Automatic grading evaluation of winter wheat lodging based on deep learning.

Lodging is a crucial factor that limits wheat yield and quality in wheat breeding. Therefore, accurate and timely determination of winter wheat lodging grading is of great practical importance for agricultural insurance companies to assess agricultural losses and good seed selection. However, using artificial fields to investigate the inclination angle and lodging area of winter wheat lodging in actual production is time-consuming, laborious, subjective, and unreliable in measuring results. This study addresses these issues by designing a classification-semantic segmentation multitasking neural network model MLP_U-Net, which can accurately estimate the inclination angle and lodging area of winter wheat lodging. This model can also comprehensively, qualitatively, and quantitatively evaluate the grading of winter wheat lodging. The model is based on U-Net architecture and improves the shift MLP module structure to achieve network refinement and segmentation for complex tasks. The model utilizes a common encoder to enhance its robustness, improve classification accuracy, and strengthen the segmentation network, considering the correlation between lodging degree and lodging area parameters. This study used 82 winter wheat varieties sourced from the regional experiment of national winter wheat in the Huang-Huai-Hai southern area of the water land group at the Henan Modern Agriculture Research and Development Base. The base is located in Xinxiang City, Henan Province. Winter wheat lodging images were collected using the unmanned aerial vehicle (UAV) remote sensing platform. Based on these images, winter wheat lodging datasets were created using different time sequences and different UAV flight heights. These datasets aid in segmenting and classifying winter wheat lodging degrees and areas. The results show that MLP_U-Net has demonstrated superior detection performance in a small sample dataset. The accuracies of winter wheat lodging degree and lodging area grading were 96.1% and 92.2%, respectively, when the UAV flight height was 30 m. For a UAV flight height of 50 m, the accuracies of winter wheat lodging degree and lodging area grading were 84.1% and 84.7%, respectively. These findings indicate that MLP_U-Net is highly robust and efficient in accurately completing the winter wheat lodging-grading task. This valuable insight provides technical references for UAV remote sensing of winter wheat disaster severity and the assessment of losses.

Near-infrared spectroscopy combined with fuzzy fast pseudoinverse linear discriminant analysis to discriminate mee tea grades.

Mee tea, one of the major types of green tea in China, is often used for export because of its elegant appearance, high fragrance and strong taste. However, the quality of tea differs greatly due to the difference in raw material selection and production technology level. In order to accurately and quickly differentiate different grades of Mee tea, fuzzy fast pseudoinverse linear discriminant analysis (FFPLDA) was proposed based on fast pseudoinverse linear discriminant analysis (FPLDA) for extracting discriminant information from near-infrared (NIR) spectra. Firstly, NIR spectra of Mee tea samples were acquired, and then they were preprocessed by multiplicative scatter correlation (MSC). Secondly, the compression of data was achieved by principal component analysis (PCA). Thirdly, linear discriminant analysis (LDA), FPLDA, FFPLDA and fuzzy Foley-Sammon transformation (FFST) were respectively performed to retrieve discriminant information from NIR data. Finally, the K-nearest neighbor (KNN) was utilized to classify Mee tea grades. In this study, experimental results showed that the accuracy of FFPLDA was higher than that of LDA, FFST and FPLDA. Therefore, NIR spectroscopy coupled with FFPLDA and KNN has a good effect in discrimination of Mee tea grades and also a great application potential.

MIAPS: a web-based system for remotely accessing and presenting medical images.

MIAPS (medical image access and presentation system) is a web-based system designed for remotely accessing and presenting DICOM image. MIAPS is accessed with web browser through the Internet. MIAPS provides four features: DICOM image retrieval, maintenance, presentation and output. MIAPS does not intent to replace sophisticated commercial and open source packages, but it provides a web-based solution for teleradiology and medical image sharing. The system has been evaluated by 39 hospitals in China for 10 months.

Multiview locally linear embedding for effective medical image retrieval.

Content-based medical image retrieval continues to gain attention for its potential to assist radiological image interpretation and decision making. Many approaches have been proposed to improve the performance of medical image retrieval system, among which visual features such as SIFT, LBP, and intensity histogram play a critical role. Typically, these features are concatenated into a long vector to represent medical images, and thus traditional dimension reduction techniques such as locally linear embedding (LLE), principal component analysis (PCA), or laplacian eigenmaps (LE) can be employed to reduce the "curse of dimensionality". Though these approaches show promising performance for medical image retrieval, the feature-concatenating method ignores the fact that different features have distinct physical meanings. In this paper, we propose a new method called multiview locally linear embedding (MLLE) for medical image retrieval. Following the patch alignment framework, MLLE preserves the geometric structure of the local patch in each feature space according to the LLE criterion. To explore complementary properties among a range of features, MLLE assigns different weights to local patches from different feature spaces. Finally, MLLE employs global coordinate alignment and alternating optimization techniques to learn a smooth low-dimensional embedding from different features. To justify the effectiveness of MLLE for medical image retrieval, we compare it with conventional spectral embedding methods. We conduct experiments on a subset of the IRMA medical image data set. Evaluation results show that MLLE outperforms state-of-the-art dimension reduction methods.

Dual-force ISOMAP: a new relevance feedback method for medical image retrieval.

With great potential for assisting radiological image interpretation and decision making, content-based image retrieval in the medical domain has become a hot topic in recent years. Many methods to enhance the performance of content-based medical image retrieval have been proposed, among which the relevance feedback (RF) scheme is one of the most promising. Given user feedback information, RF algorithms interactively learn a user's preferences to bridge the "semantic gap" between low-level computerized visual features and high-level human semantic perception and thus improve retrieval performance. However, most existing RF algorithms perform in the original high-dimensional feature space and ignore the manifold structure of the low-level visual features of images. In this paper, we propose a new method, termed dual-force ISOMAP (DFISOMAP), for content-based medical image retrieval. Under the assumption that medical images lie on a low-dimensional manifold embedded in a high-dimensional ambient space, DFISOMAP operates in the following three stages. First, the geometric structure of positive examples in the learned low-dimensional embedding is preserved according to the isometric feature mapping (ISOMAP) criterion. To precisely model the geometric structure, a reconstruction error constraint is also added. Second, the average distance between positive and negative examples is maximized to separate them; this margin maximization acts as a force that pushes negative examples far away from positive examples. Finally, the similarity propagation technique is utilized to provide negative examples with another force that will pull them back into the negative sample set. We evaluate the proposed method on a subset of the IRMA medical image dataset with a RF-based medical image retrieval framework. Experimental results show that DFISOMAP outperforms popular approaches for content-based medical image retrieval in terms of accuracy and stability.