High-Performance Plant Pest and Disease Detection Based on Model Ensemble with Inception Module and Cluster Algorithm

Abstract

Protecting crop yields is the most important aspect of agricultural production, and one of the important measures in preserving yields is the control of crop pests and diseases; therefore, the identification of crop pests and diseases is of irreplaceable importance. In recent years, with the maturity of computer vision technology, more possibilities have been provided for implementing plant disease detection. However, although deep learning methods are widely used in various computer vision tasks, there are still limitations and obstacles in practical applications. Traditional deep learning-based algorithms have some drawbacks in this research area: (1) Recognition accuracy and computational speed cannot be combined. (2) Different pest and disease features interfere with each other and reduce the accuracy of pest and disease diagnosis. (3) Most of the existing researches focus on the recognition efficiency and ignore the inference efficiency, which limits the practical production application. In this study, an integrated model integrating single-stage and two-stage target detection networks is proposed. The single-stage network is based on the YOLO network, and its internal structure is optimized; the two-stage network is based on the Faster-RCNN, and the target frame size is first clustered using a clustering algorithm in the candidate frame generation stage to improve the detection of small targets. Afterwards, the two models are integrated to perform the inference task. For training, we use transfer learning to improve the model training speed. Finally, among the 37 pests and 8 diseases detected, this model achieves 85.2% mAP, which is much higher than other comparative models. After that, we optimize the model for the poor detection categories and verify the generalization performance on open source datasets. In addition, in order to quickly apply this method to real-world scenarios, we developed an application embedded in this model for the mobile platform and put the model into practical agricultural use.

A Novel Crop Pest Detection Model Based on YOLOv5

“…Recent researches [54][55][56][57][58][59][60] exhibit the YOLO series' efficacy in addressing diverse crop-related challenges. However, CNNs base on DL approaches, have limitations in practical pest detection scenarios [61], restricting their real-world applicability. Under complex backgrounds and the presence of different pest species, We propose YOLOv5s-pest (HSPPF + C3NCBAM + g 3 Conv + Soft-NMS) to improve detection precision, where HSPPF module is strategically crafted to fortify the model's capacity to extract multi-scale receptive field information within feature maps.…”

A deep learning approach for electric motor fault diagnosis based on modified InceptionV3

“…It is renowned for its innovative inception module, which incorporates multiple convolutional filters of different sizes in parallel, allowing the network to capture both local and global features efficiently. This unique structure helps alleviate the vanishing gradient problem 41 and enables effective feature extraction at various scales, which is the key advantage of InceptionV3. It utilizes multiple convolutional kernels of different sizes in parallel to process input data, enabling the capture of features across diverse scales.…”

“…It is renowned for its innovative inception module, which incorporates multiple convolutional filters of different sizes in parallel, allowing the network to capture both local and global features efficiently. This unique structure helps alleviate the vanishing gradient problem 41 www.nature.com/scientificreports/ sizes in parallel to process input data, enabling the capture of features across diverse scales. This enhances the network's ability to perceive objects and structures of different sizes.…”

An Advancing GCT-Inception-ResNet-V3 Model for Arboreal Pest Identification

“…Automated systems using machine learning offer a promising solution, yet they require vast amounts of data and computational resources, often lacking in field conditions [14]. The ResNet architecture, with its deep residual learning framework [15,16], offers a potential improvement in learning complex features for accurate pest identification, yet like other advanced CNN (Convolutional Neural Network) architectures (e.g., VGG16 [17,18], DenseNet [19], and Inception-V3 [20][21][22]), it faces challenges in deployment on resource-constrained mobile devices, thus restricting their accessibility. Additionally, memory-efficient CNN architectures [23,24], although apt for mobile environments, typically sacrifice classification precision.…”