外文摘要：The early identification of plant diseases is crucial for preventing the loss of crop production. Recently, the advancement of deep learning has significantly improved the identification of plant leaf diseases. However, most approaches depend on a single convolutional neural network (CNN) to extract the leaf features, ignoring the opportunity to take full advantage of the feature richness available in the images. This paper explores a novel CNN model with multiple automated feature extractors, namely, dense fusion CNN (DFNet), for classifying plant leaf diseases. DFNet aims to increase the diversity of extracted features in order to improve discrimination. Instead of using a single-CNN model, DFNet relies on a double-pretrained CNN model, MobileNetV2 and NASNetMobile, as the feature extractor. The features extracted from each CNN are fused in the fusion layer using a fully connected network. The proposed method was evaluated using corn (Zea mays L.) and coffee (Coffea canephora) leaf disease datasets and compared to the existing models. The experiment showed that DFNet is superior and consistent to other CNN methods by achieving an accuracy of 97.53% for corn leaf diseases and 94.65% for coffee leaf diseases.
作者：Prakosa, Setya Widyawan;Koppen, Mario;Leu, Jenq-Shiou;Avian, Cries;Faisal, Muhamad
作者单位：Kyushu Inst Technol;Natl Taiwan Univ Sci & Technol
期刊名称：AGRONOMY JOURNAL
期刊影响因子：0.0
出版年份：2024
出版刊次：116（3）
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台