Plant leaf disease classification using EfficientNet deep learning model

Deep feature based rice leaf disease identification using support vector machine

Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition and pest range assessment, etc. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this paper, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this work will be a valuable resource for researchers who study the detection of plant diseases and insect pests. At the same time, we also discussed some of the current challenges and problems that need to be resolved.

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Plant Disease Detection and Classification by Deep Learning—A Review

Plant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study plant diseases and pests identification has become a research issue of great concern to researchers. This review provides a definition of plant diseases and pests detection problem, puts forward a comparison with traditional plant diseases and pests detection methods. According to the difference of network structure, this study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized. Common datasets are introduced, and the performance of existing studies is compared. On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.

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Plant diseases and pests detection based on deep learning: a review.

Few-Shot Learning approach for plant disease classification using images taken in the field

Using deep transfer learning for image-based plant disease identification

Food security, which has currently attracted much attention, requires minimizing crop damage by timely detection of plant diseases. Therefore, the automatic identification and diagnosis of plant diseases are highly desired in agricultural information. In this paper, we propose a novel approach to identify plant diseases. The method is divided into two parts: starting with the enhancement of the artificial neural network, the extracted pixel values and feature values are input to the enhanced artificial neural network for the image segmentation; then, following the establishment of a CNN based model, the segmented images are input to the proposed CNN model for the image classification. The proposed approach shows an impressive performance in the experimental analyses. It achieved an average accuracy of 93.75% to identify the crop diseases under the complex background conditions, and the validation accuracy was, on average, 10% higher than that of the conventional method. Additionally, almost all the plant disease samples were correctly detected by the proposed approach, and thus the recall rate achieved 100%. The experimental finding presents a substantial performance relative to other state-of-the-art methods and demonstrates the efficiency and extensibility of the proposed approach.

Jinxiu Chen

Papers

Using deep transfer learning for image-based plant disease identification

A cognitive vision method for the detection of plant disease images