Abstract
In response to the difficulty of plant leaf disease detection and classification, this study proposes a novel plant leaf disease detection method called deep block attention SSD (DBA_SSD) for disease identification and disease degree classification of plant leaves. We propose three plant leaf detection methods, namely, squeeze-and-excitation SSD (Se_SSD), deep block SSD (DB_SSD), and DBA_SSD. Se_SSD fuses SSD feature extraction network and attention mechanism channel, DB_SSD improves VGG feature extraction network, and DBA_SSD fuses the improved VGG network and channel attention mechanism. To reduce the training time and accelerate the training process, the convolutional layers trained in the Image Net image dataset by the VGG model are migrated to this model, whereas the collected plant leaves disease image dataset is randomly divided into training set, validation set, and test set in the ratio of 8:1:1. We chose the PlantVillage dataset after careful consideration because it contains images related to the domain of interest. This dataset consists of images of 14 plants, including images of apples, tomatoes, strawberries, peppers, and potatoes, as well as the leaves of other plants. In addition, data enhancement methods, such as histogram equalization and horizontal flip were used to expand the image data. The performance of the three improved algorithms is compared and analyzed in the same environment and with the classical target detection algorithms YOLOv4, YOLOv3, Faster RCNN, and YOLOv4 tiny. Experiments show that DBA_SSD outperforms the two other improved algorithms, and its performance in comparative analysis is superior to other target detection algorithms.
Highlights
Plants are susceptible to various diseases, thereby affecting their quality and yield seriously
This study focuses on proposing a novel end-to-end plant disease detection algorithm called Deep Block Attention SSD (DBA_SSD) for plant leaves
The research methods on plant disease recognition mainly focuses on two aspects: one is disease recognition based on machine learning, and the general steps include diseased leaf image segmentation, feature extraction, and disease recognition; and the other is target recognition technology based on deep learning, wherein terminal end-to-end target detection is favored by many researchers because of its fast recognition speed and efficient feature extraction methods
Summary
Plants are susceptible to various diseases, thereby affecting their quality and yield seriously. The identification of plant diseases is an effective way to inhibit the rapid development of diseases and avoid their occurrence. People are used to making subjective judgments by crop disease category, and often disease detection is expert-based, making it a costly and error-prone process. Agricultural detection based on artificial intelligence, such as crop yield prediction [1], weed identification processing [2], and plant disease detection [3,4], is widely used with the development of artificial intelligence technology. Machine learning-based disease detection requires preprocessing the dataset, extracting the features of disease regions in the image using feature extraction algorithms, sending the obtained feature information to the classifier to obtain the model parameters, and obtaining the disease categories and the degree of disease to be detected. The needs of largescale planting, based on which it is important to research a fast end-to-end plants disease detection method, cannot be met
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