Classifying, Detecting, and Predicting Infestation Patterns of the Brown Planthopper in Rice Paddies

Abstract

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.