稻田褐飞虱侵染模式的分类、检测与预测

Christopher G. Harris, Y. Trisyono
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引用次数: 6

摘要

褐飞虱(Nilaparvata lugens)是一种对南亚、东南亚和东亚的水稻植物造成广泛损害的害虫。据估计,水稻作物产量损失的10-30%可归因于BPH。在本文中,我们开发了一种使用cnn检测和分类BPH形式的方法,然后使用CNN-LSTMs学习模型对BPH在几个水稻种植区的侵染迁移模式进行建模。该预测模型考虑了风速和风向、湿度、环境温度、杀虫剂的使用、BPH的形式、水稻品系和水稻幼苗间距等输入,以预测BPH虫害随时间的传播。检测和分类模型优于其他已知的BPH分类模型,准确率为89.33%。我们的预测模型在82.65%的时间内准确地模拟了受生物污染影响的区域(由灯陷阱计数确定)。这些模型可以帮助检测、分类和模拟其他农业害虫的侵害,从而改善大米的粮食安全。世界上9亿贫困人口的大部分热量摄入都依赖于大米这种主要作物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classifying, Detecting, and Predicting Infestation Patterns of the Brown Planthopper in Rice Paddies
The brown planthopper (BPH), Nilaparvata lugens (Stål), is a pest responsible for widespread damage to rice plants throughout South, Southeast, and East Asia. It is estimated that 10-30% of yield loss in rice crops is attributable to the BPH. In this paper, we develop a method to detect and classify the forms of BPH using CNNs and then model the infestation migration patterns of BPH in several rice-growing regions by using a CNN-LSTMs learning model. This prediction model considers inputs such as wind speed and direction, humidity, ambient temperature, the use of pesticides, the form of BPH, strain of rice, and spacing between rice seedlings to make predictions on the spread of BPH infestations over time. The detection and classification model outperformed other known BPH classification models, providing accuracy rates of 89.33%. Our prediction model accurately modeled the BPH-affected area 82.65% of the time (as determined by lamp trap counts). These models can help detect, classify, and model the infestations of other agricultural pests, improving food security for rice, the staple crop that 900 million of the world's poor depend on for most of their calorie intake.
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