Traits preferred by birds to attack on standing wheat crop under temperate condition and its prediction through random forest model

A. K. Singh, L. Singh, J. K. Yasin, R. K. Raman, P. Sundram, P. Jeet, Kirti Saurabh, V. Prakash, A. Upadhyaya, U. Kumar
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Abstract

Wheat is an important cool season cereal crop of India, production and quality get compromised due to biotic and abiotic stresses. Wheat is susceptible to a variety of diseases and insect pests, including birds. Birds attack on standing crop are not very often and extent and pattern of damage varies considerably, however, its management practices is generally not taken in to account. The bird preferred to attack wheat crops while they were in the reproductive stage, especially during milking to dough stage. The data collected over the course of a two-year field study in the Kashmir valley at Research Farm Srinagar (Jammu and Kashmir) has been subjected to Random Forest model to determine whether there is a trait-specific specific preference for attack if any. Random forest (RF) model identified five major features out of 13 selected features of wheat germplasm with seventy percent accuracy for parrot birds’ preferred to attack on wheat germplasm. RF Model suggests that taller height, having long spikes and a greater number of ears, along with more biomass and grain yields, could be possible traits that bird pest target during the reproductive stage. It was inferenced that the average performance of the random forest model demonstrated with model accuracy (71%), sensitivity (0.77), kappa 0.41 and receiver-operating-characteristic (ROC-0.74) and showed good fit for classification of parrot attack on wheat germplasm.
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温带条件下鸟类攻击直立小麦作物的偏好性状及其随机森林模型预测
小麦是印度重要的冷季谷类作物,其产量和质量受到生物和非生物胁迫的影响。小麦易受多种病虫害的影响,包括鸟类。鸟类对直立作物的攻击并不经常发生,损害的程度和模式差别很大,然而,其管理措施通常没有被考虑在内。这种鸟喜欢在小麦作物的繁殖阶段,特别是在挤奶到面团阶段攻击小麦作物。在克什米尔山谷斯利那加研究农场(查谟和克什米尔)进行的为期两年的实地研究过程中收集的数据已被纳入随机森林模型,以确定是否存在针对特定特征的特定攻击偏好(如果有的话)。随机森林(RF)模型从13个小麦种质特征中识别出5个主要特征,对鹦鹉对小麦种质的偏好攻击准确率达到70%。RF模型表明,较高的高度,长穗和更多的穗,以及更多的生物量和粮食产量,可能是鸟类害虫在繁殖阶段的目标特征。结果表明,随机森林模型的平均性能为模型精度(71%)、灵敏度(0.77)、kappa(0.41)和接收者操作特性(ROC-0.74),对小麦种质鹦鹉攻击分类具有较好的拟合性。
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