The early detection of diseases is important in agriculture for an efficient crop yield. The bacterial spot, late blight, septoria leaf spot and yellow curved leaf diseases affect the crop quality of tomatoes. Automatic methods for classification of plant diseases also help taking action after detecting the symptoms of leaf diseases. This paper presents a Convolutional Neural Network (CNN) model and Learning Vector Quantization (LVQ) algorithm based method for tomato leaf disease detection and classification. The dataset contains 500 images of tomato leaves with four symptoms of diseases. We have modeled a CNN for automatic feature extraction and classification. Color information is actively used for plant leaf disease researches. In our model, the filters are applied to three channels based on RGB components. The LVQ has been fed with the output feature vector of convolution part for training the network. The experimental results validate that the proposed method effectively recognizes four different types of tomato leaf diseases.

Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm

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.

Deep learning techniques, and in particular Convolutional Neural Networks (CNNs), have led to significant progress in image processing. Since 2016, many applications for the automatic identification of crop diseases have been developed. These applications could serve as a basis for the development of expertise assistance or automatic screening tools. Such tools could contribute to more sustainable agricultural practices and greater food production security. To assess the potential of these networks for such applications, we survey 19 studies that relied on CNNs to automatically identify crop diseases. We describe their profiles, their main implementation aspects and their performance. Our survey allows us to identify the major issues and shortcomings of works in this research area. We also provide guidelines to improve the use of CNNs in operational contexts as well as some directions for future research.

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Convolutional Neural Networks for the Automatic Identification of Plant Diseases.

Performance Analysis of Deep Learning CNN Models for Disease Detection in Plants using Image Segmentation

Recent advances in image processing techniques for automated leaf pest and disease recognition – A review

Three-channel convolutional neural networks for vegetable leaf disease recognition

Plant species recognition methods using leaf image: Overview

Identifying the interactions of the drug-target is central to the cognate areas including drug discovery and drug reposition. Although the high-throughput biotechnologies have made tremendous progress, the indispensable clinical trials remain to be expensive, laborious, and intricate. Therefore, a convenient and reliable computer-aided method has become the focus on inferring drug-target interactions (DTIs). In this research, we propose a novel computational model integrating a pyramid histogram of oriented gradients (PHOG), Position-Specific Scoring Matrix (PSSM), and rotation forest (RF) classifier for identifying DTIs. Specifically, protein primary sequences are first converted into PSSMs to describe the potential biological evolution information. After that, PHOG is employed to mine the highly representative features of PSSM from multiple pyramid levels, and the complete describers of drug-target pairs are generated by combining the molecular substructure fingerprints and PHOG features. Finally, we feed the complete describers into the RF classifier for effective prediction. The experiments of 5-fold Cross-Validations (CV) yield mean accuracies of 88.96%, 86.37%, 82.88%, and 76.92% on four golden standard data sets (enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively). Moreover, the paper also conducts the state-of-art light gradient boosting machine (LGBM) and support vector machine (SVM) to further verify the performance of the proposed model. The experimental outcomes substantiate that the established model is feasible and reliable to predict DTIs. There is an excellent prospect that our model is capable of predicting DTIs as an efficient tool on a large scale.

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An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints.

The identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. However, the traditional high-throughput techniques based on clinical trials are costly, cumbersome, and time-consuming for identifying DTIs. Hence, new intelligent computational methods are urgently needed to surmount these defects in predicting DTIs. In this paper, we propose a novel computational method that combines position-specific scoring matrix (PSSM), elastic net based sparse features extraction, and rotation forest (RF) classifier. Specifically, we converted each protein primary sequence into PSSM, which contains biological evolutionary information. Then we extract the hidden sparse feature descriptors in PSSM by elastic net based sparse feature extraction method (ESFE). After that, we fuse them with the features of drug, which are represented by molecular fingerprints. Finally, rotation forest classifier works on detecting the potential drug-target interactions. When performing the proposed method by the experiments of fivefold cross validation (CV) on enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor datasets, this method achieves average accuracies of 90.32%, 88.91%, 80.65%, and 79.73%, respectively. We also compared the proposed model with the state-of-the-art support vector machine (SVM) classifier and other effective methods on the same datasets. The comparison results distinctly indicate that the proposed model possesses the efficient and robust ability to predict DTIs. We expect that the new model will be able to take effects on predicting massive DTIs.

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Wen-Zhun Huang

Papers

Three-channel convolutional neural networks for vegetable leaf disease recognition

Plant species recognition methods using leaf image: Overview

An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints.

A Sparse Feature Extraction Method with Elastic Net for Drug-Target Interaction Identification