Smart Agricultural Machine with a Computer Vision-Based Weeding and Variable-Rate Irrigation Scheme
Open accessAbstract
This paper proposes a scheme that combines computer vision and multi-tasking processes to develop a small-scale smart agricultural machine that can automatically weed and perform variable rate irrigation within a cultivated field. Image processing methods such as HSV (hue (H), saturation (S), value (V)) color conversion, estimation of thresholds during the image binary segmentation process, and morphology operator procedures are used to confirm the position of the plant and weeds, and those results are used to perform weeding and watering operations. Furthermore, the data on the wet distribution area of surface soil (WDAS) and the moisture content of the deep soil is provided to a fuzzy logic controller, which drives pumps to perform variable rate irrigation and to achieve water savings. The proposed system has been implemented in small machines and the experimental results show that the system can classify plant and weeds in real time with an average classification rate of 90% or higher. This allows the machine to do weeding and watering while maintaining the moisture content of the deep soil at 80 ± 10% and an average weeding rate of 90%.
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The system can classify plants and weeds in real time with an average classification rate of 90% or higher.
“The system can classify plants and weeds in real time with an average classification rate of 90% or higher.”
From Advanced Applications of Industrial Robotics: New Trends and Possibilities (2021) · cites this paper as [105]
Section 2.1.1 describes the system’s weed/plant classification method, and Section 3.2 describes real-time observation of crops and weeds. The retrieved main-text passages do not establish an average classification rate of 90% or higher; that figure appears only in the Abstract, which is excluded as evidence under the stated rubric. (LLM, conf=0.73)
Experimental — accuracy not yet measuredThis allows the machine to perform weeding and watering while maintaining the moisture content of the deep soil at 80 ± 10% and an average weeding rate of 90%.
“This allows the machine to perform weeding and watering while maintaining the moisture content of the deep soil at 80 ± 10% and an average weeding rate of 90%.”
From Advanced Applications of Industrial Robotics: New Trends and Possibilities (2021) · cites this paper as [105]
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Research works printed but not linked: 32
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Advanced Applications of Industrial Robotics: New Trends and Possibilities
Dzedzickis, A., Subačiūtė-Žemaitienė, J., Šutinys, E., Samukaitė-Bubnienė, U., & Bučinskas, V. (2021) · Applied Sciences · doi:10.3390/app12010135 · Cited by 228 · see citing works
- Authors
- Chang, C.-L., & Lin, K.-M.
- Affiliations
- National Pingtung University of Science and Technology (TW)
- Corresponding author
- Chang, Chung-Liang
- ORCID
- Chang, Chung-Liang
- Venue
- Robotics
- Publisher
- MDPI AG
- Published
- 2018-07-19
- Volume
- 7
- Issue
- 3
- Pages
- 38
- ISSN
- 2218-6581 (ISSN-L)
- OpenAlex
- W2883454019
- Cited by
- 112
- References
- 0
- Citations by year
- 2026: 9 · 2025: 14 · 2024: 19 · 2023: 21 · 2022: 20 · 2021: 12 · 2020: 10 · 2019: 7
- FWCI
- 6.12
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Robotics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Science and Technology (MOST 106-2221-E-020-011)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Physical Sciences › Engineering › Mechanical Engineering › Agricultural Engineering and Mechanization
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- agricultural robotics, variable rate irrigation, computer vision, weed detection, plant detection, foreground-background segmentation, morphological operators, fuzzy logic controller, soil moisture, water saving
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