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

Abstract Fast and accurate plant disease detection is critical to increasing agricultural productivity in a sustainable way. Traditionally, human experts have been relied upon to diagnose anomalies in plants caused by diseases, pests, nutritional deficiencies or extreme weather. However, this is expensive, time consuming and in some cases impractical. To counter these challenges, research into the use of image processing techniques for plant disease recognition has become a hot research topic. In this paper, we provide a comprehensive review of recent studies carried out in the area of crop pest and disease recognition using image processing and machine learning techniques. We hope that this work will be a valuable resource for researchers in this area of crop pest and disease recognition using image processing techniques. In particular, we concentrate on the use of RGB images owing to the low cost and high availability of digital RGB cameras. We report that recent efforts have focused on the use of deep learning instead of training shallow classifiers using hand-crafted features. Researchers have reported high recognition accuracies on particular datasets but in many cases, the performance of those systems deteriorated significantly when tested on different datasets or in field conditions. Nevertheless, progress made so far has been encouraging. Experimental results showing the leaf disease recognition performance of ten CNN architectures in terms of recognition accuracy, recall, precision, specificity, F1-score, training duration and storage requirements are also presented. Subsequently, recommendations are made on the most suitable architectures to deploy in conventional as well as mobile/embedded computing environments. We also discuss some of the unresolved challenges that need to be addressed in order to develop practical automatic plant disease recognition systems for use in field conditions.

[1]  Ashwin Dhakal,et al.  Image-Based Plant Disease Detection with Deep Learning , 2018, International Journal of Computer Trends and Technology.

[2]  Yi Li,et al.  R-FCN: Object Detection via Region-based Fully Convolutional Networks , 2016, NIPS.

[3]  V. Singh,et al.  Sunflower leaf diseases detection using image segmentation based on particle swarm optimization , 2019, Artificial Intelligence in Agriculture.

[4]  Fuji Ren,et al.  Feature Reuse Residual Networks for Insect Pest Recognition , 2019, IEEE Access.

[5]  Erich-Christian Oerke,et al.  Hyperspectral phenotyping of the reaction of grapevine genotypes to Plasmopara viticola. , 2016, Journal of experimental botany.

[6]  Baskar Ganapathysubramanian,et al.  An explainable deep machine vision framework for plant stress phenotyping , 2018, Proceedings of the National Academy of Sciences.

[7]  Raja Purushothaman,et al.  Tomato crop disease classification using pre-trained deep learning algorithm , 2018 .

[8]  Abdul Bais,et al.  Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network , 2020 .

[9]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[10]  Dong Sun Park,et al.  High-Performance Deep Neural Network-Based Tomato Plant Diseases and Pests Diagnosis System With Refinement Filter Bank , 2018, Front. Plant Sci..

[11]  Artzai Picón,et al.  Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild , 2019, Comput. Electron. Agric..

[12]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Changshui Zhang,et al.  An In-field Automatic Wheat Disease Diagnosis System , 2017, Comput. Electron. Agric..

[14]  Xanthoula Eirini Pantazi,et al.  Automated leaf disease detection in different crop species through image features analysis and One Class Classifiers , 2019, Comput. Electron. Agric..

[15]  Yun Zhang,et al.  Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks , 2017, Symmetry.

[16]  Li Yujian,et al.  A comparative study of fine-tuning deep learning models for plant disease identification , 2019, Comput. Electron. Agric..

[17]  H. Sabrol,et al.  Tomato plant disease classification in digital images using classification tree , 2016, 2016 International Conference on Communication and Signal Processing (ICCSP).

[18]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[19]  Mark Rea,et al.  A High-Throughput Phenotyping System Using Machine Vision to Quantify Severity of Grapevine Powdery Mildew , 2019, Plant phenomics.

[20]  Sang Cheol Kim,et al.  A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition , 2017, Sensors.

[21]  Manisha Sharma,et al.  Image Processing Based Leaf Rot Disease, Detection of Betel Vine (Piper BetleL.) , 2016 .

[22]  Pablo J. Zarco-Tejada,et al.  High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices , 2013 .

[23]  Pablo J. Zarco-Tejada,et al.  Early Detection and Quantification of Almond Red Leaf Blotch Using High-Resolution Hyperspectral and Thermal Imagery , 2016, Remote. Sens..

[24]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[25]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Ghulam Muhammad,et al.  Automatic Fruit Classification Using Deep Learning for Industrial Applications , 2019, IEEE Transactions on Industrial Informatics.

[27]  Uday Pratap Singh,et al.  Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease , 2019, IEEE Access.

[28]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[29]  Zhanhong Ma,et al.  Identification of Alfalfa Leaf Diseases Using Image Recognition Technology , 2016, PloS one.

[30]  Uday Pratap Singh,et al.  Bacterial Foraging Optimization Based Radial Basis Function Neural Network (BRBFNN) for Identification and Classification of Plant Leaf Diseases: An Automatic Approach Towards Plant Pathology , 2018, IEEE Access.

[31]  Aydin Kaya,et al.  Analysis of transfer learning for deep neural network based plant classification models , 2019, Comput. Electron. Agric..

[32]  Henry Medeiros,et al.  Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network , 2018, IEEE Robotics and Automation Letters.

[33]  Achim Walter,et al.  Hyperspectral Canopy Sensing of Wheat Septoria Tritici Blotch Disease , 2018, Front. Plant Sci..

[34]  Jayme Garcia Arnal Barbedo,et al.  Annotated Plant Pathology Databases for Image-Based Detection and Recognition of Diseases , 2018, IEEE Latin America Transactions.

[35]  Marcel Salathé,et al.  Using Deep Learning for Image-Based Plant Disease Detection , 2016, Front. Plant Sci..

[36]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[37]  D. Vydeki,et al.  Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm , 2020 .

[38]  Konstantinos P. Ferentinos,et al.  Deep learning models for plant disease detection and diagnosis , 2018, Comput. Electron. Agric..

[39]  Fumio Okura,et al.  How Convolutional Neural Networks Diagnose Plant Disease , 2019, Plant phenomics.

[40]  Jayme Garcia Arnal Barbedo,et al.  Factors influencing the use of deep learning for plant disease recognition , 2018, Biosystems Engineering.

[41]  J. Yosinski,et al.  Automated Identification of Northern Leaf Blight-Infected Maize Plants from Field Imagery Using Deep Learning. , 2017, Phytopathology.

[42]  Wenzhun Huang,et al.  Three-channel convolutional neural networks for vegetable leaf disease recognition , 2019, Cognitive Systems Research.

[43]  Yang Lu,et al.  Identification of rice diseases using deep convolutional neural networks , 2017, Neurocomputing.

[44]  Andrea Luvisi,et al.  X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion , 2017, Front. Plant Sci..

[45]  Daechul Park,et al.  A Multiclass Deep Convolutional Neural Network Classifier for Detection of Common Rice Plant Anomalies , 2018 .

[46]  Mohsen Azadbakht,et al.  Wheat leaf rust detection at canopy scale under different LAI levels using machine learning techniques , 2019, Comput. Electron. Agric..

[47]  Víctor Martínez-Martínez,et al.  Leaf and canopy reflectance spectrometry applied to the estimation of angular leaf spot disease severity of common bean crops , 2018, PloS one.

[48]  Artzai Picón,et al.  Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case , 2017, Comput. Electron. Agric..

[49]  Julian M. Alston,et al.  Reflections on Agricultural R&D, Productivity, and the Data Constraint: Unfinished Business, Unsettled Issues , 2018 .

[50]  Georg Bareth,et al.  Investigation of Leaf Diseases and Estimation of Chlorophyll Concentration in Seven Barley Varieties Using Fluorescence and Hyperspectral Indices , 2013, Remote. Sens..

[51]  Xi Cheng,et al.  Pest identification via deep residual learning in complex background , 2017, Comput. Electron. Agric..

[52]  Jayme Garcia Arnal Barbedo,et al.  A review on the main challenges in automatic plant disease identification based on visible range images , 2016 .

[53]  Hitoshi Iyatomi,et al.  Basic Study of Automated Diagnosis of Viral Plant Diseases Using Convolutional Neural Networks , 2015, ISVC.

[54]  A. K. Misra,et al.  Detection of plant leaf diseases using image segmentation and soft computing techniques , 2017 .

[55]  Jayme G. A. Barbedo,et al.  Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification , 2018, Comput. Electron. Agric..

[56]  Mingming Zhang,et al.  Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks , 2018, IEEE Access.

[57]  Victor Alchanatis,et al.  Detection and counting of flowers on apple trees for better chemical thinning decisions , 2019, Precision Agriculture.

[58]  Gensheng Hu,et al.  Identification of tea leaf diseases by using an improved deep convolutional neural network , 2019, Sustain. Comput. Informatics Syst..

[59]  Darko Stefanovic,et al.  Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification , 2016, Comput. Intell. Neurosci..

[60]  Lei Zhang,et al.  Detection of peanut leaf spots disease using canopy hyperspectral reflectance , 2019, Comput. Electron. Agric..

[61]  Tristan Perez,et al.  DeepFruits: A Fruit Detection System Using Deep Neural Networks , 2016, Sensors.

[62]  Kemal Adem,et al.  Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms , 2019, Physica A: Statistical Mechanics and its Applications.

[63]  Peng Jiang,et al.  Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks , 2019, IEEE Access.

[64]  Jayme Garcia Arnal Barbedo,et al.  Plant disease identification from individual lesions and spots using deep learning , 2019, Biosystems Engineering.

[65]  Abdelouahab Moussaoui,et al.  Deep Learning for Tomato Diseases: Classification and Symptoms Visualization , 2017, Appl. Artif. Intell..

[66]  Darko Stefanovic,et al.  Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection , 2019, Symmetry.

[67]  Kushtrim Bresilla,et al.  Single-Shot Convolution Neural Networks for Real-Time Fruit Detection Within the Tree , 2019, Front. Plant Sci..