Emily Branstad-Spates, Lina Castano-Duque, Gretchen Mosher, Charles Hurburgh Jr., Kanniah Rajasekaran, Phillip Owens, H. Edwin Winzeler, Erin Bowers
{"title":"预测爱荷华州玉米中的伏马菌毒素：梯度提升机器学习","authors":"Emily Branstad-Spates, Lina Castano-Duque, Gretchen Mosher, Charles Hurburgh Jr., Kanniah Rajasekaran, Phillip Owens, H. Edwin Winzeler, Erin Bowers","doi":"10.1002/cche.10824","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background and Objectives</h3>\n \n <p>Fumonisin (FUM), a secondary metabolite from <i>Fusarium</i> spp., poses major concerns for the United States corn industry. This study evaluated a prepublished Illinois-centric predictive model with historical Iowa FUM contamination data using gradient boosting machine (GBM) learning and compared influential predictors with an Iowa-centric model. Corn samples (<i>n</i> = 529) were collected from 2010, 2020, and 2021 in Iowa's 99 counties, and 2011 data were used for independent validation (<i>n</i> = 89).</p>\n </section>\n \n <section>\n \n <h3> Findings</h3>\n \n <p>Applying a 2 ppm (mg/kg) threshold for FUM high and low contamination events, the overall accuracy was 71.08% and 85.39% for the Illinois- and Iowa-centric models in 2011. Balanced accuracies were 60.23% and 50.00% for the Illinois- and Iowa-centric models. For Iowa's remaining years (testing data), the overall accuracy was 98.10%, and balanced accuracy was 50.00%.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>FUM-GBM analyses determined the top influential predictor for the Illinois-centric model was satellite-acquired normalized difference vegetation index (NDVI) (Veg_index) in March, whereas the top predictor for the Iowa-centric model was precipitation (PRCP) in October.</p>\n </section>\n \n <section>\n \n <h3> Significance and Novelty</h3>\n \n <p>Results indicate that meteorological and agronomic events, such as PRCP and Veg_index in early planting stages and during harvest, may influence the probability of high FUM levels in corn.</p>\n </section>\n </div>","PeriodicalId":9807,"journal":{"name":"Cereal Chemistry","volume":"101 6","pages":"1261-1272"},"PeriodicalIF":2.2000,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cche.10824","citationCount":"0","resultStr":"{\"title\":\"Predicting fumonisins in Iowa corn: Gradient boosting machine learning\",\"authors\":\"Emily Branstad-Spates, Lina Castano-Duque, Gretchen Mosher, Charles Hurburgh Jr., Kanniah Rajasekaran, Phillip Owens, H. Edwin Winzeler, Erin Bowers\",\"doi\":\"10.1002/cche.10824\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background and Objectives</h3>\\n \\n <p>Fumonisin (FUM), a secondary metabolite from <i>Fusarium</i> spp., poses major concerns for the United States corn industry. This study evaluated a prepublished Illinois-centric predictive model with historical Iowa FUM contamination data using gradient boosting machine (GBM) learning and compared influential predictors with an Iowa-centric model. Corn samples (<i>n</i> = 529) were collected from 2010, 2020, and 2021 in Iowa's 99 counties, and 2011 data were used for independent validation (<i>n</i> = 89).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Findings</h3>\\n \\n <p>Applying a 2 ppm (mg/kg) threshold for FUM high and low contamination events, the overall accuracy was 71.08% and 85.39% for the Illinois- and Iowa-centric models in 2011. Balanced accuracies were 60.23% and 50.00% for the Illinois- and Iowa-centric models. For Iowa's remaining years (testing data), the overall accuracy was 98.10%, and balanced accuracy was 50.00%.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>FUM-GBM analyses determined the top influential predictor for the Illinois-centric model was satellite-acquired normalized difference vegetation index (NDVI) (Veg_index) in March, whereas the top predictor for the Iowa-centric model was precipitation (PRCP) in October.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Significance and Novelty</h3>\\n \\n <p>Results indicate that meteorological and agronomic events, such as PRCP and Veg_index in early planting stages and during harvest, may influence the probability of high FUM levels in corn.</p>\\n </section>\\n </div>\",\"PeriodicalId\":9807,\"journal\":{\"name\":\"Cereal Chemistry\",\"volume\":\"101 6\",\"pages\":\"1261-1272\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cche.10824\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cereal Chemistry\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/cche.10824\",\"RegionNum\":4,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CHEMISTRY, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cereal Chemistry","FirstCategoryId":"97","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cche.10824","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
Predicting fumonisins in Iowa corn: Gradient boosting machine learning
Background and Objectives
Fumonisin (FUM), a secondary metabolite from Fusarium spp., poses major concerns for the United States corn industry. This study evaluated a prepublished Illinois-centric predictive model with historical Iowa FUM contamination data using gradient boosting machine (GBM) learning and compared influential predictors with an Iowa-centric model. Corn samples (n = 529) were collected from 2010, 2020, and 2021 in Iowa's 99 counties, and 2011 data were used for independent validation (n = 89).
Findings
Applying a 2 ppm (mg/kg) threshold for FUM high and low contamination events, the overall accuracy was 71.08% and 85.39% for the Illinois- and Iowa-centric models in 2011. Balanced accuracies were 60.23% and 50.00% for the Illinois- and Iowa-centric models. For Iowa's remaining years (testing data), the overall accuracy was 98.10%, and balanced accuracy was 50.00%.
Conclusions
FUM-GBM analyses determined the top influential predictor for the Illinois-centric model was satellite-acquired normalized difference vegetation index (NDVI) (Veg_index) in March, whereas the top predictor for the Iowa-centric model was precipitation (PRCP) in October.
Significance and Novelty
Results indicate that meteorological and agronomic events, such as PRCP and Veg_index in early planting stages and during harvest, may influence the probability of high FUM levels in corn.
期刊介绍：
Cereal Chemistry publishes high-quality papers reporting novel research and significant conceptual advances in genetics, biotechnology, composition, processing, and utilization of cereal grains (barley, maize, millet, oats, rice, rye, sorghum, triticale, and wheat), pulses (beans, lentils, peas, etc.), oilseeds, and specialty crops (amaranth, flax, quinoa, etc.). Papers advancing grain science in relation to health, nutrition, pet and animal food, and safety, along with new methodologies, instrumentation, and analysis relating to these areas are welcome, as are research notes and topical review papers.
The journal generally does not accept papers that focus on nongrain ingredients, technology of a commercial or proprietary nature, or that confirm previous research without extending knowledge. Papers that describe product development should include discussion of underlying theoretical principles.
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