☆ 4.7 Review

Trends in vision-based machine learning techniques for plant disease identification: A systematic review

EXPERT SYSTEMS WITH APPLICATIONS (2022)

Related references

Note: Only part of the references are listed.
Article Agriculture, Multidisciplinary

Automatic detection and severity analysis of grape black measles disease based on deep learning and fuzzy logic

Miaomiao Ji et al.

Summary: This paper proposes an automatic detection and severity analysis method for grape black measles disease based on deep learning and fuzzy logic. The method combines image analysis with statistical calculation to effectively classify grape leaves with different disease risks.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2022)

Add to Collection
Article Agriculture, Multidisciplinary

Assessment of state-of-the-art deep learning based citrus disease detection techniques using annotated optical leaf images

Sathian Dananjayan et al.

Summary: This study developed a new dataset comprising images of infected citrus leaves with precise annotations, and used machine learning and deep learning models to detect citrus leaf diseases, evaluating the performance of each model. The results showed that Scaled YOLOv4 P7 and CenterNet2 with Res2Net 101 DCN-BiFPN achieved high accuracy and early prediction of citrus leaf diseases.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2022)

Add to Collection
Article Agriculture, Multidisciplinary

RIC-Net: A plant disease classification model based on the fusion of Inception and residual structure and embedded attention mechanism

Yun Zhao et al.

Summary: In this paper, a convolutional neural network model based on Inception and residual structure with an embedded modified convolutional block attention module (CBAM) was proposed to enhance classification accuracy of plant leaf diseases. The model achieved a high overall accuracy of 99.55% for the identification of corn, potato, and tomato diseases. Additionally, individual accuracy rates of 98.44%, 99.43%, and 95.20% were achieved for corn, potato, and tomato, respectively. The model demonstrated fewer parameters, shorter training time, and higher recognition accuracy compared to existing image classification models.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2022)

Add to Collection
Article Computer Science, Hardware & Architecture

AgriWealth: IoT based farming system

Deepika Sarpal et al.

Summary: The agricultural sector in India plays a significant role in the country's economy, but its potential for further growth is limited due to lack of infrastructure and resources. This paper proposes the use of a sensor-based irrigation model integrated with an Android application to monitor and control farms in real-time. The application also predicts suitable crops based on weather parameters and provides a classified portal for farmers and customers. The prototype has shown a significant reduction in water usage in fields and a high test accuracy for the selected Machine Learning algorithm.

MICROPROCESSORS AND MICROSYSTEMS (2022)

Add to Collection
Article Biochemical Research Methods

Plant Disease Detection Using Generated Leaves Based on DoubleGAN

Yafeng Zhao et al.

Summary: The study explores the use of DoubleGAN to generate images of unhealthy plant leaves in order to balance unbalanced datasets. The WGAN is used to generate a pretrained model and the SRGAN is used to generate high-resolution images. Compared to DCGAN, the images generated by DoubleGAN are clearer and achieve higher recognition accuracy.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2022)

Add to Collection
Article Computer Science, Artificial Intelligence

Modified ride-NN optimizer for the IoT based plant disease detection

Monalisa Mishra et al.

Summary: The Internet of Things (IoT) has shown great potential in automated plant disease detection in agriculture, with a developed model achieving an accuracy of 0.9156. This model utilizes pre-processing, segmentation, feature extraction, and a novel classifier for optimal weight selection in neural networks. Results indicate improved detection performance in various IoT environments.

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2021)

Add to Collection
Review Agronomy

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

Jinzhu Lu et al.

Summary: This review article discusses the latest CNN networks relevant to plant leaf disease classification, summarizes the DL principles involved in plant disease classification, presents the main problems and corresponding solutions of CNN used for plant disease classification, and discusses the future development direction in plant disease classification.

AGRICULTURE-BASEL (2021)

Add to Collection
Article Computer Science, Artificial Intelligence

Rice-net: an efficient artificial fish swarm optimization applied deep convolutional neural network model for identifying the Oryza sativa diseases

N. V. Raja Reddy Goluguri et al.

Summary: This research utilizes deep convolutional neural network combined with different optimization methods to identify rice diseases, with the proposed DCNN-LSTM (EAFSO) technique showing superior performance with an accuracy of 97.5%.

NEURAL COMPUTING & APPLICATIONS (2021)

Add to Collection
Article Agriculture, Multidisciplinary

A cucumber leaf disease severity classification method based on the fusion of DeepLabV3+and U-Net

Chunshan Wang et al.

Summary: The study proposed a two-stage model that combines DeepLabV3+ and U-Net for cucumber leaf disease severity classification in complex backgrounds. Experimental results showed that the model was able to segment leaves and disease spots step-by-step to complete the disease severity classification with high accuracy.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Add to Collection
Article Agriculture, Multidisciplinary

IoT-Agro: A smart farming system to Colombian coffee farms

Jhonn Pablo Rodriguez et al.

Summary: The adoption of smart technologies in agriculture creates a competitive edge for farmers and related industries, but selecting the most appropriate technology is not easy. This paper introduces a Smart Farming System based on a three-layered architecture, providing more accurate data analysis and management for agricultural production through the evaluation of different technologies and algorithms.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Add to Collection
Article Agriculture, Multidisciplinary

Towards automatic field plant disease recognition

Penghui Gui et al.

Summary: This paper proposes an improved CNN model for field plant disease recognition, which enhances the recognition accuracy through data augmentation and feature optimization.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Add to Collection
Article Agriculture, Multidisciplinary

T-CNN: Trilinear convolutional neural networks model for visual detection of plant diseases

Dongfang Wang et al.

Summary: This paper discusses the importance of detecting plant diseases and the application of convolutional neural networks in crop and disease recognition, proposing a new method that separates crop and disease identification and demonstrating its effectiveness. The results show high accuracy in crop and disease identification in controlled laboratory environments, and decent accuracy in real-world environments as well.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Add to Collection
Article Agriculture, Multidisciplinary

MEAN-SSD: A novel real-time detector for apple leaf diseases using improved light-weight convolutional neural networks

Henan Sun et al.

Summary: This study proposes a lightweight CNN model for real-time detection of apple leaf diseases on mobile devices. By constructing a dataset and introducing new modules, efficient and accurate detection of 5 common apple leaf diseases on mobile devices has been successfully achieved.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Add to Collection
Article Agriculture, Multidisciplinary

L-CSMS: novel lightweight network for plant disease severity recognition

Shao Xiang et al.

Summary: A lightweight CNN-based network with channel shuffle operation and multiple-size module is proposed for plant disease severity recognition, achieving competitive performance over previous works. The incorporation of channel shuffle operation and multiple-size convolution modules in the building block as a stacked topology is a key innovation in this study, demonstrating the effectiveness of the proposed method for plant disease diagnosis through extensive experiments.

JOURNAL OF PLANT DISEASES AND PROTECTION (2021)

Add to Collection
Article Computer Science, Artificial Intelligence

A cognitive vision method for the detection of plant disease images

Junde Chen et al.

Summary: The study proposes a novel approach to identify plant diseases through enhancing artificial neural networks and establishing a CNN-based model. The approach demonstrates impressive performance in experimental analyses, achieving high average accuracy and recall rates.

MACHINE VISION AND APPLICATIONS (2021)

Add to Collection
Article Computer Science, Artificial Intelligence

Recognition and counting of wheat mites in wheat fields by a three-step deep learning method

Peng Chen et al.

Summary: This paper proposes a three-step deep learning method for identifying and counting wheat mites from digital images. It involves separating large images into smaller datasets, extracting features using CNN, and utilizing Region Proposal Network and fully connected layers to output the position and population information of wheat mites. Different deep learning networks like ZFnet and VGG16 achieved high accuracies in this study.

NEUROCOMPUTING (2021)

Add to Collection
Article Agriculture, Multidisciplinary

Disease detection in tomato leaves via CNN with lightweight architectures implemented in Raspberry Pi 4

Victor Gonzalez-Huitron et al.

Summary: This study focuses on applying deep learning techniques to disease classification in tomato leaves using low-cost and low-power devices, training and evaluating convolutional neural network models suitable for such devices. Quantitative and qualitative evaluation and analysis are conducted through quality metrics and saliency maps, culminating in an implementation on Raspberry Pi 4 with a graphical user interface.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Add to Collection
Review Agriculture, Multidisciplinary

Technological support for detection and prediction of plant diseases: A systematic mapping study

Vinicius Bischoff et al.

Summary: The field of plant disease diagnosis and epidemiology focuses on assessing symptoms caused by pathogens, with many studies utilizing machine learning methods. A promising area of research involves the combination of computer vision and neural networks for disease detection in plants. This systematic review provides insights for future studies in the field.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Add to Collection
Article Agriculture, Multidisciplinary

Disease and pest infection detection in coconut tree through deep learning techniques

Piyush Singh et al.

Summary: This study focuses on developing an end-to-end framework using image processing and deep learning technology to detect diseases and pest infections in coconut trees, achieving high accuracy in classification.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Add to Collection
Article Agriculture, Multidisciplinary

Deep learning for the differentiation of downy mildew and spider mite in grapevine under field conditions

Salvador Gutierrez et al.

Summary: This study successfully applied deep learning and computer vision techniques to detect and differentiate downy mildew and spider mite symptoms on grapevine leaves, achieving an accuracy of up to 0.94. The results demonstrate the potential of these non-invasive techniques in the classification of pests and diseases in commercial crop production.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Add to Collection
Article Ecology

Plant leaf disease classification using EfficientNet deep learning model

Umit Atila et al.

Summary: The study introduces the use of EfficientNet deep learning architecture for classifying plant leaf diseases and compares its performance with other state-of-the-art deep learning models. Trained using the PlantVillage dataset, the results demonstrate that the B5 and B4 models of EfficientNet architecture outperformed other deep learning models in terms of accuracy and precision.

ECOLOGICAL INFORMATICS (2021)

Add to Collection
Article Ecology

VirLeafNet: Automatic analysis and viral disease diagnosis using deep-learning in Vigna mungo plant

Rakesh Chandra Joshi et al.

Summary: Various viral diseases adversely affect plant growth, leading to the need for automatic detection methods to monitor viral infections in crops. The proposed deep-learning-based method in this study is able to automatically detect viral infections in Vigna mungo, providing real-time classification on different categories of leaf images.

ECOLOGICAL INFORMATICS (2021)

Add to Collection
Article Ecology

Identification of disease using deep learning and evaluation of bacteriosis in peach leaf

Saumya Yadav et al.

Summary: By utilizing deep learning and imaging methods, a model has been developed to detect bacterial spot disease in peach crops, achieving a high accuracy rate of 98.75% in identifying healthy and infected peach leaves in just 0.185 seconds per image. This model could serve as a diagnostic or early warning tool in real farming conditions.

ECOLOGICAL INFORMATICS (2021)

Add to Collection
Article Computer Science, Artificial Intelligence

Self-supervised cross-iterative clustering for unlabeled plant disease images

Uno Fang et al.

Summary: This paper proposes a self-supervised clustering framework for grouping plant disease images based on the vulnerability of Kernel K-means, which shows high superiority over other methods in experiments.

NEUROCOMPUTING (2021)

Add to Collection
Article Optics

Detection of oil palm leaf disease based on color histogram and supervised classifier

Hamdani Hamdani et al.

Summary: A new method for detecting oil palm leaf disease is proposed in this study, which utilizes various color spaces and feature extraction methods, combined with principal component analysis and artificial neural network classifier. The method achieved high sensitivity, specificity, and accuracy on a local dataset, distinguishing between healthy and infected leaf classes.

OPTIK (2021)

Add to Collection
Article Computer Science, Hardware & Architecture

Performance of deep learning vs machine learning in plant leaf disease detection

R. Sujatha et al.

Summary: Plants are recognized as essential for humanity's energy production, but plant diseases can cause significant damage to crop production. Identification of leaf disease in farming requires labor and knowledge. In terms of disease detection, deep learning methods outperform machine learning methods.

MICROPROCESSORS AND MICROSYSTEMS (2021)

Add to Collection
Article Agriculture, Multidisciplinary

A deep learning approach for RGB image-based powdery mildew disease detection on strawberry leaves

Jaemyung Shin et al.

Summary: This study utilized Deep Learning models to detect powdery mildew in strawberries, optimizing well-established learners and performing data augmentation to prevent overfitting. The six DL algorithms used showed an average classification accuracy of over 92%, with ResNet-50 performing the best in classification.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Add to Collection
Article Agriculture, Multidisciplinary

A vegetable disease recognition model for complex background based on region proposal and progressive learning

Ji Zhou et al.

Summary: This study introduces a vegetable disease recognition model that can locate key regions in disease images under complex backgrounds and improve recognition accuracy through progressive learning network.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Add to Collection
Article Agriculture, Multidisciplinary

Development of a high-throughput plant disease symptom severity assessment tool using machine learning image analysis and integrated geolocation

James W. Clohessy et al.

Summary: A high-throughput assessment tool was developed for quantifying disease severity in peanut fields using RTK-GPS, cameras, and machine learning software. The tool accurately classified and quantified disease symptoms, demonstrating successful application under field conditions.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Add to Collection
Article Computer Science, Artificial Intelligence

Identification of rice plant diseases using lightweight attention networks

Junde Chen et al.

Summary: Rice is a crucial crop, but diseases can harm yields and food security. This study utilizes deep learning and attention mechanisms to optimize model training, achieving efficient rice disease identification with high accuracy.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

Add to Collection
Article Computer Science, Artificial Intelligence

Identifying crop diseases using attention embedded MobileNet-V2 model

Junde Chen et al.

Summary: Crop diseases are a major issue globally, leading to decreased crop production. Image-based automatic identification methods have gained attention for addressing this problem. This study introduces a Location-wise Soft Attention mechanism in MobileNet-V2, showing promising results in crop disease recognition through experimental analyses.

APPLIED SOFT COMPUTING (2021)

Add to Collection
Article Computer Science, Information Systems

DBA_SSD: A Novel End-to-End Object Detection Algorithm Applied to Plant Disease Detection

Jun Wang et al.

Summary: This study proposes a novel plant leaf disease detection method and conducts experiments on the PlantVillage dataset using improved algorithms, showing that DBA_SSD outperforms other algorithms.

INFORMATION (2021)

Add to Collection
Article Agricultural Engineering

Disease classification in aubergine with local symptomatic region using deep learning models

Aravind Krishnaswamy Rangarajan et al.

Summary: Recent trends in the application of deep learning techniques for crop disease classification have gained international attention. This study focused on classifying five diseases in Solanum melongena using the VGG16 model, achieving an accuracy of 91.3% on a smartphone deployment. The analysis of feature parameters and reasons for misclassification were also discussed.

BIOSYSTEMS ENGINEERING (2021)

Add to Collection
Review Agriculture, Multidisciplinary

Plant diseases recognition on images using convolutional neural networks: A systematic review

Andre Abade et al.

Summary: The study provides a systematic review of 121 papers over the past decade, examining the use of convolutional neural networks in plant disease identification and classification, identifying innovative trends and research gaps that need attention.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Add to Collection
Article Agriculture, Multidisciplinary

Tomato plant disease detection using transfer learning with C-GAN synthetic images

Amreen Abbas et al.

Summary: The paper introduces a deep learning-based method utilizing transfer learning and synthetic image generation, achieving high accuracy in classifying tomato leaf images. This approach demonstrates superior effectiveness and precision compared to existing methods.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Add to Collection
Article Agriculture, Multidisciplinary

LASSR: Effective super-resolution method for plant disease diagnosis

Quan Huu Cap et al.

Summary: The study proposes a high-quality image generation method for leaf disease diagnosis, which significantly improves diagnostic performance; experimental results show that the performance on an unseen test dataset is boosted by over 21% compared to the baseline model, and over 2% better than a model trained with images generated by ESRGAN.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

Add to Collection
Article Computer Science, Artificial Intelligence

Rice diseases detection and classification using attention based neural network and bayesian optimization

Yibin Wang et al.

Summary: In this research, an attention-based depthwise separable neural network with Bayesian optimization (ADSNNBO) is proposed for detecting and classifying rice disease from rice leaf images. The experimental results show that the model can quickly and accurately detect rice diseases, with a test accuracy of 94.65%. Additionally, the proposed attention mechanism effectively guides the model to learn informative features.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

Add to Collection
Article Chemistry, Analytical

IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet

Nidhi Kundu et al.

Summary: The decrease in crop yield and degradation in product quality due to plant diseases like rust and blast in pearl millet is a concern for farmers and the agriculture industry. Automating plant disease detection and classification using deep learning and IoT-based solutions is proposed to address this issue. The research focuses on developing a low-cost 'Automatic and Intelligent Data Collector and Classifier' framework integrating IoT and deep learning to improve crop yield and product quality.

SENSORS (2021)

Add to Collection
Article Computer Science, Artificial Intelligence

Plant disease detection based on a deep model

Rasim Alguliyev et al.

Summary: This paper proposes a deep learning based model for identifying and diagnosing plant leaf diseases, which provides a high-accuracy solution. Experimental results demonstrate that the proposed model excels in diagnosing plant diseases, and the addition of gated recurrent unit neural network further improves accuracy.

SOFT COMPUTING (2021)

Add to Collection
Article Telecommunications

Automated Plant Leaf Disease Detection and Classification Using Fuzzy Based Function Network

Siddharth Singh Chouhan et al.

Summary: In recent years, the applications of computer vision concepts and information communication technology in various fields have been discussed, focusing on automated disease detection in smart agriculture using the IoT_FBFN network.

WIRELESS PERSONAL COMMUNICATIONS (2021)

Add to Collection
Proceedings Paper Computer Science, Artificial Intelligence

Tip-burn stress detection of lettuce canopy grown in Plant Factories

Riccardo Gozzovelli et al.

Summary: In recent years, efforts have been made to tackle various plant stresses using sensors and deep learning methods. A new method for stress detection in dense plant canopies under artificial lights has been proposed, showing good accuracy and simple data acquisition methods.

2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021) (2021)

Add to Collection
Proceedings Paper Computer Science, Artificial Intelligence

Automatic Quantification of Plant Disease from Field Image Data Using Deep Learning

Kanish Garg et al.

Summary: Plant disease significantly affects yield, leading breeders to select disease-resistant plant cultivars. Utilizing UAV-acquired images for disease quantification is gaining popularity. This study proposes a deep framework for leaf segmentation and disease area detection to improve breeding efficiency.

2021 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2021) (2021)

Add to Collection
Article Agriculture, Multidisciplinary

Recognition of early blight and late blight diseases on potato leaves based on graph cut segmentation

Chaojun Hou et al.

Summary: This study proposed an accurate method for recognizing diseases on potato leaves, utilizing an automatic scheme for image segmentation and feature extraction, evaluating performance with various classifiers, and achieving successful recognition of potato diseases and infection degrees.

JOURNAL OF AGRICULTURE AND FOOD RESEARCH (2021)

Add to Collection
Article Computer Science, Information Systems

Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network

Changjian Zhou et al.

Summary: The restructured residual dense network model proposed in this paper can efficiently identify tomato leaf diseases with high accuracy and performance, providing an effective method for crop disease recognition.

IEEE ACCESS (2021)

Add to Collection
Article Computer Science, Information Systems

A Generic Approach for Wheat Disease Classification and Verification Using Expert Opinion for Knowledge-Based Decisions

Waleej Haider et al.

Summary: Crop diseases have a significant impact on crop production due to the lack of modern disease identification methods. Utilizing local agricultural knowledge can enhance disease management efficiency.

IEEE ACCESS (2021)

Add to Collection
Article Computer Science, Artificial Intelligence

Plant Disease Recognition: A Large-Scale Benchmark Dataset and a Visual Region and Loss Reweighting Approach

Xinda Liu et al.

Summary: This paper systematically investigates visual plant disease recognition for diagnosis, constructing a large-scale plant disease dataset and emphasizing diseased parts by reweighting both visual regions and loss. The proposed method effectively addresses the challenges in plant disease recognition and demonstrates advantages through extensive evaluations.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2021)

Add to Collection
Article Computer Science, Information Systems

SPEDCCNN: Spatial Pyramid-Oriented Encoder-Decoder Cascade Convolution Neural Network for Crop Disease Leaf Segmentation

Yuxia Yuan et al.

Summary: This paper proposes a spatial pyramid-oriented encoder-decoder cascade convolution neural network for crop disease leaf segmentation. Experimental results show that the proposed method has higher segmentation accuracy, with average values of various indexes exceeding 90%, and can meticulously reflect the external morphological features of the crop disease leaf and relatively better retain the color and texture information.

IEEE ACCESS (2021)

Add to Collection
Article Computer Science, Information Systems

Heterogeneous Internet of things organization Predictive Analysis Platform for Apple Leaf Diseases Recognition

Sanjeevi Pandiyan et al.

COMPUTER COMMUNICATIONS (2020)

Add to Collection
Article Agriculture, Multidisciplinary

Research on deep learning in apple leaf disease recognition

Yong Zhong et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Agriculture, Multidisciplinary

Unsupervised image translation using adversarial networks for improved plant disease recognition

Haseeb Nazki et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Agriculture, Multidisciplinary

New perspectives on plant disease characterization based on deep learning

Sue Han Lee et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Agriculture, Multidisciplinary

Crop leaf disease recognition based on Self-Attention convolutional neural network

Weihui Zeng et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Agriculture, Multidisciplinary

SoyNet: Soybean leaf diseases classification

Aditya Karlekar et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Computer Science, Artificial Intelligence

An optimized model based on convolutional neural networks and orthogonal learning particle swarm optimization algorithm for plant diseases diagnosis

Ashraf Darwish et al.

SWARM AND EVOLUTIONARY COMPUTATION (2020)

Add to Collection
Article Chemistry, Analytical

A Framework for Agricultural Pest and Disease Monitoring Based on Internet-of-Things and Unmanned Aerial Vehicles

Demin Gao et al.

SENSORS (2020)

Add to Collection
Article Agriculture, Multidisciplinary

An optimized dense convolutional neural network model for disease recognition and classification in corn leaf

Abdul Waheed et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Agriculture, Multidisciplinary

Few-Shot Learning approach for plant disease classification using images taken in the field

David Argueso et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Agriculture, Multidisciplinary

Deep feature based rice leaf disease identification using support vector machine

Prabira Kumar Sethy et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Agriculture, Multidisciplinary

Using deep transfer learning for image-based plant disease identification

Junde Chen et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Agriculture, Multidisciplinary

Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach

Mohamed Kerkech et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Computer Science, Artificial Intelligence

Uncertainty quantification for plant disease detection using Bayesian deep learning

S. Hernandez et al.

APPLIED SOFT COMPUTING (2020)

Add to Collection
Article Agricultural Engineering

Identification and recognition of rice diseases and pests using convolutional neural networks

Chowdhury R. Rahman et al.

BIOSYSTEMS ENGINEERING (2020)

Add to Collection
Article Agricultural Engineering

Recognition of diseased Pinus trees in UAV images using deep learning and AdaBoost classifier

Gensheng Hu et al.

BIOSYSTEMS ENGINEERING (2020)

Add to Collection
Article Agriculture, Multidisciplinary

Automatic vegetable disease identification approach using individual lesion features

Aliyu Muhammad Abdu et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Agriculture, Multidisciplinary

EfficientNet-B4-Ranger: A novel method for greenhouse cucumber disease recognition under natural complex environment

Pan Zhang et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Agriculture, Multidisciplinary

Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease

Utpal Barman et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Agriculture, Multidisciplinary

Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset

Yonghua Xiong et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Biochemical Research Methods

Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model

Jun Liu et al.

PLANT METHODS (2020)

Add to Collection
Editorial Material Multidisciplinary Sciences

COVID-19 risks to global food security

David Laborde et al.

SCIENCE (2020)

Add to Collection
Review Plant Sciences

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Reem Ibrahim Hasan et al.

PLANTS-BASEL (2020)

Add to Collection
Article Agriculture, Multidisciplinary

Image recognition of four rice leaf diseases based on deep learning and support vector machine

Feng Jiang et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Agriculture, Multidisciplinary

Zero- and few-shot learning for diseases recognition of Citrus aurantium L. using conditional adversarial autoencoders

Fangming Zhong et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Agriculture, Multidisciplinary

Early detection of grapevine leafroll disease in a red-berried wine grape cultivar using hyperspectral imaging

Zongmei Gao et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Agriculture, Multidisciplinary

Do we really need deep CNN for plant diseases identification?

Yang Li et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Agriculture, Multidisciplinary

A solanaceae disease recognition model based on SE-Inception

Zhenbo Li et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Review Agriculture, Multidisciplinary

Automatic identification of diseases in grains crops through computational approaches: A review

R. Manavalan

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Agriculture, Multidisciplinary

Identi fi cation of tomato leaf diseases based on combination of ABCK-BWTR and B-ARNet

Xiao Chen et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Agriculture, Multidisciplinary

Grape disease image classification based on lightweight convolution neural networks and channelwise attention

Zhe Tang et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2020)

Add to Collection
Article Computer Science, Information Systems

Self-Supervised Collaborative Multi-Network for Fine-Grained Visual Categorization of Tomato Diseases

Guofeng Yang et al.

IEEE ACCESS (2020)

Add to Collection
Article Computer Science, Information Systems

GANs-Based Data Augmentation for Citrus Disease Severity Detection Using Deep Learning

Qingmao Zeng et al.

IEEE ACCESS (2020)

Add to Collection
Review Agricultural Engineering

Deep Learning Application in Plant Stress Imaging: A Review

Zongmei Gao et al.

AGRIENGINEERING (2020)

Add to Collection
Article Computer Science, Information Systems

Detection of Peach Disease Image Based on Asymptotic Non-Local Means and PCNN-IPELM

Shuangjie Huang et al.

IEEE ACCESS (2020)

Add to Collection
Review Engineering, Multidisciplinary

Systematic review of deep learning techniques in plant disease detection

M. Nagaraju et al.

INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT (2020)

Add to Collection
Article Computer Science, Information Systems

Maize Leaf Disease Identification Based on Feature Enhancement and DMS-Robust Alexnet

Mingjie Lv et al.

IEEE ACCESS (2020)

Add to Collection
Article Environmental Sciences

Global food security in the context of COVID-19: A scenario-based exploratory analysis

Parmeshwar Udmale et al.

PROGRESS IN DISASTER SCIENCE (2020)

Add to Collection
Article Computer Science, Information Systems

Northern Maize Leaf Blight Detection Under Complex Field Environment Based on Deep Learning

Jun Sun et al.

IEEE ACCESS (2020)

Add to Collection
Article Computer Science, Information Systems

A Data Augmentation Method Based on Generative Adversarial Networks for Grape Leaf Disease Identification

Bin Liu et al.

IEEE ACCESS (2020)

Add to Collection
Article Agriculture, Multidisciplinary

A comparative study of fine-tuning deep learning models for plant disease identification

Edna Chebet Too et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2019)

Add to Collection
Article Agriculture, Multidisciplinary

PD2SE-Net: Computer-assisted plant disease diagnosis and severity estimation network

Qiaokang Liang et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2019)

Add to Collection
Article Agriculture, Multidisciplinary

SLIC_SVM based leaf diseases saliency map extraction of tea plant

Yunyun Sun et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2019)

Add to Collection
Article Ecology

The global burden of pathogens and pests on major food crops

Serge Savary et al.

NATURE ECOLOGY & EVOLUTION (2019)

Add to Collection
Article Agricultural Engineering

Plant disease identification from individual lesions and spots using deep learning

Jayme Garcia Arnal Barbedo

BIOSYSTEMS ENGINEERING (2019)

Add to Collection
Article Computer Science, Hardware & Architecture

Identification of plant leaf diseases using a nine-layer deep convolutional neural network

G. Geetharamani et al.

COMPUTERS & ELECTRICAL ENGINEERING (2019)

Add to Collection
Article Agriculture, Multidisciplinary

Cucumber leaf disease identification with global pooling dilated convolutional neural network

Shanwen Zhang et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2019)

Add to Collection
Article Computer Science, Interdisciplinary Applications

Deep neural networks with transfer learning in millet crop images

Solemane Coulibaly et al.

COMPUTERS IN INDUSTRY (2019)

Add to Collection
Article Agriculture, Multidisciplinary

A low shot learning method for tea leaf's disease identification

Hu Gensheng et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2019)

Add to Collection
Article Agriculture, Multidisciplinary

Novel data augmentation strategies to boost supervised segmentation of plant disease

Clement Douarre et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2019)

Add to Collection
Article Agriculture, Multidisciplinary

Automated leaf disease detection in different crop species through image features analysis and One Class Classifiers

X. E. Pantazi et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2019)

Add to Collection
Article Agriculture, Multidisciplinary

A new proposal for automatic identification of multiple soybean diseases

Juliana Mariana Macedo Araujo et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2019)

Add to Collection
Article Agriculture, Multidisciplinary

Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions

Artzai Picon et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2019)

Add to Collection
Article Computer Science, Artificial Intelligence

Plant disease recognition using fractional-order Zernike moments and SVM classifier

Parminder Kaur et al.

NEURAL COMPUTING & APPLICATIONS (2019)

Add to Collection
Review Plant Sciences

Non-destructive techniques of detecting plant diseases: A review

Maimunah Mohd Ali et al.

PHYSIOLOGICAL AND MOLECULAR PLANT PATHOLOGY (2019)

Add to Collection
Article Computer Science, Information Systems

Rapid Detection of Rice Disease Based on FCM-KM and Faster R-CNN Fusion

Guoxiong Zhou et al.

IEEE ACCESS (2019)

Add to Collection
Article Computer Science, Information Systems

Strawberry Verticillium Wilt Detection Network Based on Multi-Task Learning and Attention

Xuan Nie et al.

IEEE ACCESS (2019)

Add to Collection
Review Computer Science, Information Systems

A Review of Data Analysis for Early-Childhood Period: Taxonomy, Motivations, Challenges, Recommendation, and Methodological Aspects

A. H. Alamoodi et al.

IEEE ACCESS (2019)

Add to Collection
Article Computer Science, Information Systems

An Optimized Method for Segmentation and Classification of Apple Diseases Based on Strong Correlation and Genetic Algorithm Based Feature Selection

Muhammad Attique Khan et al.

IEEE ACCESS (2019)

Add to Collection
Article Computer Science, Information Systems

Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks

Peng Jiang et al.

IEEE ACCESS (2019)

Add to Collection
Article Agriculture, Multidisciplinary

Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection

Muhammad Sharif et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)

Add to Collection
Article Agriculture, Multidisciplinary

Deep learning models for plant disease detection and diagnosis

Konstantinos P. Ferentinos

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)

Add to Collection
Article Engineering, Electrical & Electronic

Feature-Ensemble-Based Novelty Detection for Analyzing Plant Hyperspectral Datasets

Ali AlSuwaidi et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2018)

Add to Collection
Article Computer Science, Information Systems

Bacterial Foraging Optimization Based Radial Basis Function Neural Network (BRBFNN) for Identification and Classification of Plant Leaf Diseases: An Automatic Approach Towards Plant Pathology

Siddharth Singh Chouhan et al.

IEEE ACCESS (2018)

Add to Collection
Article Agriculture, Multidisciplinary

A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network

Juncheng Ma et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)

Add to Collection
Article Agriculture, Multidisciplinary

Using Support Vector Machines classification to differentiate spectral signatures of potato plants infected with Potato Virus Y

L. M. Griffel et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)

Add to Collection
Review Agriculture, Multidisciplinary

An automated detection and classification of citrus plant diseases using image processing techniques: A review

Zahid Iqbal et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)

Add to Collection
Article Agriculture, Multidisciplinary

Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification

Jayme Garcia Arnal Barbedo

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)

Add to Collection
Review Environmental Sciences

Hazardous effects of chemical pesticides on human health-Cancer and other associated disorders

Akash Sabarwal et al.

ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY (2018)

Add to Collection
Article Computer Science, Information Systems

Annotated Plant Pathology Databases for Image-Based Detection and Recognition of Diseases

Jayme Garcia Arnal Barbedo et al.

IEEE Latin America Transactions (2018)

Add to Collection
Review Agriculture, Multidisciplinary

CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features

Muhammad Attique Khan et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)

Add to Collection
Review Agriculture, Multidisciplinary

Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado

Jaafar Abdulridha et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2018)

Add to Collection
Article Engineering, Electrical & Electronic

Mapping Mosaic Virus in Sugarcane Based on Hyperspectral Images

Erika Akemi Saito Moriya et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2017)

Add to Collection
Article Computer Science, Artificial Intelligence

Identification of rice diseases using deep convolutional neural networks

Yang Lu et al.

NEUROCOMPUTING (2017)

Add to Collection
Review Environmental Sciences

Exposure to pesticides and the associated human health effects

Ki-Hyun Kim et al.

SCIENCE OF THE TOTAL ENVIRONMENT (2017)

Add to Collection
Article Agriculture, Multidisciplinary

Gradation of yellow mosaic virus disease of okra and bitter gourd based on entropy based binning and Naive Bayes classifier after identification of leaves

Dhiman Mondal et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2017)

Add to Collection
Article Agriculture, Multidisciplinary

A segmentation method for greenhouse vegetable foliar disease spots images using color information and region growing

Juncheng Ma et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2017)

Add to Collection
Article Agriculture, Multidisciplinary

Particle Swarm Optimization based incremental classifier design for rice disease prediction

Shampa Sengupta et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2017)

Add to Collection
Article Agriculture, Multidisciplinary

Fusion of superpixel, expectation maximization and PHOG for recognizing cucumber diseases

Shanwen Zhang et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2017)

Add to Collection
Article Agriculture, Multidisciplinary

Symptom based automated detection of citrus diseases using color histogram and textural descriptors

H. Ali et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2017)

Add to Collection
Article Agriculture, Multidisciplinary

Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case

Alexander Johannes et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2017)

Add to Collection
Article Agriculture, Multidisciplinary

Leaf image based cucumber disease recognition using sparse representation classification

Shanwen Zhang et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2017)

Add to Collection
Article Agricultural Engineering

Identifying multiple plant diseases using digital image processing

Jayme Garcia Arnal Barbedo et al.

BIOSYSTEMS ENGINEERING (2016)

Add to Collection
Review Agricultural Engineering

A review on the main challenges in automatic plant disease identification based on visible range images

Jayme Garcia Arnal Barbedo

BIOSYSTEMS ENGINEERING (2016)

Add to Collection
Article Agriculture, Multidisciplinary

Local descriptors for soybean disease recognition

Rillian Diello Lucas Pires et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2016)

Add to Collection
Article Computer Science, Artificial Intelligence

Cucumber disease recognition based on Global-Local Singular value decomposition

Shanwen Zhang et al.

NEUROCOMPUTING (2016)

Add to Collection
Article Plant Sciences

Using Deep Learning for Image-Based Plant Disease Detection

Sharada P. Mohanty et al.

FRONTIERS IN PLANT SCIENCE (2016)

Add to Collection
Article Agriculture, Multidisciplinary

Disease detection of Cercospora Leaf Spot in sugar beet by robust template matching

Rong Zhou et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2014)

Add to Collection
Article Agriculture, Multidisciplinary

Mid-infrared spectroscopy for early detection of basal stem rot disease in oil palm

Shohreh Liaghat et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2014)

Add to Collection
Article Agriculture, Multidisciplinary

Rice diseases classification using feature selection and rule generation techniques

Santanu Phadikar et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2013)

Add to Collection
Article Agriculture, Multidisciplinary

Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance

T. Rumpf et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2010)

Add to Collection
Review Agriculture, Multidisciplinary

A review of advanced techniques for detecting plant diseases

Sindhuja Sankaran et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2010)

Add to Collection
Article Agricultural Engineering

An image-processing based algorithm to automatically identify plant disease visual symptoms

A. Camargo et al.

BIOSYSTEMS ENGINEERING (2009)

Add to Collection
Article Agriculture, Multidisciplinary

Image pattern classification for the identification of disease causing agents in plants

A. Camargo et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2009)

Add to Collection
Article Agriculture, Multidisciplinary

Identification of citrus disease using color texture features and discriminant analysis

R. Pydipati et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2006)

Add to Collection
Review Agriculture, Multidisciplinary

Crop losses to pests

E. -C. Oerke

JOURNAL OF AGRICULTURAL SCIENCE (2006)

Add to Collection
Article Agriculture, Multidisciplinary

Life cycle impact assessment of pesticides on human health and ecosystems

M Margni et al.

AGRICULTURE ECOSYSTEMS & ENVIRONMENT (2002)

Add to Collection
Webinars Posters Questions Hubs Funding Journals Papers Connect Collections Reviewers About Us FAQs Mobile App Privacy Policy Terms of Use
© Peeref 2019-2024. All rights reserved.