Blended Features Classification of Leaf-Based Cucumber Disease Using Image Processing Techniques

Existing plant leaf disease detection approaches are based on features of extracting algorithms. These algorithms have some limits in feature selection for the diseased portion, but they can be used in conjunction with other image processing methods. Diseases of a plant can be classified from their symptoms. We proposed a cucumber leaf recognition approach, consisting of five steps: preprocessing, normalization, features extraction, features fusion, and classification. Otsu’s thresholding is implemented in preprocessing and Tan–Triggs normalization is applied for normalizing the dataset. During the features extraction step, texture and shape features are extracted. In addition, increasing the instances improves some characteristics. Through a principal component analysis approach, serial feature fusion is employed to provide a feature score. Fused features can be classified through a support vector machine. The accuracy of the Fine KNN is 94.30%, which is higher than the previous work in past papers.

LLFE: A Novel Learning Local Features Extraction for UAV Navigation Based on Infrared Aerial Image and Satellite Reference Image Matching

Local features extraction is a crucial technology for image matching navigation of an unmanned aerial vehicle (UAV), where it aims to accurately and robustly match a real-time image and a geo-referenced image to obtain the position update information of the UAV. However, it is a challenging task due to the inconsistent image capture conditions, which will lead to extreme appearance changes, especially the different imaging principle between an infrared image and RGB image. In addition, the sparsity and labeling complexity of existing public datasets hinder the development of learning-based methods in this research area. This paper proposes a novel learning local features extraction method, which uses local features extracted by deep neural network to find the correspondence features on the satellite RGB reference image and real-time infrared image. First, we propose a single convolution neural network that simultaneously extracts dense local features and their corresponding descriptors. This network combines the advantages of a high repeatability local feature detector and high reliability local feature descriptors to match the reference image and real-time image with extreme appearance changes. Second, to make full use of the sparse dataset, an iterative training scheme is proposed to automatically generate the high-quality corresponding features for algorithm training. During the scheme, the dense correspondences are automatically extracted, and the geometric constraints are added to continuously improve the quality of them. With these improvements, the proposed method achieves state-of-the-art performance for infrared aerial (UAV captured) image and satellite reference image, which shows 4–6% performance improvements in precision, recall, and F1-score, compared to the other methods. Moreover, the applied experiment results show its potential and effectiveness on localization for UAVs navigation and trajectory reconstruction application.