Effective and efficient multi-crop pest detection based on deep learning object detection models

Traditional machine learning-based pest classification methods are a tedious and time-consuming process. A method of multi-class pest detection based on deep learning and convolutional neural networks could be the solution. It automatically extracts the complex features of different pests from the crop pest images. In this paper, various significant deep learning-based object detection models like SSD, EfficientDet, Faster R-CNN, and CenterNet are implemented based on the Tensorflow Object Detection framework. Several significant networks like MobileNet V2, ResNet101 V1, Inception ResNet V2, EfficientNet, and HourGlass104 are employed as backbone networks for these models to extract the different features of the pests. Object detection models are capable of identifying and locating pests in crops. Initially, these models are pre-trained with the COCO dataset and later be fine-tuned to the target pest dataset of 20 different pest classes. After conducting experiments on these models using the pest dataset, we demonstrate that Faster R-CNN ResNet101 V1 outperformed every other model and achieved mAP of 74.77%. Additionally, it is developed as a lightweight model, whose size is similar to 9 MB, and can detect pest objects in 130 milliseconds per image, allowing it to be used on resources-constrained devices commonly used by farmers.