外文摘要：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.
外文关键词：Disease severity;ANN model;LASSO regression;Pest incidence;Pod bug;Pod rot
作者：Verma, Rajshree;Kushwaha, Kailash Pati Singh;Bijlwan, Amit;Bisht, Ashish Singh
作者单位：Govind Ballabh Pant Univ Agr & Technol
期刊名称：AUSTRALASIAN PLANT PATHOLOGY
期刊影响因子：0.0
出版年份：2024
出版刊次：53（3）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台