioning

confidence: 99%

Advancements in precision control in intra-row weeding: A comprehensive review

Lahre,

Satpathy

2024

Int. J. Res. Agron.

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Sensor-based weed detection is a significant breakthrough in precise weed management that reduces the need for manual work and herbicides. GPS systems have shown promising results, with a single pass covering 30-49% of the intra-row area. Additionally, using an RGB sensor and a laser sensor together can identify the center of a plant, regardless of lighting conditions. Robots have demonstrated the ability to distinguish 99.7% of crop plants in dense outdoor areas with high weed density. These advancements offer opportunities for more effective and environmentally-friendly weed management in different farming systems.

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“…DL techniques are widely used in agriculture, and their applications are increasing as algorithms are improved [8]. Several studies compiling DL applications in weed detection are presented in the works of [9][10][11][12]. Among the different types of DL neural networks is Convolutional Neural Networks (CNN), a type of ANN architecture specially designed to process visual data, such as images and videos.…”

Section: Introductionmentioning

confidence: 99%

Application of Convolutional Neural Networks in Weed Detection and Identification: A Systematic Review

García-Navarrete,

Correa-Guimaraes,

Navas-Gracia

2024

Agriculture

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Weeds are unwanted and invasive plants that proliferate and compete for resources such as space, water, nutrients, and sunlight, affecting the quality and productivity of the desired crops. Weed detection is crucial for the application of precision agriculture methods and for this purpose machine learning techniques can be used, specifically convolutional neural networks (CNN). This study focuses on the search for CNN architectures used to detect and identify weeds in different crops; 61 articles applying CNN architectures were analyzed during the last five years (2019–2023). The results show the used of different devices to acquire the images for training, such as digital cameras, smartphones, and drone cameras. Additionally, the YOLO family and algorithms are the most widely adopted architectures, followed by VGG, ResNet, Faster R-CNN, AlexNet, and MobileNet, respectively. This study provides an update on CNNs that will serve as a starting point for researchers wishing to implement these weed detection and identification techniques.

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Classification of weed using machine learning techniques: a review—challenges, current and future potential techniques

Cited by 30 publications

References 111 publications

Progress in the Application of CNN-Based Image Classification and Recognition in Whole Crop Growth Cycles

Progress in the Application of CNN-Based Image Classification and Recognition in Whole Crop Growth Cycles

Advancements in precision control in intra-row weeding: A comprehensive review

Application of Convolutional Neural Networks in Weed Detection and Identification: A Systematic Review

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