A Structured Image Processing Operation Library to Automatically Isolate Weeds and Crops

Ashqer, Yaqeen Salatneh; Bikdash, Marwan; Liang, Chyi‐Lyi

doi:10.1109/southeastcon42311.2019.9020595

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…In this section, we summarize the findings of our surveys on application articles and literature reviews. For convenience, we merged the single paper, which proposed a framework [ 82 ] into the application articles (i.e., implementation) category.…”

Section: Review Of Weed Detection Algorithmsmentioning

confidence: 99%

“…In [ 82 ], authors create and implement a framework called the Image Processing Operation (IPO) library for the classification of weeds. IPO stores information about weeds and crops in JSON format which are then automatically converted to MATLAB functions to perform weed discrimination, with the option to add personalized user-defined functions.…”

Section: Summary Of Identified Articles In Slrmentioning

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: Review Of Weed Detection Algorithmsmentioning

confidence: 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

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“…The final colored isolated image is shown in Figure3C. A more detailed discussion of the isolation algorithm is presented in our previous workAshqer et al (2019) The output image from isolation will be used for feature calculations.…”

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confidence: 99%

Using Realistic Images for Plant Classification and Effect on Classification Difficulty

Ashqer,

Bikdash,

Liang

2024

Preprint

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We proposed a hybrid computer-vision framework that distinguishes different plant species (such as radish and weeds) that combines machine learning methods (such as Support Vector Machine (SVM) and Random Forest (RF) classifiers) with application-specific features and image processing methods such as our own plant leaf isolation algorithm. The designed features include geometrical features that are sensitive to plant shape, as well as moment- invariant and texture features. The accuracy obtained using the combination of the designed features, and the isolation algorithm was 81.1% using SVM and 88.4% using Random Forest. We used 10-fold cross-validation to illustrate the importance of designing and selecting good features. We compared our designs with generic deep neural networks. We also compared our features with other features, such as SURF features classification, and our methods were more robust and produced better results. Throughout, we used realistic images obtained in the field, where the quality of the images depends on many factors such as lighting, seasons, occlusions, etc. We therefore include a careful discussion of the difficulty of classification problems and its dependence on the quality of images, and we propose computable definitions of problem difficulty, robustness, level of corruption, and degree of performance degradation due to corruption in the context of precision agriculture.

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