Prediction models of macro-nutrient content in plant organs of Cucumis melo in response to soil elements using support vector regression
Background Undoubtedly, the importance of food and food security as one of the present and future challenges is not invisible to anyone. Nowadays, the development of methods for monitoring the nutrient content in crop products is an essential issue for implementing reasonable and logical soil proper...
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| Published in | PeerJ (San Francisco, CA) Vol. 11; p. e15417 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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San Diego, USA
PeerJ. Ltd
02.10.2023
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| ISSN | 2167-8359 2167-8359 |
| DOI | 10.7717/peerj.15417 |
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| Abstract | Background Undoubtedly, the importance of food and food security as one of the present and future challenges is not invisible to anyone. Nowadays, the development of methods for monitoring the nutrient content in crop products is an essential issue for implementing reasonable and logical soil properties management. The modeling technique can evaluate the soil properties of fields and study the subject of crop yield through soil management. This study aims to predict fruit yield and macro-nutrient content in plant organs of Cucumis melo in response to soil elements using support vector regression (SVR). Methodology In the spring of 2020, this study was done as a factorial test in a randomized complete block design with three replications. The first factor was the use of fertilizers in six levels: no fertilizer (control), cow manure (30 t ha.sup.-1 ), sheep manure (30t ha.sup.-1 ), nanobiomic foliar application (2 l ha.sup.-1 ), silicone foliar application (3 l ha.sup.-1 ), and chemical fertilizer from urea, triple superphosphate, and potassium sulfate sources (200, 100, and 150 kg ha.sup.-1 ). In addition, four levels of vermicompost considering as the second factor: no vermicompost (control), 5, 10, and 15 t ha.sup.-1 . Input data sets such as fruit yield and nitrogen, phosphorus, and potassium levels in the seeds, fruits, leaves, and roots are used to calibrate the probabilistic model of SP using SVR. Results According to the results, when the data sets of the nitrogen, phosphorus, and potassium in the fruit uses as input, the accuracy of these models was higher than 80.0% (R.sup.2 = 0.807 for predicting fruit nitrogen; R.sup.2 = 0.999 for fruit phosphorus; R.sup.2 = 0.968 for fruit potassium). Also, the results of the prediction models in response to soil elements showed that the soil nitrogen content ranged from 0.05 to 1.1%, soil phosphorus from 10 to 59 mg kg.sup.-1 , and soil potassium from 180 to 320 mg kg.sup.-1 , which offers a suitable macro-nutrient content in the soil. Likewise, the best fruit nitrogen content ranged from 1.27 to 4.33%, fruit phosphorus from 15.74 to 26.19%, fruit potassium from 15.19 to 19.67%, and fruit yield from 2.16 to 5.95 kg per plant obtained under NPK chemical fertilizers and using 15 t ha.sup.-1 of vermicompost. Conclusions Because the fruit values had the highest contribution in prediction than observed values, thus identified as the best plant organs in response to soil elements. Based on our findings, the importance of fruit phosphorus identifies as a determinant that strongly influenced melon prediction models. More significant values of soil elements do not affect increasing fruit yield and macro-nutrient content in plant organs, and excessive application may not be economical. Therefore, our studies provide an efficient approach with potentially high accuracy to estimate fruit yield and macro-nutrient in the fruits of Cucumis melo in response to soil elements and cause a saving in the amount of fertilizer during the growing season. |
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| AbstractList | Background Undoubtedly, the importance of food and food security as one of the present and future challenges is not invisible to anyone. Nowadays, the development of methods for monitoring the nutrient content in crop products is an essential issue for implementing reasonable and logical soil properties management. The modeling technique can evaluate the soil properties of fields and study the subject of crop yield through soil management. This study aims to predict fruit yield and macro-nutrient content in plant organs of Cucumis melo in response to soil elements using support vector regression (SVR). Methodology In the spring of 2020, this study was done as a factorial test in a randomized complete block design with three replications. The first factor was the use of fertilizers in six levels: no fertilizer (control), cow manure (30 t ha.sup.-1 ), sheep manure (30t ha.sup.-1 ), nanobiomic foliar application (2 l ha.sup.-1 ), silicone foliar application (3 l ha.sup.-1 ), and chemical fertilizer from urea, triple superphosphate, and potassium sulfate sources (200, 100, and 150 kg ha.sup.-1 ). In addition, four levels of vermicompost considering as the second factor: no vermicompost (control), 5, 10, and 15 t ha.sup.-1 . Input data sets such as fruit yield and nitrogen, phosphorus, and potassium levels in the seeds, fruits, leaves, and roots are used to calibrate the probabilistic model of SP using SVR. Results According to the results, when the data sets of the nitrogen, phosphorus, and potassium in the fruit uses as input, the accuracy of these models was higher than 80.0% (R.sup.2 = 0.807 for predicting fruit nitrogen; R.sup.2 = 0.999 for fruit phosphorus; R.sup.2 = 0.968 for fruit potassium). Also, the results of the prediction models in response to soil elements showed that the soil nitrogen content ranged from 0.05 to 1.1%, soil phosphorus from 10 to 59 mg kg.sup.-1 , and soil potassium from 180 to 320 mg kg.sup.-1 , which offers a suitable macro-nutrient content in the soil. Likewise, the best fruit nitrogen content ranged from 1.27 to 4.33%, fruit phosphorus from 15.74 to 26.19%, fruit potassium from 15.19 to 19.67%, and fruit yield from 2.16 to 5.95 kg per plant obtained under NPK chemical fertilizers and using 15 t ha.sup.-1 of vermicompost. Conclusions Because the fruit values had the highest contribution in prediction than observed values, thus identified as the best plant organs in response to soil elements. Based on our findings, the importance of fruit phosphorus identifies as a determinant that strongly influenced melon prediction models. More significant values of soil elements do not affect increasing fruit yield and macro-nutrient content in plant organs, and excessive application may not be economical. Therefore, our studies provide an efficient approach with potentially high accuracy to estimate fruit yield and macro-nutrient in the fruits of Cucumis melo in response to soil elements and cause a saving in the amount of fertilizer during the growing season. Background Undoubtedly, the importance of food and food security as one of the present and future challenges is not invisible to anyone. Nowadays, the development of methods for monitoring the nutrient content in crop products is an essential issue for implementing reasonable and logical soil properties management. The modeling technique can evaluate the soil properties of fields and study the subject of crop yield through soil management. This study aims to predict fruit yield and macro-nutrient content in plant organs of Cucumis melo in response to soil elements using support vector regression (SVR). Methodology In the spring of 2020, this study was done as a factorial test in a randomized complete block design with three replications. The first factor was the use of fertilizers in six levels: no fertilizer (control), cow manure (30 t ha−1), sheep manure (30 t ha−1), nanobiomic foliar application (2 l ha−1), silicone foliar application (3 l ha−1), and chemical fertilizer from urea, triple superphosphate, and potassium sulfate sources (200, 100, and 150 kg ha−1). In addition, four levels of vermicompost considering as the second factor: no vermicompost (control), 5, 10, and 15 t ha−1. Input data sets such as fruit yield and nitrogen, phosphorus, and potassium levels in the seeds, fruits, leaves, and roots are used to calibrate the probabilistic model of SP using SVR. Results According to the results, when the data sets of the nitrogen, phosphorus, and potassium in the fruit uses as input, the accuracy of these models was higher than 80.0% (R2 = 0.807 for predicting fruit nitrogen; R2 = 0.999 for fruit phosphorus; R2 = 0.968 for fruit potassium). Also, the results of the prediction models in response to soil elements showed that the soil nitrogen content ranged from 0.05 to 1.1%, soil phosphorus from 10 to 59 mg kg−1, and soil potassium from 180 to 320 mg kg−1, which offers a suitable macro-nutrient content in the soil. Likewise, the best fruit nitrogen content ranged from 1.27 to 4.33%, fruit phosphorus from 15.74 to 26.19%, fruit potassium from 15.19 to 19.67%, and fruit yield from 2.16 to 5.95 kg per plant obtained under NPK chemical fertilizers and using 15 t ha−1 of vermicompost. Conclusions Because the fruit values had the highest contribution in prediction than observed values, thus identified as the best plant organs in response to soil elements. Based on our findings, the importance of fruit phosphorus identifies as a determinant that strongly influenced melon prediction models. More significant values of soil elements do not affect increasing fruit yield and macro-nutrient content in plant organs, and excessive application may not be economical. Therefore, our studies provide an efficient approach with potentially high accuracy to estimate fruit yield and macro-nutrient in the fruits of Cucumis melo in response to soil elements and cause a saving in the amount of fertilizer during the growing season. Undoubtedly, the importance of food and food security as one of the present and future challenges is not invisible to anyone. Nowadays, the development of methods for monitoring the nutrient content in crop products is an essential issue for implementing reasonable and logical soil properties management. The modeling technique can evaluate the soil properties of fields and study the subject of crop yield through soil management. This study aims to predict fruit yield and macro-nutrient content in plant organs of Cucumis melo in response to soil elements using support vector regression (SVR).BackgroundUndoubtedly, the importance of food and food security as one of the present and future challenges is not invisible to anyone. Nowadays, the development of methods for monitoring the nutrient content in crop products is an essential issue for implementing reasonable and logical soil properties management. The modeling technique can evaluate the soil properties of fields and study the subject of crop yield through soil management. This study aims to predict fruit yield and macro-nutrient content in plant organs of Cucumis melo in response to soil elements using support vector regression (SVR).In the spring of 2020, this study was done as a factorial test in a randomized complete block design with three replications. The first factor was the use of fertilizers in six levels: no fertilizer (control), cow manure (30 t ha-1), sheep manure (30 t ha-1), nanobiomic foliar application (2 l ha-1), silicone foliar application (3 l ha-1), and chemical fertilizer from urea, triple superphosphate, and potassium sulfate sources (200, 100, and 150 kg ha-1). In addition, four levels of vermicompost considering as the second factor: no vermicompost (control), 5, 10, and 15 t ha-1. Input data sets such as fruit yield and nitrogen, phosphorus, and potassium levels in the seeds, fruits, leaves, and roots are used to calibrate the probabilistic model of SP using SVR.MethodologyIn the spring of 2020, this study was done as a factorial test in a randomized complete block design with three replications. The first factor was the use of fertilizers in six levels: no fertilizer (control), cow manure (30 t ha-1), sheep manure (30 t ha-1), nanobiomic foliar application (2 l ha-1), silicone foliar application (3 l ha-1), and chemical fertilizer from urea, triple superphosphate, and potassium sulfate sources (200, 100, and 150 kg ha-1). In addition, four levels of vermicompost considering as the second factor: no vermicompost (control), 5, 10, and 15 t ha-1. Input data sets such as fruit yield and nitrogen, phosphorus, and potassium levels in the seeds, fruits, leaves, and roots are used to calibrate the probabilistic model of SP using SVR.According to the results, when the data sets of the nitrogen, phosphorus, and potassium in the fruit uses as input, the accuracy of these models was higher than 80.0% (R2 = 0.807 for predicting fruit nitrogen; R2 = 0.999 for fruit phosphorus; R2 = 0.968 for fruit potassium). Also, the results of the prediction models in response to soil elements showed that the soil nitrogen content ranged from 0.05 to 1.1%, soil phosphorus from 10 to 59 mg kg-1, and soil potassium from 180 to 320 mg kg-1, which offers a suitable macro-nutrient content in the soil. Likewise, the best fruit nitrogen content ranged from 1.27 to 4.33%, fruit phosphorus from 15.74 to 26.19%, fruit potassium from 15.19 to 19.67%, and fruit yield from 2.16 to 5.95 kg per plant obtained under NPK chemical fertilizers and using 15 t ha-1 of vermicompost.ResultsAccording to the results, when the data sets of the nitrogen, phosphorus, and potassium in the fruit uses as input, the accuracy of these models was higher than 80.0% (R2 = 0.807 for predicting fruit nitrogen; R2 = 0.999 for fruit phosphorus; R2 = 0.968 for fruit potassium). Also, the results of the prediction models in response to soil elements showed that the soil nitrogen content ranged from 0.05 to 1.1%, soil phosphorus from 10 to 59 mg kg-1, and soil potassium from 180 to 320 mg kg-1, which offers a suitable macro-nutrient content in the soil. Likewise, the best fruit nitrogen content ranged from 1.27 to 4.33%, fruit phosphorus from 15.74 to 26.19%, fruit potassium from 15.19 to 19.67%, and fruit yield from 2.16 to 5.95 kg per plant obtained under NPK chemical fertilizers and using 15 t ha-1 of vermicompost.Because the fruit values had the highest contribution in prediction than observed values, thus identified as the best plant organs in response to soil elements. Based on our findings, the importance of fruit phosphorus identifies as a determinant that strongly influenced melon prediction models. More significant values of soil elements do not affect increasing fruit yield and macro-nutrient content in plant organs, and excessive application may not be economical. Therefore, our studies provide an efficient approach with potentially high accuracy to estimate fruit yield and macro-nutrient in the fruits of Cucumis melo in response to soil elements and cause a saving in the amount of fertilizer during the growing season.ConclusionsBecause the fruit values had the highest contribution in prediction than observed values, thus identified as the best plant organs in response to soil elements. Based on our findings, the importance of fruit phosphorus identifies as a determinant that strongly influenced melon prediction models. More significant values of soil elements do not affect increasing fruit yield and macro-nutrient content in plant organs, and excessive application may not be economical. Therefore, our studies provide an efficient approach with potentially high accuracy to estimate fruit yield and macro-nutrient in the fruits of Cucumis melo in response to soil elements and cause a saving in the amount of fertilizer during the growing season. Undoubtedly, the importance of food and food security as one of the present and future challenges is not invisible to anyone. Nowadays, the development of methods for monitoring the nutrient content in crop products is an essential issue for implementing reasonable and logical soil properties management. The modeling technique can evaluate the soil properties of fields and study the subject of crop yield through soil management. This study aims to predict fruit yield and macro-nutrient content in plant organs of Cucumis melo in response to soil elements using support vector regression (SVR). In the spring of 2020, this study was done as a factorial test in a randomized complete block design with three replications. The first factor was the use of fertilizers in six levels: no fertilizer (control), cow manure (30 t ha.sup.-1 ), sheep manure (30t ha.sup.-1 ), nanobiomic foliar application (2 l ha.sup.-1 ), silicone foliar application (3 l ha.sup.-1 ), and chemical fertilizer from urea, triple superphosphate, and potassium sulfate sources (200, 100, and 150 kg ha.sup.-1 ). In addition, four levels of vermicompost considering as the second factor: no vermicompost (control), 5, 10, and 15 t ha.sup.-1 . Input data sets such as fruit yield and nitrogen, phosphorus, and potassium levels in the seeds, fruits, leaves, and roots are used to calibrate the probabilistic model of SP using SVR. According to the results, when the data sets of the nitrogen, phosphorus, and potassium in the fruit uses as input, the accuracy of these models was higher than 80.0% (R.sup.2 = 0.807 for predicting fruit nitrogen; R.sup.2 = 0.999 for fruit phosphorus; R.sup.2 = 0.968 for fruit potassium). Also, the results of the prediction models in response to soil elements showed that the soil nitrogen content ranged from 0.05 to 1.1%, soil phosphorus from 10 to 59 mg kg.sup.-1 , and soil potassium from 180 to 320 mg kg.sup.-1 , which offers a suitable macro-nutrient content in the soil. Likewise, the best fruit nitrogen content ranged from 1.27 to 4.33%, fruit phosphorus from 15.74 to 26.19%, fruit potassium from 15.19 to 19.67%, and fruit yield from 2.16 to 5.95 kg per plant obtained under NPK chemical fertilizers and using 15 t ha.sup.-1 of vermicompost. Because the fruit values had the highest contribution in prediction than observed values, thus identified as the best plant organs in response to soil elements. Based on our findings, the importance of fruit phosphorus identifies as a determinant that strongly influenced melon prediction models. More significant values of soil elements do not affect increasing fruit yield and macro-nutrient content in plant organs, and excessive application may not be economical. Therefore, our studies provide an efficient approach with potentially high accuracy to estimate fruit yield and macro-nutrient in the fruits of Cucumis melo in response to soil elements and cause a saving in the amount of fertilizer during the growing season. |
| ArticleNumber | e15417 |
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| Author | Dahmardeh, Mahdi Keshtehgar, Abbas Ghanbari, Ahmad Khammari, Issa |
| Author_xml | – sequence: 1 givenname: Abbas surname: Keshtehgar fullname: Keshtehgar, Abbas organization: Department of Agronomy, University of Zabol, Zabol, Sistan and Baluchestan, Iran – sequence: 2 givenname: Mahdi surname: Dahmardeh fullname: Dahmardeh, Mahdi organization: Department of Agronomy, University of Zabol, Zabol, Sistan and Baluchestan, Iran – sequence: 3 givenname: Ahmad surname: Ghanbari fullname: Ghanbari, Ahmad organization: Department of Agronomy, University of Zabol, Zabol, Sistan and Baluchestan, Iran – sequence: 4 givenname: Issa surname: Khammari fullname: Khammari, Issa organization: Department of Agronomy, University of Zabol, Zabol, Sistan and Baluchestan, Iran |
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| Cites_doi | 10.1016/j.geoderma.2019.07.014 10.20546/ijcmas.2017.603.017 10.1590/S0103-84782002000400028 10.7717/peerj.11463 10.1016/j.ress.2016.09.003 10.3390/s18103408 10.1080/00380768.2014.942879 10.1007/978-1-4757-2440-0 10.1111/j.1467-8667.2012.00767.x 10.3923/pjbs.2014.408.413 10.1016/j.biortech.2012.01.126 10.1016/j.geoderma.2017.11.006 10.1080/01431161.2018.1513180 10.1080/00103624.2013.832284 10.1080/01904167.2018.1551491 10.1016/j.scienta.2019.108756 10.1007/s10681-009-0110-6 10.1016/j.geoderma.2005.03.007 10.1007/BF01338151 10.1111/ppl.12747 10.7717/peerj.12726 10.1016/j.neucom.2013.08.012 10.1002/jsfa.7390 10.1016/j.catena.2017.02.006 10.1016/j.fcr.2015.11.011 10.1590/01000683rbcs20140172 10.2136/sssaj2001.652480x 10.1016/j.atmosres.2017.04.017 10.1039/B918972F 10.1002/jsfa.3139 10.3920/9789086866038_014 10.1016/j.still.2015.08.015 |
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| SubjectTerms | Agricultural Science Crop yields Data Mining and Machine Learning Food supply Macro-nutrients Melon Natural Resource Management Organic fertilizers Phosphates Phosphatic fertilizers Phosphorus content Plant Science Prediction model Real estate management Soil elements Soil management Soil Science Soils Support vector regression Urea |
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| Title | Prediction models of macro-nutrient content in plant organs of Cucumis melo in response to soil elements using support vector regression |
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