A High-Precision Detection Method of Apple Leaf Diseases Using Improved Faster R-CNN

Abstract

Apple leaf diseases seriously affect the sustainable production of apple fruit. Early infection monitoring of apple leaves and timely disease control measures are the key to ensuring the regular growth of apple fruits and achieving a high-efficiency economy. Consequently, disease detection schemes based on computer vision can compensate for the shortcomings of traditional disease detection methods that are inaccurate and time-consuming. Nowadays, to solve the limitations ranging from complex background environments to dense and small characteristics of apple leaf diseases, an improved Faster region-based convolutional neural network (Faster R-CNN) method was proposed. The advanced Res2Net and feature pyramid network architecture were introduced as the feature extraction network for extracting reliable and multi-dimensional features. Furthermore, RoIAlign was also employed to replace RoIPool so that accurate candidate regions will be produced to address the object location. Moreover, soft non-maximum suppression was applied for precise detection performance of apple leaf disease when making inferences to the images. The improved Faster R-CNN structure behaves effectively in the annotated apple leaf disease dataset with an accuracy of 63.1% average precision, which is higher than other object detection methods. The experiments proved that our improved Faster R-CNN method provides a highly precise apple leaf disease recognition method that could be used in real agricultural practice.

BLSENet: A Novel Lightweight Bilinear Convolutional Neural Network Based on Attention Mechanism and Feature Fusion Strategy for Apple Leaf Disease Classification

“…Te accuracy of RFCA ResNet is 89.61%. It is superior to other methods and has good generalization performance, which has certain theoretical signifcance and practical value [15]. Gaikwad et al used CNN to classify leaf disease.…”

APEIOU Integration for Enhanced YOLOV7: Achieving Efficient Plant Disease Detection

“…Building upon this foundation, researchers have conducted various studies. For instance, to address the limitations of complex background environments and sparse features in apple leaf disease detection, some proposed an improved Faster R-CNN method integrating advanced Res2Net and feature pyramid network architectures for reliable multidimensional feature extraction [18]. Another group introduced MFaster R-CNN, employing a hybrid loss function constructed using a central cost function and four pre-trained structures to enhance maize disease detection in real-world environments [19].…”

Object–Environment Fusion of Visual System for Automatic Pear Picking

“…To reduce the impact of irrelevant features in identifying bruises on apples, Hou et al [21] improved Faster R-CNN by incorporating a feature pyramid network (FPN) into ResNet50 [22] and integrating a normalization-based attention module into the residual network. In the study by Gong et al [23], in order to improve Faster R-CNN, Res2Net and a feature pyramid network were used as the backbone network, and RoIAlign was replaced with RoIPool. The results demonstrated that the improved Faster R-CNN achieved average precision of 63.1%.…”