A Smartphone-Based Application for Scale Pest Detection Using Multiple-Object Detection Methods

Hui-Jun Cheng,Che-Lun Hung,Jian-Wen Chen,Wan-Ju Lin,Shu-Pei Chen,Chun-Yuan Lin

doi:10.3390/electronics10040372

Abstract

Taiwan’s economy mainly relies on the export of agricultural products. If even the suspicion of a pest is found in the crop products after they are exported, not only are the agricultural products returned but the whole batch of crops is destroyed, resulting in extreme crop losses. The species of mealybugs, Coccidae, and Diaspididae, which are the primary pests of the scale insect in Taiwan, can not only lead to serious damage to the plants but also severely affect agricultural production. Hence, to recognize the scale pests is an important task in Taiwan’s agricultural field. In this study, we propose an AI-based pest detection system for solving the specific issue of detection of scale pests based on pictures. Deep-learning-based object detection models, such as faster region-based convolutional networks (Faster R-CNNs), single-shot multibox detectors (SSDs), and You Only Look Once v4 (YOLO v4), are employed to detect and localize scale pests in the picture. The experimental results show that YOLO v4 achieved the highest classification accuracy among the algorithms, with 100% in mealybugs, 89% in Coccidae, and 97% in Diaspididae. Meanwhile, the computational performance of YOLO v4 has indicated that it is suitable for real-time application. Moreover, the inference results of the YOLO v4 model further help the end user. A mobile application using the trained scale pest recognition model has been developed to facilitate pest identification in farms, which is helpful in applying appropriate pesticides to reduce crop losses.

Highlights

Taiwan is a rich source of valuable agriculture products, and agriculture plays an important role in Taiwan’s economic production
Use of the convolutional neural networks (CNNs) model to analyze big data on the cloud has helped achieve state-of-the-art results in many tasks involved in recognizing pests and diseases
This paper develops an intelligent pest identification systraining detection models, saving the toplatform the cloud of objectthe detection models, saving the models to models the cloud ofplatform database is database essential tem based on a cloud platform, which employs the Things technology to upis essential for further inference

Summary

Introduction

Taiwan is a rich source of valuable agriculture products, and agriculture plays an important role in Taiwan’s economic production. The climate of Taiwan is hot and humid, leading to various threats from pests in crop production. The influence of climate change is increasingly serious and the threats from pests to crops are becoming more severe and unpredictable. Scale insects are phytophagous insects that eat green plants [1]. These insects suck the sap of plant organs, especially leaves, fruits, stems, and roots, which causes sooty mold disease. This disease affects photosynthesis and leads to tissue infection, resulting in damage to the plants and a reduction in the market value of the plant commodities in terms of quality and quantity. As farmers encounter the issue of pest attacks, they rely on their own previous experiences and knowledge to make a diagnosis

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