Optimal bilateral filter and Convolutional Neural Network based denoising method of medical image measurements

Crop production can be greatly reduced due to various diseases, which seriously endangers food security. Thus, detecting plant diseases accurately is necessary and urgent. Traditional classification methods, such as naked-eye observation and laboratory tests, have many limitations, such as being time consuming and subjective. Currently, deep learning (DL) methods, especially those based on convolutional neural network (CNN), have gained widespread application in plant disease classification. They have solved or partially solved the problems of traditional classification methods and represent state-of-the-art technology in this field. In this work, we reviewed the latest CNN networks pertinent to plant leaf disease classification. We summarized DL principles involved in plant disease classification. Additionally, we summarized the main problems and corresponding solutions of CNN used for plant disease classification. Furthermore, we discussed the future development direction in plant disease classification.

https://www.mdpi.com/2077-0472/11/8/707/pdf

Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification

Rapid improvements in deep learning (DL) techniques have made it possible to detect and recognize objects from images. DL approaches have recently entered various agricultural and farming applications after being successfully employed in various fields. Automatic identification of plant diseases can help farmers manage their crops more effectively, resulting in higher yields. Detecting plant disease in crops using images is an intrinsically difficult task. In addition to their detection, individual species identification is necessary for applying tailored control methods. A survey of research initiatives that use convolutional neural networks (CNN), a type of DL, to address various plant disease detection concerns was undertaken in the current publication. In this work, we have reviewed 100 of the most relevant CNN articles on detecting various plant leaf diseases over the last five years. In addition, we identified and summarized several problems and solutions corresponding to the CNN used in plant leaf disease detection. Moreover, Deep convolutional neural networks (DCNN) trained on image data were the most effective method for detecting early disease detection. We expressed the benefits and drawbacks of utilizing CNN in agriculture, and we discussed the direction of future developments in plant disease detection.

/pdf/convolutional-neural-networks-in-detection-of-plant-leaf-289cpsct.pdf

Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review

Long short-term memory-based grade monitoring in froth flotation using a froth video sequence

With the rapid development of deep neural network (DNN), many DNN-based models for performance monitoring have been developed recently. However, some challenges still exist in the industrial performance monitoring: 1) different sample rates and time delays between the inputs and labeled performance; 2) a light-weight DNN architecture. Under this circumstance, we design a DNN named feature reconstruction-regression network (FR-R net) in this article. First, we extract the feature vector series as the input feature in order to capture the dynamic temporal information of the input data. Then, we design a feature reconstruction network with a weight-shared kernel network and fixed positional encoding to generate a reconstructed feature vector. Finally, we send the reconstructed feature vector into fully connected layers as a regression network to link the labeled performance. The effectiveness of the proposed FR-R net is validated on both a simulation case and an industrial froth flotation process.

Feature Reconstruction-Regression Network: A Light-Weight Deep Neural Network for Performance Monitoring in the Froth Flotation

A watershed segmentation algorithm based on an optimal marker for bubble size measurement

Accurate and in-time performance monitoring plays a great role in industrial processes. However, since the labeled performances are usually measured by some special devices with a relatively long-time interval, the current deep neural networks (DNNs) for the performance monitoring always treat them only as an output. But actually, in the industrial process like froth flotation, labeled performance at the previous moment could also offer valuable information for performance monitoring at the current moment, especially under unstable working conditions. Therefore, we propose a Siamese time series and difference network (STS-D net) which integrates input features at different time steps and labeled performance at the previous moment effectively. The proposed STS-D net includes two sub-networks. One is the Siamese time series network (STS-net) which aims to extract effective and uniform feature representations for the input time series at the current and previous moments; the other is the difference network which integrates the feature representations of the two input time series with labeled performance at the previous moment to predict the performance at the current moment in an incremental way. Effectiveness of the proposed STS-D net is validated in a real-world froth flotation process.

Siamese time series and difference networks for performance monitoring in the froth flotation process

The burst bubble rate has been strongly linked to froth stability, and thus, it is always used for performance prediction or modeling in the froth flotation. Due to different bubble motions and intensity changes as the bubbles move, the current burst bubble recognition methods are ineffective. Therefore, in this article, a similarity-based method for burst bubble recognition is proposed. The proposed method uses the local motion correction to deal with the different motion cases, and it selects the chamfer distance and the weighted normalized cross correlation as the similarity to decrease the influence of the intensity changes by the convex shape. Furthermore, the weighted normalized cross correlation is flexibly integrated with the template mask matching and the partial template matching. Extensive experiments have validated the effectiveness and robustness of the proposed method, where the precision and ${F}_{1}$ -score have been increased by at least 7.41% and 4.36%, respectively, compared with the current methods.

Hu Zhang

Papers

A watershed segmentation algorithm based on an optimal marker for bubble size measurement

Siamese time series and difference networks for performance monitoring in the froth flotation process

Long short-term memory-based grade monitoring in froth flotation using a froth video sequence

Feature Reconstruction-Regression Network: A Light-Weight Deep Neural Network for Performance Monitoring in the Froth Flotation

A Similarity-Based Burst Bubble Recognition Using Weighted Normalized Cross Correlation and Chamfer Distance