Detecting diseases in apple tree leaves using FPN-ISResNet-Faster RCNN

• Published in: European journal of remote sensing Vol. 56; no. 1
• Main Authors: Hou, Jingwei, Yang, Chen, He, Yonghong, Hou, Bo
• Format: Journal Article
• Language: English
• Published: Cagiari Taylor & Francis 31.12.2023; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Accuracy; Apple leaf disease; Apples; Artificial neural networks; convolutional neural network; Crop diseases; Datasets; Deep learning; Engineering; Environmental engineering; Faster RCNN; Feature extraction; FPN; Fruit trees; Fruits; ISResNet; Leaves; Machine learning; Neural networks; Plant diseases
• ISSN: 2279-7254; 2279-7254
• DOI: 10.1080/22797254.2023.2186955

Apple leaf diseases typified by small disease spots are generally difficult to detect in images. This study proposes a deep learning model called the feature pyramid networks (FPNs) -inception squeeze-and-excitation ResNet (ISResNet)-Faster RCNN (region with convolutional neural network) model to improve the accuracy of detecting apple leaf diseases. Apple leaf diseases were identified, evaluated, and validated by using the FPN-ISResNet-Faster RCNN. The results were compared with those obtained by the single-shot multibox detector (SSD), Faster RCNN, and ISResNet-Faster RCNN. The detection accuracies obtained by using different feature extraction networks, positions and numbers of SE, inception modules, scales of FPN structures, and scales of anchor frames were also compared. The results showed that the values of average precision (AP), and APs with the thresholds of the intersection over union of 0.5 and 0.75 (AP50 and AP75), obtained from the FPN-ISResNet-Faster RCNN were 62.71%, 93.68%, and 70.94%, respectively, which are higher than those of the SSD, VGG-Faster RCNN, GoogleNet-Faster RCNN, ResNet50-Faster RCNN, ResNeXt-Faster RCNN, and ISResNet-Faster RCNN. FPN-ISResNet-Faster RCNN was shown to be able to detect diseases in apple leaves with high accuracy and generalizability.