Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 143
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 143
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 209
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3098
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 574
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 488
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Severity: Warning
Message: Attempt to read property "Count" on bool
Filename: helpers/my_audit_helper.php
Line Number: 3100
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3100
Function: _error_handler
File: /var/www/html/application/controllers/Detail.php
Line: 574
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 488
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
New and simple crop yield prediction methods are expected to be developed owing to the increasing environmental stress caused by climate change. Algorithms of machine learning could be a powerful tool for predicting crop yield; however, the required feature variables and differences in their prediction accuracy are poorly addressed. The objectives of this study were to identify the best combination of feature variables to predict the yield of cowpea (Vigna unguiculata), which is widely grown in central Sudan Savanna under environmentally restricted conditions, and clarify the differences in the accuracy of major machine learning algorithms. The study also explored the environmental and plant factors affecting the prediction errors. Sample data were obtained from cowpea field experiments in central Sudan Savanna. The prediction was performed using 28 models, encompassing four machine learning algorithms and seven combinations of feature variables. Support Vector Regression and Neural Network algorithms effectively predicted cowpea yields using continuous leaf coverage rates as feature variables; however, some differences were observed in their prediction accuracy depending on the soil types and growth habits. The use of feature variables that are related to shoot growth and plant physiological status could minimize prediction errors.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579339 | PMC |
http://dx.doi.org/10.1038/s41598-024-80288-3 | DOI Listing |
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