Eff2Net: An efficient channel attention-based convolutional neural network for skin disease classification

In this article we present a model of new deep learning composition for remote ship detection. Proposed architecture is composed of newly developed derivatives of ResNet, DenseNet and CNN composed into one global classifier. Since training of such model is demanding we have developed also a new proposition of transfer learning. Each of architectures was trained on different input data. In the final phase they are all composed into one global model for which training is finished by the use of augmented images from all the input collections. The proposed model of training enabled improved features of classification. Results of numerical experiments have shown that our newly proposed deep learning classifier with developed transfer learning model presents values of 99% Accuracy, 98% Precision, 99% Recall and 97% Specificity after training in only 40 iterations.

Deep neural network with transfer learning in remote object detection from drone

Reduction in chemical usage for crop management due to the environmental and health issues is a key area in achieving sustainable agricultural practices. One area in which this can be achieved is through the development of intelligent spraying systems which can identify the target for example crop disease or weeds allowing for precise spraying reducing chemical usage. Artificial intelligence and computer vision has the potential to be applied for the precise detection and classification of crops. In this paper, a study is presented that uses instance segmentation for the task of leaf and rust disease detection in apple orchards using Mask R-CNN. Three different Mask R-CNN network backbones (ResNet-50, MobileNetV3-Large and MobileNetV3-Large-Mobile) are trained and evaluated for the tasks of object detection, segmentation and disease detection. Segmentation masks on a subset of the Plant Pathology Challenge 2020 database are annotated by the authors, and these are used for the training and evaluation of the proposed Mask R-CNN based models. The study highlights that a Mask R-CNN model with a ResNet-50 backbone provides good accuracy for the task, particularly in the detection of very small rust disease objects on the leaves.

/pdf/leaf-disease-segmentation-and-detection-in-apple-orchards-e2mozl3e.pdf

Leaf Disease Segmentation and Detection in Apple Orchards for Precise Smart Spraying in Sustainable Agriculture

The early growth process of wheat is vulnerable to various factors, and poor growth leads to vacancies in the planting row. Therefore, the wheat images captured by unmanned aerial vehicles (UAV) are essential for monitoring the growth of wheat and preventing diseases and insect pests. This paper uses wheat images captured by UAV as a dataset, and propose a novel residual-capsule network with threshold convolution (RCTC) for segmentation of wheat plantation rows. The network is achieved by replacing the AveragePooling of the improved ResNet34 with Capsule. Since the capsule network represents the features by vectors, it can explain the direction of features and the relative positions between features. Therefore, deeper feature information can be extracted. In addition, to reduce redundant features and enhance effective features, a new threshold convolution is also proposed. Experiments on the wheat field dataset show that our proposed algorithm can effectively segment the wheat plantation rows images collected by UAV, and is superior to some existing well-known algorithms, and can provide scientific support and reference for the decision-making process of smart agriculture.

Residual-capsule networks with threshold convolution for segmentation of wheat plantation rows in UAV images

To identify the recent advancements in the development of plant disease detection and classification system based on Machine Learning (ML) and Deep Learning (DL) models In this study, we have collected more than 45 papers published during the year 2017-2020 from the peer-reviewed journals of different databases such as Scopus and Web of Science analogous to the keywords such as plant disease identification, recognition, and classification using ML and DL algorithms An organized way of analysis of various plant disease classification models has been shown in well-formed tables In this paper, we have conducted a systematic literature study on the applications of the state-of-the-art ML and DL algorithms such as Support Vector Machine (SVM), Neural Network (NN), K-Nearest Neighbor (KNN), Naive Bayes (NB), other few popular ML algorithms and AlexNet, GoogLeNet, VGGNet, and other few popular DL algorithms respectively for plant disease categorization Each stated algorithm is characterized through the corresponding processing methods such as image segmentation, feature extraction, along with the standardized experimental-setup metrics such as total number of training/testing dataset employed, number of diseases under considerations, type of classifier utilized, and the percentage of classification accuracy This work will be a beneficial resource for researchers to recognize any particular type of plant diseases through data-driven approaches The development of mobile-based applications using the studied ML/DL approaches will surely increase agricultural productivity

A Review of Machine Learning Approaches in Plant Leaf Disease Detection and Classification

Crop disease diagnosis is an essential step in crop disease treatment and is a hot issue in agricultural research. However, in agricultural production, identifying only coarse-grained diseases of crops is insufficient because treatment methods are different in different grades of even the same disease. Inappropriate treatments are not only ineffective in treating diseases but also affect crop yield and food safety. We combine IoT technology with deep learning to build an IoT system for crop fine-grained disease identification. This system can automatically detect crop diseases and send diagnostic results to farmers. We propose a multidimensional feature compensation residual neural network (MDFC-ResNet) model for fine-grained disease identification in the system. MDFC-ResNet identifies from three dimensions, namely, species, coarse-grained disease, and fine-grained disease and sets up a compensation layer that uses a compensation algorithm to fuse multidimensional recognition results. Experiments show that the MDFC-ResNet neural network has better recognition effect and is more instructive in actual agricultural production activities than other popular deep learning models.

/pdf/mdfc-resnet-an-agricultural-iot-system-to-accurately-aixg5t6b06.pdf

MDFC–ResNet: An Agricultural IoT System to Accurately Recognize Crop Diseases

The application of deep learning methods to diagnose diseases has become a new research topic in the medical field. In the field of medicine, skin disease is one of the most common diseases, and its visual representation is more prominent compared with the other types of diseases. Accordingly, the use of deep learning methods for skin disease image recognition is of great significance and has attracted the attention of researchers. In this study, we review 45 research efforts on the identification of skin disease by using deep learning technology since 2016. We analyze these studies from the aspects of disease type, data set, data processing technology, data augmentation technology, model for skin disease image recognition, deep learning framework, evaluation indicators, and model performance. Moreover, we summarize the traditional and machine learning-based skin disease diagnosis and treatment methods. We also analyze the current progress in this field and predict four directions that may become the research topic in the future. Our results show that the skin disease image recognition method based on deep learning is better than those of dermatologists and other computer-aided treatment methods in skin disease diagnosis, especially the multi deep learning model fusion method has the best recognition effect.

/pdf/deep-learning-in-skin-disease-image-recognition-a-review-1kcyd8abr8.pdf

Deep Learning in Skin Disease Image Recognition: A Review

Since the great ability of integration and proactive mobility, unmanned aerial vehicle (UAV) has been hot spot resent years. Motivated by the evolving requirements for monitoring and data collection, UAV has been considered as a part of collaborative, hybrid system called UAV integrated system which generates great benefits for the whole system. Due to the fragile of the nature of wireless transmission in UAV integrated system, the security of UAV integrated system becomes the real challenge in practice. It has been discussed in the perspective of physical layer security. However, the solutions usually involve two shortcomings in practice: the knowledge of the location of the eavesdropper has to be given as precondition, which is in contrast to the fact that the eavesdropper must hide itself properly; the other one is that non-convex problems are reduced to search the UAV trajectory for avoiding eavesdropper, it is not hard to see that non-convex optimization involves much computation while the consumption of power of UAV is critical in practice. In this paper, we propose a new strategy to deal with the security of UAV integrated system based on network coding such that the system doesn't need extra knowledge of eavesdropper and new solution indicates a linear algorithm which is more suitable to the demand of power consumption of UAV than non-convex optimization.

On security of UAV integrated system with network coding

The invention discloses a family pasture breeding benefit analysis system. The family pasture breeding benefit analysis system comprises a calculation module, a weight prediction system and a price prediction system.

Family pasture breeding benefit analysis system

The invention discloses a price prediction method. The price prediction method comprises the following steps: S1, obtaining original data; S2, preprocessing the data; S3, performing feature selection;S4, introducing meteorological factors to establish a price prediction model; and S5: performing model evaluation.

Hu Weijian

Papers

MDFC–ResNet: An Agricultural IoT System to Accurately Recognize Crop Diseases

Deep Learning in Skin Disease Image Recognition: A Review

On security of UAV integrated system with network coding

Family pasture breeding benefit analysis system

Price prediction method