Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt
Monitoring strategic agricultural crops in terms of crop growth performance, by accurate cost-effective and quick tools is crucially important in site-specific management to avoid crop reductions. The availability of commercial high resolution satellite images with high resolution (spatial and spect...
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MDPI AG
2022-02-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/12/3/332 |
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| author | Adel H. Elmetwalli Yasser S. A. Mazrou Andrew N. Tyler Peter D. Hunter Osama Elsherbiny Zaher Mundher Yaseen Salah Elsayed |
| author_facet | Adel H. Elmetwalli Yasser S. A. Mazrou Andrew N. Tyler Peter D. Hunter Osama Elsherbiny Zaher Mundher Yaseen Salah Elsayed |
| author_sort | Adel H. Elmetwalli |
| collection | DOAJ |
| description | Monitoring strategic agricultural crops in terms of crop growth performance, by accurate cost-effective and quick tools is crucially important in site-specific management to avoid crop reductions. The availability of commercial high resolution satellite images with high resolution (spatial and spectral) as well as in situ spectra measurements can help decision takers to have deep insight on crop stress in a certain region. The research attempts to examine remote sensing dataset for forecasting wheat crop (<i>Sakha 61</i>) characteristics including the leaf area index (LAI), plant height (plant-h), above ground biomass (AGB) and Soil Plant Analysis Development (SPAD) value of wheat across non-stress, drought and salinity-induced stress in the Nile Delta region. In this context, the ability of in situ spectroradiometry measurements and QuickBird high resolution images was evaluated in our research. The efficiency of Random Forest (RF) and Artificial Neural Network (ANN), mathematical models was assessed to estimate the four measured wheat characteristics based on vegetation spectral reflectance indices (V-SRIs) extracted from both approaches and their interactions. Field surveys were carried out to collect in situ spectroradiometry measurements concomitant with the acquisition of QuickBird imagery. The results demonstrated that several V-SRIs extracted from in situ spectroradiometry data and the QuickBird image correlated with the LAI, plant-h, AGB, and SPAD value of wheat crop across the study site. The determination coefficient (R<sup>2</sup>) values of the association between V-SRIs of in situ spectroradiometry data and various determined wheat characteristics varied from 0.26 to 0.85. The ANN-GSIs-3 was found to be the optimum predictive model, demonstrating a greater relationship between the advanced features and LAI. The three features of V-SRIs comprised in this model were strongly significant for the prediction of LAI. The attained results indicated high R<sup>2</sup> values of 0.94 and 0.86 for the training and validation phases. The ANN-GSIs-3 model constructed for the determination of chlorophyll in the plant which had higher performance expectations (R<sup>2</sup> = 0.96 and 0.92 for training and validation datasets, respectively). In conclusion, the results of our study revealed that high resolution remote sensing images such as QuickBird or similar imagery, and in situ spectroradiometry measurements have the feasibility of providing necessary crop monitoring data across non-stressed and stressed (drought and salinity) conditions when integrating V-SRIs with ANN and RF algorithms. |
| first_indexed | 2024-03-09T13:55:06Z |
| format | Article |
| id | doaj.art-826e7199e51c466497660bf371af46c1 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-09T13:55:06Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-826e7199e51c466497660bf371af46c12023-11-30T20:43:05ZengMDPI AGAgriculture2077-04722022-02-0112333210.3390/agriculture12030332Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of EgyptAdel H. Elmetwalli0Yasser S. A. Mazrou1Andrew N. Tyler2Peter D. Hunter3Osama Elsherbiny4Zaher Mundher Yaseen5Salah Elsayed6Agricultural Engineering Department, Faculty of Agriculture, Tanta University, Tanta 31527, EgyptCommunity College at Muhyle, King Khalid University, Abha 62587, Saudi ArabiaSchool of Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA, UKSchool of Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA, UKAgricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, EgyptAdjunct Research Fellow, USQ’s Advanced Data Analytics Research Group, School of Mathematics Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, AustraliaAgricultural Engineering, Evaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Minufiya 32897, EgyptMonitoring strategic agricultural crops in terms of crop growth performance, by accurate cost-effective and quick tools is crucially important in site-specific management to avoid crop reductions. The availability of commercial high resolution satellite images with high resolution (spatial and spectral) as well as in situ spectra measurements can help decision takers to have deep insight on crop stress in a certain region. The research attempts to examine remote sensing dataset for forecasting wheat crop (<i>Sakha 61</i>) characteristics including the leaf area index (LAI), plant height (plant-h), above ground biomass (AGB) and Soil Plant Analysis Development (SPAD) value of wheat across non-stress, drought and salinity-induced stress in the Nile Delta region. In this context, the ability of in situ spectroradiometry measurements and QuickBird high resolution images was evaluated in our research. The efficiency of Random Forest (RF) and Artificial Neural Network (ANN), mathematical models was assessed to estimate the four measured wheat characteristics based on vegetation spectral reflectance indices (V-SRIs) extracted from both approaches and their interactions. Field surveys were carried out to collect in situ spectroradiometry measurements concomitant with the acquisition of QuickBird imagery. The results demonstrated that several V-SRIs extracted from in situ spectroradiometry data and the QuickBird image correlated with the LAI, plant-h, AGB, and SPAD value of wheat crop across the study site. The determination coefficient (R<sup>2</sup>) values of the association between V-SRIs of in situ spectroradiometry data and various determined wheat characteristics varied from 0.26 to 0.85. The ANN-GSIs-3 was found to be the optimum predictive model, demonstrating a greater relationship between the advanced features and LAI. The three features of V-SRIs comprised in this model were strongly significant for the prediction of LAI. The attained results indicated high R<sup>2</sup> values of 0.94 and 0.86 for the training and validation phases. The ANN-GSIs-3 model constructed for the determination of chlorophyll in the plant which had higher performance expectations (R<sup>2</sup> = 0.96 and 0.92 for training and validation datasets, respectively). In conclusion, the results of our study revealed that high resolution remote sensing images such as QuickBird or similar imagery, and in situ spectroradiometry measurements have the feasibility of providing necessary crop monitoring data across non-stressed and stressed (drought and salinity) conditions when integrating V-SRIs with ANN and RF algorithms.https://www.mdpi.com/2077-0472/12/3/332artificial neural networkQuickBirdrandom forestsatellite imagessalinityspectral indices |
| spellingShingle | Adel H. Elmetwalli Yasser S. A. Mazrou Andrew N. Tyler Peter D. Hunter Osama Elsherbiny Zaher Mundher Yaseen Salah Elsayed Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt Agriculture artificial neural network QuickBird random forest satellite images salinity spectral indices |
| title | Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt |
| title_full | Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt |
| title_fullStr | Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt |
| title_full_unstemmed | Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt |
| title_short | Assessing the Efficiency of Remote Sensing and Machine Learning Algorithms to Quantify Wheat Characteristics in the Nile Delta Region of Egypt |
| title_sort | assessing the efficiency of remote sensing and machine learning algorithms to quantify wheat characteristics in the nile delta region of egypt |
| topic | artificial neural network QuickBird random forest satellite images salinity spectral indices |
| url | https://www.mdpi.com/2077-0472/12/3/332 |
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