Feature aggregation for nutrient deficiency identification in chili based on machine learning
Macronutrient deficiency inhibits the growth and development of chili plants. One of the non-destructive methods that plays a role in processing plant image data based on specific characteristics is computer vision. This study uses 5166 image data after augmentation process for six plant health cond...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2023-06-01
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| Series: | Artificial Intelligence in Agriculture |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589721723000156 |
| _version_ | 1797791902555176960 |
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| author | Deffa Rahadiyan Sri Hartati Wahyono Andri Prima Nugroho |
| author_facet | Deffa Rahadiyan Sri Hartati Wahyono Andri Prima Nugroho |
| author_sort | Deffa Rahadiyan |
| collection | DOAJ |
| description | Macronutrient deficiency inhibits the growth and development of chili plants. One of the non-destructive methods that plays a role in processing plant image data based on specific characteristics is computer vision. This study uses 5166 image data after augmentation process for six plant health conditions. But the analysis of one feature cannot represent plant health condition. Therefore, a careful combination of features is required. This study combines three types of features with HSV and RGB for color, GLCM and LBP for texture, and Hu moments and centroid distance for shapes. Each feature and its combination are trained and tested using the same MLP architecture. The combination of RGB, GLCM, Hu moments, and Distance of centroid features results the best performance. In addition, this study compares the MLP architecture used with previous studies such as SVM, Random Forest Technique, Naive Bayes, and CNN. CNN produced the best performance, followed by SVM and MLP, with accuracy reaching 97.76%, 90.55% and 89.70%, respectively. Although MLP has lower accuracy than CNN, the model for identifying plant health conditions has a reasonably good success rate to be applied in a simple agricultural environment. |
| first_indexed | 2024-03-13T02:25:25Z |
| format | Article |
| id | doaj.art-b83385c903a5465cb1828fd45282af49 |
| institution | Directory Open Access Journal |
| issn | 2589-7217 |
| language | English |
| last_indexed | 2024-03-13T02:25:25Z |
| publishDate | 2023-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Agriculture |
| spelling | doaj.art-b83385c903a5465cb1828fd45282af492023-06-30T04:22:40ZengKeAi Communications Co., Ltd.Artificial Intelligence in Agriculture2589-72172023-06-0187790Feature aggregation for nutrient deficiency identification in chili based on machine learningDeffa Rahadiyan0Sri Hartati1 Wahyono2Andri Prima Nugroho3Department of Computer Science and Electronics, Universitas Gadjah Mada (UGM), Yogyakarta, IndonesiaDepartment of Computer Science and Electronics, Universitas Gadjah Mada (UGM), Yogyakarta, Indonesia; Corresponding authors.Department of Computer Science and Electronics, Universitas Gadjah Mada (UGM), Yogyakarta, IndonesiaDepartment of Agricultural and Biosystems Engineering, Universitas Gadjah Mada (UGM), Yogyakarta, IndonesiaMacronutrient deficiency inhibits the growth and development of chili plants. One of the non-destructive methods that plays a role in processing plant image data based on specific characteristics is computer vision. This study uses 5166 image data after augmentation process for six plant health conditions. But the analysis of one feature cannot represent plant health condition. Therefore, a careful combination of features is required. This study combines three types of features with HSV and RGB for color, GLCM and LBP for texture, and Hu moments and centroid distance for shapes. Each feature and its combination are trained and tested using the same MLP architecture. The combination of RGB, GLCM, Hu moments, and Distance of centroid features results the best performance. In addition, this study compares the MLP architecture used with previous studies such as SVM, Random Forest Technique, Naive Bayes, and CNN. CNN produced the best performance, followed by SVM and MLP, with accuracy reaching 97.76%, 90.55% and 89.70%, respectively. Although MLP has lower accuracy than CNN, the model for identifying plant health conditions has a reasonably good success rate to be applied in a simple agricultural environment.http://www.sciencedirect.com/science/article/pii/S2589721723000156Feature CombinationMulti-Layer PerceptronClassifierNutrient deficiency |
| spellingShingle | Deffa Rahadiyan Sri Hartati Wahyono Andri Prima Nugroho Feature aggregation for nutrient deficiency identification in chili based on machine learning Artificial Intelligence in Agriculture Feature Combination Multi-Layer Perceptron Classifier Nutrient deficiency |
| title | Feature aggregation for nutrient deficiency identification in chili based on machine learning |
| title_full | Feature aggregation for nutrient deficiency identification in chili based on machine learning |
| title_fullStr | Feature aggregation for nutrient deficiency identification in chili based on machine learning |
| title_full_unstemmed | Feature aggregation for nutrient deficiency identification in chili based on machine learning |
| title_short | Feature aggregation for nutrient deficiency identification in chili based on machine learning |
| title_sort | feature aggregation for nutrient deficiency identification in chili based on machine learning |
| topic | Feature Combination Multi-Layer Perceptron Classifier Nutrient deficiency |
| url | http://www.sciencedirect.com/science/article/pii/S2589721723000156 |
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