On the Development of the Automatic Weed Detection Tool

Skacev, Hristo; Micovic, Andrej; Gutic, Bojan; Dotilic, Dusan; Vesić, Ana; Ignjatovic, Vuk; Lakicevic, Sava; Jakovljević, Marina Marjanović; Živković, Miodrag

doi:10.1109/zinc50678.2020.9161802

Cited by 3 publications

(2 citation statements)

References 1 publication

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“…In [ 33 ], the authors introduce the concept of positive (weed present) and negative (weed not present) images. They employ drone-acquired images of ‘black-grass’ and ‘common chickweed’ for the positive class and ‘wheat’, ‘maize’, and ‘sugar beet’ for the negative class.…”

Section: Summary Of Identified Articles In Slrmentioning

confidence: 99%

“…Through some initial searches, we determined that DL applications in research for weed detection have increased considerably since 2015, and they primarily use convolutional neural networks (CNNs) and their variants such as SegNet, GoogLeNet, ResNet, DetectNet, and VGGNet [ 30 , 31 ]. Though some survey articles have been published since 2015 on DL applications for weed detection [ 32 , 33 ], they lack proper SLR.…”

Section: Introductionmentioning

confidence: 99%

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Weed Detection Using Deep Learning: A Systematic Literature Review

Murad

Mahmood

Forkan

et al. 2023

Sensors

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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.

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Section: Summary Of Identified Articles In Slrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Weed Detection Using Deep Learning: A Systematic Literature Review

Murad

Mahmood

Forkan

et al. 2023

Sensors

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Weedbot: Weed Easy Extraction Using Deep Learning with a Robotic System

Ilangovan,

Meena,

Sharmila Begum

2024

J. Phys.: Conf. Ser.

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Weeds refer to plants that a farmer does not intentionally cultivate because they interfere with or limit the growth and yield of crops. Due to their fast reproduction and spreading, weeds are well-known for quickly increasing in population over large areas. From the very beginning of the agricultural revolution, weed control used to be done by hand but as time and technology advanced so did methods for managing unwanted plants. Human Intervention or using a single-row weed cutter, weeds are removed from fields. Due to the difficulties of eliminating the background class from the image and increasing the accuracy of the navigation system of the bot, as well as the fact that it does not support the technique of extracting weeds from paddy fields, a vehicle method for removing weeds from the field has not been created. These difficulties have been tackled by combining computer vision with automation, minimizing the need for human exertion. The project aims to create a robotic chopper that deal with weeds and employ CNN (Convolutional Neural Network) algorithm for image categorization of weeds and crops. We use image processing techniques in the proposed system to identify weeds. The system is trained with 590 images of weeds and crops, and testing is performed with 254 images. The system obtained an accuracy of 80% towards processing weeds and then chopped by the robotic arm. The robotic arm cutter that has great precision and is simple to operate, inexpensive and does not require hazardous chemicals.

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Tarımda Drone Kullanımı ve Geleceği

Özgüven

ALTAŞ

GÜVEN

et al. 2022

Ordu Üniversitesi Bilim Ve Teknoloji Dergisi

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Agriculture is both a vital sector of activity for the sustainability of life and strategic field of activity for provides raw materials to non-agricultural sectors and contributes to national income and employment. The use of new techniques or devices in agriculture, which emerged with the rapid development of technology, makes agricultural applications easier and more effective. The use of drones in agriculture, which is one of the most popular technological developments in recent years, has become widespread and its use is increasing even more with the addition of new application areas. The popularity of drones and their use in agriculture also attract the attention of those from different disciplines other than agriculture. Due to the insufficient technical knowledge of those in different disciplines on agriculture, false information or ineffective use of drones in agriculture may occur. In this study, information is given about the drone and its components, the advantages and disadvantages of the drone, the cameras and sensors that can be used with the drone. Then, the use of drones in agriculture today is explained with sample applications and predictions are presented with the use of drones in agriculture in the future. In addition, explanations were made about the use of drones in agriculture, some misinformation and ineffective use.

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On the Development of the Automatic Weed Detection Tool

Cited by 3 publications

References 1 publication

Weed Detection Using Deep Learning: A Systematic Literature Review

Weed Detection Using Deep Learning: A Systematic Literature Review

Weedbot: Weed Easy Extraction Using Deep Learning with a Robotic System

Tarımda Drone Kullanımı ve Geleceği

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