ACADSTAFF UGM
| Title | : | An Overview of Identification and Estimation Nutrient on Plants Leaves Image Using Machine Learning |
| Author | : |
DEFFA RAHADIYAN (1) Prof. Dra. Sri Hartati, M.Sc., Ph.D. (2) Wahyono, Ph.D. (3) Ir. Andri Prima Nugroho, S.T.P., M.Sc., Ph.D., IPU., ASEAN Eng. (4) |
| Date | : | 31 2022 |
| Keyword | : | Classifier,Deep Learning, Feature Extraction,Image Processing,Macronutrient Deficiency Classifier,Deep Learning, Feature Extraction,Image Processing,Macronutrient Deficiency |
| Abstract | : | Lack of nutrients affects plant growth and causes plant damage. Deficiency of macronutrient such as nitrogen, potassium, calcium, and phosphorus are big problem for agriculture and its prevention will be very useful for agro-industry. The destructive methods for identifying nutrient deficiencies are soil analysis, plant tissue analysis which requires expert knowledge and laboratory testing, but the test results are not necessarily accurate due to human error. Non-destructive methods such as computer vision can help digital farmer who lack knowledge of botany to identify macronutrient deficiencies. Identification and estimation of macronutrient deficiencies using computer vision consists of several stages, namely data acquisition, preprocessing, segmentation, feature extraction, to identification and estimation method. Image data in the form of RGB, NIR, etc. Several researchers have conducted studies to identify and estimate macronutrient deficiencies using different method. These methods are traditional methods such as rule based to K-Nearest Neighbor (KNN), Linear Regression, Artificial Neural Networks (ANN), Deep Learning with various architectures, and others. Several studies have their respective results and limitations, therefore this paper focuses on reviewing current research developments and providing an overview of the work and challenges in the future. The result of the comparative study is that Deep Learning such as CNN is a promising method because most studies can identify macronutrient deficiencies with an accuracy of more than 80%. However, there are still some challenges such as overcoming overlapping images with complex backgrounds, identification of multi-deficiencies, and estimation of the content of each macronutrient in RGB images. © 2022 Little Lion Scientific. All rights reserved. |
| Group of Knowledge | : | Ilmu Komputer |
| Original Language | : | English |
| Level | : | Internasional |
| Status | : |
Published
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