Seedling Growth Stress Quantification Based on Environmental Factors Using Sensor Fusion and Image Processing
Understanding the diverse environmental influences on seedling growth is critical for maximizing yields. The need for a more comprehensive understanding of how various environmental factors affect seedling growth is required. Integrating sensor data and image processing techniques offers a promising...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Horticulturae |
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| Online Access: | https://www.mdpi.com/2311-7524/10/2/186 |
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| author | Sumaiya Islam Md Nasim Reza Shahriar Ahmed Samsuzzaman Yeon Jin Cho Dong Hee Noh Sun-Ok Chung |
| author_facet | Sumaiya Islam Md Nasim Reza Shahriar Ahmed Samsuzzaman Yeon Jin Cho Dong Hee Noh Sun-Ok Chung |
| author_sort | Sumaiya Islam |
| collection | DOAJ |
| description | Understanding the diverse environmental influences on seedling growth is critical for maximizing yields. The need for a more comprehensive understanding of how various environmental factors affect seedling growth is required. Integrating sensor data and image processing techniques offers a promising approach to accurately detect stress symptoms and uncover hidden patterns, enhancing the comprehension of seedling responses to environmental factors. The objective of this study was to quantify environmental stress symptoms for six seedling varieties using image-extracted feature characteristics. Three sensors were used: an RGB camera for color, shape, and size information; a thermal camera for measuring canopy temperature; and a depth camera for providing seedling height from the image-extracted features. Six seedling varieties were grown under controlled conditions, with variations in temperature, light intensity, nutrients, and water supply, while daily automated imaging was conducted for two weeks. Key seedling features, including leaf area, leaf color, seedling height, and canopy temperature, were derived through image processing techniques. These features were then employed to quantify stress symptoms for each seedling type. The analysis of stress effects on the six seedling varieties revealed distinct responses to environmental stressors. Integration of color, size, and shape parameters established a visual hierarchy: pepper and pak choi seedlings showed a good response, cucumber seedlings showed a milder response, and lettuce and tomato seedlings displayed an intermediate response. Pepper and tomato seedlings exhibited a wide range of growth stress symptoms, at 13.00% to 83.33% and 2.96% to 70.01%, respectively, indicating considerable variability in their reactions to environmental stressors. The suggested classification approach provides valuable groundwork for advancing stress monitoring and enabling growers to optimize environmental conditions. |
| first_indexed | 2024-03-07T22:30:33Z |
| format | Article |
| id | doaj.art-608f551e0f1a4b6593e30a29f7763958 |
| institution | Directory Open Access Journal |
| issn | 2311-7524 |
| language | English |
| last_indexed | 2024-03-07T22:30:33Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Horticulturae |
| spelling | doaj.art-608f551e0f1a4b6593e30a29f77639582024-02-23T15:18:51ZengMDPI AGHorticulturae2311-75242024-02-0110218610.3390/horticulturae10020186Seedling Growth Stress Quantification Based on Environmental Factors Using Sensor Fusion and Image ProcessingSumaiya Islam0Md Nasim Reza1Shahriar Ahmed2Samsuzzaman3Yeon Jin Cho4Dong Hee Noh5Sun-Ok Chung6Department of Smart Agricultural Systems, Graduate School, Chungnam National University, Daejeon 34134, Republic of KoreaDepartment of Smart Agricultural Systems, Graduate School, Chungnam National University, Daejeon 34134, Republic of KoreaDepartment of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of KoreaDepartment of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of KoreaJeonnam Agricultural Research Extension Services, Naju 58213, Republic of KoreaJeonbuk Regional Branch, Korea Electronics Technology Institute (KETI), Jeonju 54853, Republic of KoreaDepartment of Smart Agricultural Systems, Graduate School, Chungnam National University, Daejeon 34134, Republic of KoreaUnderstanding the diverse environmental influences on seedling growth is critical for maximizing yields. The need for a more comprehensive understanding of how various environmental factors affect seedling growth is required. Integrating sensor data and image processing techniques offers a promising approach to accurately detect stress symptoms and uncover hidden patterns, enhancing the comprehension of seedling responses to environmental factors. The objective of this study was to quantify environmental stress symptoms for six seedling varieties using image-extracted feature characteristics. Three sensors were used: an RGB camera for color, shape, and size information; a thermal camera for measuring canopy temperature; and a depth camera for providing seedling height from the image-extracted features. Six seedling varieties were grown under controlled conditions, with variations in temperature, light intensity, nutrients, and water supply, while daily automated imaging was conducted for two weeks. Key seedling features, including leaf area, leaf color, seedling height, and canopy temperature, were derived through image processing techniques. These features were then employed to quantify stress symptoms for each seedling type. The analysis of stress effects on the six seedling varieties revealed distinct responses to environmental stressors. Integration of color, size, and shape parameters established a visual hierarchy: pepper and pak choi seedlings showed a good response, cucumber seedlings showed a milder response, and lettuce and tomato seedlings displayed an intermediate response. Pepper and tomato seedlings exhibited a wide range of growth stress symptoms, at 13.00% to 83.33% and 2.96% to 70.01%, respectively, indicating considerable variability in their reactions to environmental stressors. The suggested classification approach provides valuable groundwork for advancing stress monitoring and enabling growers to optimize environmental conditions.https://www.mdpi.com/2311-7524/10/2/186smart horticultureseedling growthplant growth stressimage processingsensor fusion |
| spellingShingle | Sumaiya Islam Md Nasim Reza Shahriar Ahmed Samsuzzaman Yeon Jin Cho Dong Hee Noh Sun-Ok Chung Seedling Growth Stress Quantification Based on Environmental Factors Using Sensor Fusion and Image Processing Horticulturae smart horticulture seedling growth plant growth stress image processing sensor fusion |
| title | Seedling Growth Stress Quantification Based on Environmental Factors Using Sensor Fusion and Image Processing |
| title_full | Seedling Growth Stress Quantification Based on Environmental Factors Using Sensor Fusion and Image Processing |
| title_fullStr | Seedling Growth Stress Quantification Based on Environmental Factors Using Sensor Fusion and Image Processing |
| title_full_unstemmed | Seedling Growth Stress Quantification Based on Environmental Factors Using Sensor Fusion and Image Processing |
| title_short | Seedling Growth Stress Quantification Based on Environmental Factors Using Sensor Fusion and Image Processing |
| title_sort | seedling growth stress quantification based on environmental factors using sensor fusion and image processing |
| topic | smart horticulture seedling growth plant growth stress image processing sensor fusion |
| url | https://www.mdpi.com/2311-7524/10/2/186 |
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