An automatic identification system for citrus greening disease (Huanglongbing) using a YOLO convolutional neural network

Abstract

Huanglongbing (HLB), or citrus greening disease, has complex and variable symptoms, making its diagnosis almost entirely reliant on subjective experience, which results in a low diagnosis efficiency. To overcome this problem, we constructed and validated a deep learning (DL)-based method for detecting citrus HLB using YOLOv5l from digital images. Three models (Yolov5l-HLB1, Yolov5l-HLB2, and Yolov5l-HLB3) were developed using images of healthy and symptomatic citrus leaves acquired under a range of imaging conditions. The micro F1-scores of the Yolov5l-HLB2 model (85.19%) recognising five HLB symptoms (blotchy mottling, “red-nose” fruits, zinc-deficiency, vein yellowing, and uniform yellowing) in the images were higher than those of the other two models. The generalisation performance of Yolov5l-HLB2 was tested using test set images acquired under two photographic conditions (conditions B and C) that were different from that of the model training set condition (condition A). The results suggested that this model performed well at recognising the five HLB symptom images acquired under both conditions B and C, and yielded a micro F1-score of 84.64% and 85.84%, respectively. In addition, the detection performance of the Yolov5l-HLB2 model was better for experienced users than for inexperienced users. The PCR-positive rate of Candidatus Liberibacter asiaticus (CLas) detection (the causative pathogen for HLB) in the samples with five HLB symptoms as classified using the Yolov5l-HLB2 model was also compared with manual classification by experts. This indicated that the model can be employed as a preliminary screening tool before the collection of field samples for subsequent PCR testing. We also developed the ‘HLBdetector’ app using the Yolov5l-HLB2 model, which allows farmers to complete HLB detection in seconds with only a mobile phone terminal and without expert guidance. Overall, we successfully constructed a reliable automatic HLB identification model and developed the user-friendly ‘HLBdetector’ app, facilitating the prevention and timely control of HLB transmission in citrus orchards.

Maize-YOLO: A New High-Precision and Real-Time Method for Maize Pest Detection

“…In this study, we propose a real-time intelligent maize pest detection method (Maize-YOLO) that can well balance the relationship between accuracy, speed, and computational effort and outperforms current state-of-the-art real-time object detection algorithms. Our Maize-YOLO differs from current related research [ 28 , 29 , 30 ] in that it achieves a high level of detection accuracy while maintaining high speed. The method provides accurate pest detection and identification not only for maize crops but also for other crops, enabling end-to-end real-time pest detection.…”

Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning

“…YOLO is capable of identifying objects by localizing them with a bounding box and, at the same time, classifying them according to the probability to belong to a given class 22 . The YOLO series represents one-stage algorithms, which are more suited to practical applications than two-stage algorithms (such as Faster R-CNN) owing to their better balance between accuracy and speed 23 . Zhong et al 24 pointed that the YOLO model was superior to the Faster R-CNN model for the Helicobacter pylori detection task.…”

Fast pulse detection algorithm based on improved ghost convolution

“…To improve the accuracy of fast pulse detection in sonar, this paper designed multiple network models based on the YOLOV5L [1][2][3][4][5] model, with the addition of ghost convolutional modules that effectively reduce the number of parameters and improve detection accuracy. Furthermore, the influence of the ghost convolutional module position on the detection accuracy is further verified, and the time and space ghost graph convolutional modules are combined to further reduce the number of network parameters at the network structure level.…”