Weed Classification Based on Statistical Features from Gabor Transform Magnitude

Zaman, ‪Mohd Hairi Mohd; Mustaza, Seri Mastura; Ibrahim, Mohd Faisal; Zulkifley, Mohd Asyraf; Mustafa, Mohd. Marzuki

doi:10.1109/dasa53625.2021.9681930

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…░ Table 3 The 19×19 window for two-class classification result was also compared to a previously reported method, which is the Gabor Filter [20] and a combination of the Gabor filter and Gradient Field Distribution (GFD) method based on the work by Ishak et. al [3] as shown in Table 3.…”

Section: ░ Table 2: Effect Of Window Size On the Accuracy Of Classifi...mentioning

confidence: 99%

Directional Shape Feature Extraction Using Modified Line Filter Technique for Weed Classification

Mustaza

Ibrahim

Zaman

et al. 2022

IJEER

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Precision agriculture is gaining attention as it employs modern technologies and intelligence for automation in agricultural practices. In the area of weed management, automation is advantageous to select the appropriate herbicide and manage the amount used, which consequently reduced the cost and minimizes the environmental impact. Selective spraying using a sprayer boom can be implemented using automatic detection of weed type. This paper presents a weed classification method based on a modified line filter image analysis technique that can effectively detect the morphological differences, mainly directional shape features, between two types of weeds. After the result for binary classification has been verified, a third dataset is introduced which is mixed leaves which consists of an approximately balanced amount of broadleaves and narrow leaves. The weed images were pre-processed using the adaptive histogram method and difference of Gaussian to improve the image contrast and delineate the edges of the weed. The images were then processed using the proposed modified line filter feature extraction technique. The filter is based on the evaluation of pixel response that corresponds to the pre-defined lines at different orientations from 0o to 360o. The pixel strength of each line was compared to determine the overall response of the filter. The proposed method achieved around 97% classification, superior compared to previously reported methods such as Gabor wavelet as well as a combination of Gabor and Gradient Field Distribution (GFD).

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Section: ░ Table 2: Effect Of Window Size On the Accuracy Of Classifi...mentioning

confidence: 99%

Directional Shape Feature Extraction Using Modified Line Filter Technique for Weed Classification

Mustaza

Ibrahim

Zaman

et al. 2022

IJEER

Self Cite

View full text Add to dashboard Cite

show abstract

An efficient deep learning with a big data-based cotton plant monitoring system

Stephen,

Arumugam,

Arumugam

2023

Int. j. inf. tecnol.

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Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review

Qu,

2024

Agronomy

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Weeds and crops engage in a relentless battle for the same resources, leading to potential reductions in crop yields and increased agricultural costs. Traditional methods of weed control, such as heavy herbicide use, come with the drawback of promoting weed resistance and environmental pollution. As the demand for pollution-free and organic agricultural products rises, there is a pressing need for innovative solutions. The emergence of smart agricultural equipment, including intelligent robots, unmanned aerial vehicles and satellite technology, proves to be pivotal in addressing weed-related challenges. The effectiveness of smart agricultural equipment, however, hinges on accurate detection, a task influenced by various factors, like growth stages, environmental conditions and shading. To achieve precise crop identification, it is essential to employ suitable sensors and optimized algorithms. Deep learning plays a crucial role in enhancing weed recognition accuracy. This advancement enables targeted actions such as minimal pesticide spraying or precise laser excision of weeds, effectively reducing the overall cost of agricultural production. This paper provides a thorough overview of the application of deep learning for crop and weed recognition in smart agricultural equipment. Starting with an overview of intelligent agricultural tools, sensors and identification algorithms, the discussion delves into instructive examples, showcasing the technology’s prowess in distinguishing between weeds and crops. The narrative highlights recent breakthroughs in automated technologies for precision plant identification while acknowledging existing challenges and proposing prospects. By marrying cutting-edge technology with sustainable agricultural practices, the adoption of intelligent equipment presents a promising path toward efficient and eco-friendly weed management in modern agriculture.

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Weed Classification Based on Statistical Features from Gabor Transform Magnitude

Cited by 3 publications

References 21 publications

Directional Shape Feature Extraction Using Modified Line Filter Technique for Weed Classification

Directional Shape Feature Extraction Using Modified Line Filter Technique for Weed Classification

An efficient deep learning with a big data-based cotton plant monitoring system

Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review

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