Enhancing urad bean (Vigna mungo L.) crop management with machine learning: Predictive analysis of pod rot severity and pod bug incidence patterns

IF 0.9 4区农林科学 Q4 PLANT SCIENCES Australasian Plant Pathology Pub Date : 2024-03-16 DOI:10.1007/s13313-024-00967-7

{"title":"Enhancing urad bean (Vigna mungo L.) crop management with machine learning: Predictive analysis of pod rot severity and pod bug incidence patterns","authors":"","doi":"10.1007/s13313-024-00967-7","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Urad bean (<em>Vigna mungo</em> L.), commonly known as black gram, is an important pulse crop in Indian agriculture. However, the crop confronts significant challenges due to diseases, including pod rot caused by <em>Fusarium</em> sp, and pest attacks by the pod bug (<em>Clavigralla gibbosa</em>). Accurate prediction of disease severity and pest incidence is essential for formulating effective management strategies to ensure sustainable crop production. A comprehensive field experiment was conducted at the Crop Research Center, Pantnagar, Uttarakhand, during the rainy seasons of 2021 and 2022. The primary objective was to analyze the behavioral patterns of disease severity and pod bug infestations in urad bean. Data on pod rot disease severity and pest incidence were meticulously recorded on a weekly basis. Four Machine Learning approaches, namely ANN, Lasso, Ridge, and Random Forest, were trained and tested to understand the influence of meteorological parameters on pod rot and pest severity. The Random Forest model exhibited superior generalization performance in predicting both disease severity and pest incidence, closely followed by Ridge regression and Lasso regression. The ANN model showed slightly higher testing error metrics. Notably, the Random Forest model demonstrated effective control overfitting, yielding maximum R-squared values of 0.70 and 0.82 for pod rot and pest incidence, respectively. The study’s findings offer valuable insights for agricultural stakeholders in selecting appropriate prediction models to optimize crop management practices and promote sustainable agriculture.</p>","PeriodicalId":8598,"journal":{"name":"Australasian Plant Pathology","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2024-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Australasian Plant Pathology","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1007/s13313-024-00967-7","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PLANT SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

Urad bean (Vigna mungo L.), commonly known as black gram, is an important pulse crop in Indian agriculture. However, the crop confronts significant challenges due to diseases, including pod rot caused by Fusarium sp, and pest attacks by the pod bug (Clavigralla gibbosa). Accurate prediction of disease severity and pest incidence is essential for formulating effective management strategies to ensure sustainable crop production. A comprehensive field experiment was conducted at the Crop Research Center, Pantnagar, Uttarakhand, during the rainy seasons of 2021 and 2022. The primary objective was to analyze the behavioral patterns of disease severity and pod bug infestations in urad bean. Data on pod rot disease severity and pest incidence were meticulously recorded on a weekly basis. Four Machine Learning approaches, namely ANN, Lasso, Ridge, and Random Forest, were trained and tested to understand the influence of meteorological parameters on pod rot and pest severity. The Random Forest model exhibited superior generalization performance in predicting both disease severity and pest incidence, closely followed by Ridge regression and Lasso regression. The ANN model showed slightly higher testing error metrics. Notably, the Random Forest model demonstrated effective control overfitting, yielding maximum R-squared values of 0.70 and 0.82 for pod rot and pest incidence, respectively. The study’s findings offer valuable insights for agricultural stakeholders in selecting appropriate prediction models to optimize crop management practices and promote sustainable agriculture.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

利用机器学习加强蚕豆（Vigna mungo L.）作物管理：豆荚腐烂严重程度和豆荚虫发生模式的预测分析

摘要乌拉豆（Vigna mungo L.），俗称黑糯米，是印度农业中重要的豆类作物。然而，该作物面临着巨大的病害挑战，包括由镰刀菌引起的豆荚腐烂病和由豆荚虫（Clavigralla gibbosa）引起的虫害。准确预测病害严重程度和虫害发生率对于制定有效的管理策略以确保作物的可持续生产至关重要。2021 年和 2022 年雨季期间，在北阿坎德邦潘特纳加的作物研究中心进行了一项综合田间试验。主要目的是分析乌豆病害严重程度和豆荚虫侵扰的行为模式。荚腐病严重程度和虫害发生率的数据每周都有详细记录。对四种机器学习方法（即 ANN、Lasso、Ridge 和随机森林）进行了训练和测试，以了解气象参数对豆荚腐烂病和虫害严重程度的影响。随机森林模型在预测病害严重程度和虫害发生率方面表现出卓越的泛化性能，岭回归和拉索回归紧随其后。ANN 模型的测试误差指标略高。值得注意的是，随机森林模型有效地控制了过度拟合，对豆荚腐烂病和虫害发生率的最大 R 平方值分别为 0.70 和 0.82。研究结果为农业利益相关者提供了宝贵的见解，帮助他们选择适当的预测模型，优化作物管理方法，促进农业可持续发展。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Australasian Plant Pathology 生物-植物科学

CiteScore

2.90

自引率

0.00%

发文量

审稿时长

3 months

期刊介绍： Australasian Plant Pathology presents new and significant research in all facets of the field of plant pathology. Dedicated to a worldwide readership, the journal focuses on research in the Australasian region, including Australia, New Zealand and Papua New Guinea, as well as the Indian, Pacific regions. Australasian Plant Pathology is the official journal of the Australasian Plant Pathology Society.