Using Optimized Three-Band Spectral Indices and a Machine Learning Model to Assess Squash Characteristics under Moisture and Potassium Deficiency Stress
Moisture and potassium deficiency are two of the main limiting variables for squash crop performance in many water-stressed places worldwide. If major output decreases are to be avoided, it is critical to detect signs of crop stress as early as possible in the growth cycle. Proximal remote sensing c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Horticulturae |
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| Online Access: | https://www.mdpi.com/2311-7524/9/1/79 |
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| author | Mohamed A. Sharaf-Eldin Salah Elsayed Adel H. Elmetwalli Zaher Mundher Yaseen Farahat S. Moghanm Mohssen Elbagory Sahar El-Nahrawy Alaa El-Dein Omara Andrew N. Tyler Osama Elsherbiny |
| author_facet | Mohamed A. Sharaf-Eldin Salah Elsayed Adel H. Elmetwalli Zaher Mundher Yaseen Farahat S. Moghanm Mohssen Elbagory Sahar El-Nahrawy Alaa El-Dein Omara Andrew N. Tyler Osama Elsherbiny |
| author_sort | Mohamed A. Sharaf-Eldin |
| collection | DOAJ |
| description | Moisture and potassium deficiency are two of the main limiting variables for squash crop performance in many water-stressed places worldwide. If major output decreases are to be avoided, it is critical to detect signs of crop stress as early as possible in the growth cycle. Proximal remote sensing can be a reliable technique for offering a rapid and precise instrument and localized management tool. This study tested the ability of proximal hyperspectral remotely sensed data to predict squash traits in two successive seasons (spring and fall) with varying moisture and potassium rates. Spectral data were collected from drip-irrigated squash that had been treated to varied rates of irrigation and potassium fertilization over both investigated seasons. To forecast potassium-use efficiency (KUE), chlorophyll meter (Chlm), water-use efficiency (WUE), and seed yield (SY) of squash, different commonly used and newly-introduced spectral index values for three bands (3D-SRIs), as well as a Decision Tree (DT) model, were evaluated. The results revealed that the newly constructed three-band SRIs based on the wavelengths of the visible (VIS), near-infrared (NIR), and red-edge regions were sensitive enough to measure the four tested parameters of squash in this study. For instance, NDI<sub>558,646,708</sub> presented the highest R<sup>2</sup> of 0.75 for KUE, NDI<sub>744,746,738</sub> presented the highest R<sup>2</sup> of 0.65 for Chlm, and NDI<sub>670,628,392</sub> presented the highest R<sup>2</sup> of 0.64 for SY of squash. The results further demonstrated that the principal component analysis (PCA) demonstrated the ability to distinguish moisture stress from potassium deficiency stress at the flowering stage onwards. Combining 3D-SRIs, DT-based bands (DT-b), and the aggregate of all spectral characteristics (ASF) with DT models would be an effective strategy for estimating four observed parameters with appropriate accuracy. For example, the model’s approximately 30 spectral characteristics were extremely important for predicting KUE. Its outputs with R<sup>2</sup> were, for the training and validation datasets, 0.967 (RMSE = 0.175) and 0.818 (RMSE = 0.284), respectively. For measuring Chlm, the DT-DT-b-20 model demonstrated the best. In the training and validation datasets, the R<sup>2</sup> value was 0.993 (RMSE = 0.522) and 0.692 (RMSE = 2.321), respectively. The overall outcomes showed that proximal-reflectance-sensing-based 3D-SRIs and DT models based on 3D-SRIs, DT-b, and ASF could be used to evaluate the four tested parameters of squash under different levels of irrigation regimes and potassium fertilizer. |
| first_indexed | 2024-03-09T12:30:09Z |
| format | Article |
| id | doaj.art-dede1921e6d040dab7bf67022a62e38a |
| institution | Directory Open Access Journal |
| issn | 2311-7524 |
| language | English |
| last_indexed | 2024-03-09T12:30:09Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Horticulturae |
| spelling | doaj.art-dede1921e6d040dab7bf67022a62e38a2023-11-30T22:30:36ZengMDPI AGHorticulturae2311-75242023-01-01917910.3390/horticulturae9010079Using Optimized Three-Band Spectral Indices and a Machine Learning Model to Assess Squash Characteristics under Moisture and Potassium Deficiency StressMohamed A. Sharaf-Eldin0Salah Elsayed1Adel H. Elmetwalli2Zaher Mundher Yaseen3Farahat S. Moghanm4Mohssen Elbagory5Sahar El-Nahrawy6Alaa El-Dein Omara7Andrew N. Tyler8Osama Elsherbiny9Horticulture Department, Faculty of Agriculture, Kafrelsheikh University, Kafr El-Sheikh 33516, EgyptAgricultural Engineering, Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Minufiya 32897, EgyptDepartment of Agricultural Engineering, Faculty of Agriculture, Tanta University, Tanta 31527, EgyptCivil and Environmental Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi ArabiaSoil and Water Department, Faculty of Agriculture, Kafrelsheikh University, Kafr El-Sheikh 33516, EgyptDepartment of Biology, Faculty of Science and Arts, King Khalid University, Mohail 61321, Assir, Saudi ArabiaAgricultural Research Center, Department of Microbiology, Soils, Water and Environment, Research Institute, Giza 12112, EgyptAgricultural Research Center, Department of Microbiology, Soils, Water and Environment, Research Institute, Giza 12112, EgyptSchool of Biological and Environmental Sciences, University of Stirling, Stirling, Scotland FK9 4LA, UKAgricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, EgyptMoisture and potassium deficiency are two of the main limiting variables for squash crop performance in many water-stressed places worldwide. If major output decreases are to be avoided, it is critical to detect signs of crop stress as early as possible in the growth cycle. Proximal remote sensing can be a reliable technique for offering a rapid and precise instrument and localized management tool. This study tested the ability of proximal hyperspectral remotely sensed data to predict squash traits in two successive seasons (spring and fall) with varying moisture and potassium rates. Spectral data were collected from drip-irrigated squash that had been treated to varied rates of irrigation and potassium fertilization over both investigated seasons. To forecast potassium-use efficiency (KUE), chlorophyll meter (Chlm), water-use efficiency (WUE), and seed yield (SY) of squash, different commonly used and newly-introduced spectral index values for three bands (3D-SRIs), as well as a Decision Tree (DT) model, were evaluated. The results revealed that the newly constructed three-band SRIs based on the wavelengths of the visible (VIS), near-infrared (NIR), and red-edge regions were sensitive enough to measure the four tested parameters of squash in this study. For instance, NDI<sub>558,646,708</sub> presented the highest R<sup>2</sup> of 0.75 for KUE, NDI<sub>744,746,738</sub> presented the highest R<sup>2</sup> of 0.65 for Chlm, and NDI<sub>670,628,392</sub> presented the highest R<sup>2</sup> of 0.64 for SY of squash. The results further demonstrated that the principal component analysis (PCA) demonstrated the ability to distinguish moisture stress from potassium deficiency stress at the flowering stage onwards. Combining 3D-SRIs, DT-based bands (DT-b), and the aggregate of all spectral characteristics (ASF) with DT models would be an effective strategy for estimating four observed parameters with appropriate accuracy. For example, the model’s approximately 30 spectral characteristics were extremely important for predicting KUE. Its outputs with R<sup>2</sup> were, for the training and validation datasets, 0.967 (RMSE = 0.175) and 0.818 (RMSE = 0.284), respectively. For measuring Chlm, the DT-DT-b-20 model demonstrated the best. In the training and validation datasets, the R<sup>2</sup> value was 0.993 (RMSE = 0.522) and 0.692 (RMSE = 2.321), respectively. The overall outcomes showed that proximal-reflectance-sensing-based 3D-SRIs and DT models based on 3D-SRIs, DT-b, and ASF could be used to evaluate the four tested parameters of squash under different levels of irrigation regimes and potassium fertilizer.https://www.mdpi.com/2311-7524/9/1/79abiotic stress<i>Cucurbita pepo</i>potassium-use efficiencywater-use efficiencyseed yield |
| spellingShingle | Mohamed A. Sharaf-Eldin Salah Elsayed Adel H. Elmetwalli Zaher Mundher Yaseen Farahat S. Moghanm Mohssen Elbagory Sahar El-Nahrawy Alaa El-Dein Omara Andrew N. Tyler Osama Elsherbiny Using Optimized Three-Band Spectral Indices and a Machine Learning Model to Assess Squash Characteristics under Moisture and Potassium Deficiency Stress Horticulturae abiotic stress <i>Cucurbita pepo</i> potassium-use efficiency water-use efficiency seed yield |
| title | Using Optimized Three-Band Spectral Indices and a Machine Learning Model to Assess Squash Characteristics under Moisture and Potassium Deficiency Stress |
| title_full | Using Optimized Three-Band Spectral Indices and a Machine Learning Model to Assess Squash Characteristics under Moisture and Potassium Deficiency Stress |
| title_fullStr | Using Optimized Three-Band Spectral Indices and a Machine Learning Model to Assess Squash Characteristics under Moisture and Potassium Deficiency Stress |
| title_full_unstemmed | Using Optimized Three-Band Spectral Indices and a Machine Learning Model to Assess Squash Characteristics under Moisture and Potassium Deficiency Stress |
| title_short | Using Optimized Three-Band Spectral Indices and a Machine Learning Model to Assess Squash Characteristics under Moisture and Potassium Deficiency Stress |
| title_sort | using optimized three band spectral indices and a machine learning model to assess squash characteristics under moisture and potassium deficiency stress |
| topic | abiotic stress <i>Cucurbita pepo</i> potassium-use efficiency water-use efficiency seed yield |
| url | https://www.mdpi.com/2311-7524/9/1/79 |
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