Cabbage disease detection system using k-NN algorithm
Identification of plant diseases is key to avoiding losses in agricultural yields and product quantities. Plant disease study means the study of disease patterns that can be visually seen on plants. The main objective of this research is to develop a prototype system with the help of machine learnin...
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| Format: | Academic Exercise |
| Language: | English English |
| Published: |
2022
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| Online Access: | https://eprints.ums.edu.my/id/eprint/33210/1/Cabbage%20Disease%20Detection%20System%20Using%20K-nn%20Algorithm.24pages.pdf https://eprints.ums.edu.my/id/eprint/33210/2/Cabbage%20Disease%20Detection%20System%20Using%20K-nn%20Algorithm.pdf |
| _version_ | 1796911366923091968 |
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| author | Mohamad Ainuddin Sahimat |
| author_facet | Mohamad Ainuddin Sahimat |
| author_sort | Mohamad Ainuddin Sahimat |
| collection | UMS |
| description | Identification of plant diseases is key to avoiding losses in agricultural yields and product quantities. Plant disease study means the study of disease patterns that can be visually seen on plants. The main objective of this research is to develop a prototype system with the help of machine learning to detect cabbage diseases which are Alternaria Leaf Spot disease, Mosaic Virus disease, Downy Fungus disease, Bacterial Soft Rot disease, and Black Rot disease . It is very difficult to monitor plant diseases manually because it requires a large amount of work, deep expertise in plant diseases, and also requires excessive processing time. The image sample pixel will need to convert first using an otsu method and histogram method in the image processing and segmentation technique. Then, the segmented cabbage sample will use the GLCM method for feature extraction. It is a method of extracting second-order statistical texture features to detect diseases more efficiently. Finally, the KNN algorithm will be used to classify the disease based on sample nature and a cabbage disease data set. Consequently, by employing the KNN technique, the cabbage diseases are recognized at average 90% percent accuracy rates. This prototype has a very great potential to be further improved in the future. |
| first_indexed | 2024-03-06T03:17:44Z |
| format | Academic Exercise |
| id | ums.eprints-33210 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:17:44Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ums.eprints-332102022-07-18T03:30:41Z https://eprints.ums.edu.my/id/eprint/33210/ Cabbage disease detection system using k-NN algorithm Mohamad Ainuddin Sahimat QA76.75-76.765 Computer software SB1-1110 Plant culture Identification of plant diseases is key to avoiding losses in agricultural yields and product quantities. Plant disease study means the study of disease patterns that can be visually seen on plants. The main objective of this research is to develop a prototype system with the help of machine learning to detect cabbage diseases which are Alternaria Leaf Spot disease, Mosaic Virus disease, Downy Fungus disease, Bacterial Soft Rot disease, and Black Rot disease . It is very difficult to monitor plant diseases manually because it requires a large amount of work, deep expertise in plant diseases, and also requires excessive processing time. The image sample pixel will need to convert first using an otsu method and histogram method in the image processing and segmentation technique. Then, the segmented cabbage sample will use the GLCM method for feature extraction. It is a method of extracting second-order statistical texture features to detect diseases more efficiently. Finally, the KNN algorithm will be used to classify the disease based on sample nature and a cabbage disease data set. Consequently, by employing the KNN technique, the cabbage diseases are recognized at average 90% percent accuracy rates. This prototype has a very great potential to be further improved in the future. 2022 Academic Exercise NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/33210/1/Cabbage%20Disease%20Detection%20System%20Using%20K-nn%20Algorithm.24pages.pdf text en https://eprints.ums.edu.my/id/eprint/33210/2/Cabbage%20Disease%20Detection%20System%20Using%20K-nn%20Algorithm.pdf Mohamad Ainuddin Sahimat (2022) Cabbage disease detection system using k-NN algorithm. Universiti Malaysia Sabah. (Unpublished) |
| spellingShingle | QA76.75-76.765 Computer software SB1-1110 Plant culture Mohamad Ainuddin Sahimat Cabbage disease detection system using k-NN algorithm |
| title | Cabbage disease detection system using k-NN algorithm |
| title_full | Cabbage disease detection system using k-NN algorithm |
| title_fullStr | Cabbage disease detection system using k-NN algorithm |
| title_full_unstemmed | Cabbage disease detection system using k-NN algorithm |
| title_short | Cabbage disease detection system using k-NN algorithm |
| title_sort | cabbage disease detection system using k nn algorithm |
| topic | QA76.75-76.765 Computer software SB1-1110 Plant culture |
| url | https://eprints.ums.edu.my/id/eprint/33210/1/Cabbage%20Disease%20Detection%20System%20Using%20K-nn%20Algorithm.24pages.pdf https://eprints.ums.edu.my/id/eprint/33210/2/Cabbage%20Disease%20Detection%20System%20Using%20K-nn%20Algorithm.pdf |
| work_keys_str_mv | AT mohamadainuddinsahimat cabbagediseasedetectionsystemusingknnalgorithm |