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Saifullah S, Mercier D, Lucieri A, Dengel A, Ahmed S. The privacy-explainability trade-off: unraveling the impacts of differential privacy and federated learning on attribution methods. Front Artif Intell 2024; 7:1236947. [PMID: 39021435 PMCID: PMC11253022 DOI: 10.3389/frai.2024.1236947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 06/17/2024] [Indexed: 07/20/2024] Open
Abstract
Since the advent of deep learning (DL), the field has witnessed a continuous stream of innovations. However, the translation of these advancements into practical applications has not kept pace, particularly in safety-critical domains where artificial intelligence (AI) must meet stringent regulatory and ethical standards. This is underscored by the ongoing research in eXplainable AI (XAI) and privacy-preserving machine learning (PPML), which seek to address some limitations associated with these opaque and data-intensive models. Despite brisk research activity in both fields, little attention has been paid to their interaction. This work is the first to thoroughly investigate the effects of privacy-preserving techniques on explanations generated by common XAI methods for DL models. A detailed experimental analysis is conducted to quantify the impact of private training on the explanations provided by DL models, applied to six image datasets and five time series datasets across various domains. The analysis comprises three privacy techniques, nine XAI methods, and seven model architectures. The findings suggest non-negligible changes in explanations through the implementation of privacy measures. Apart from reporting individual effects of PPML on XAI, the paper gives clear recommendations for the choice of techniques in real applications. By unveiling the interdependencies of these pivotal technologies, this research marks an initial step toward resolving the challenges that hinder the deployment of AI in safety-critical settings.
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Affiliation(s)
- Saifullah Saifullah
- Department of Computer Science, RPTU Kaiserslautern-Landau, Kaiserslautern, Rhineland-Palatinate, Germany
- Smart Data and Knowledge Services (SDS), DFKI GmbH, Kaiserslautern, Rhineland-Palatinate, Germany
| | - Dominique Mercier
- Department of Computer Science, RPTU Kaiserslautern-Landau, Kaiserslautern, Rhineland-Palatinate, Germany
- Smart Data and Knowledge Services (SDS), DFKI GmbH, Kaiserslautern, Rhineland-Palatinate, Germany
| | - Adriano Lucieri
- Department of Computer Science, RPTU Kaiserslautern-Landau, Kaiserslautern, Rhineland-Palatinate, Germany
- Smart Data and Knowledge Services (SDS), DFKI GmbH, Kaiserslautern, Rhineland-Palatinate, Germany
| | - Andreas Dengel
- Department of Computer Science, RPTU Kaiserslautern-Landau, Kaiserslautern, Rhineland-Palatinate, Germany
- Smart Data and Knowledge Services (SDS), DFKI GmbH, Kaiserslautern, Rhineland-Palatinate, Germany
| | - Sheraz Ahmed
- Smart Data and Knowledge Services (SDS), DFKI GmbH, Kaiserslautern, Rhineland-Palatinate, Germany
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Feng G, Gu Y, Wang C, Zhou Y, Huang S, Luo B. Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective. PLANTS (BASEL, SWITZERLAND) 2024; 13:1722. [PMID: 38999562 PMCID: PMC11243561 DOI: 10.3390/plants13131722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 06/17/2024] [Accepted: 06/19/2024] [Indexed: 07/14/2024]
Abstract
Fusarium head blight (FHB) is a major threat to global wheat production. Recent reviews of wheat FHB focused on pathology or comprehensive prevention and lacked a summary of advanced detection techniques. Unlike traditional detection and management methods, wheat FHB detection based on various imaging technologies has the obvious advantages of a high degree of automation and efficiency. With the rapid development of computer vision and deep learning technology, the number of related research has grown explosively in recent years. This review begins with an overview of wheat FHB epidemic mechanisms and changes in the characteristics of infected wheat. On this basis, the imaging scales are divided into microscopic, medium, submacroscopic, and macroscopic scales. Then, we outline the recent relevant articles, algorithms, and methodologies about wheat FHB from disease detection to qualitative analysis and summarize the potential difficulties in the practicalization of the corresponding technology. This paper could provide researchers with more targeted technical support and breakthrough directions. Additionally, this paper provides an overview of the ideal application mode of the FHB detection technologies based on multi-scale imaging and then examines the development trend of the all-scale detection system, which paved the way for the fusion of non-destructive detection technologies of wheat FHB based on multi-scale imaging.
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Affiliation(s)
- Guoqing Feng
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China; (G.F.); (Y.G.); (C.W.); (Y.Z.); (S.H.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- College of Agricultural Engineering, Jiangsu University, Zhenjiang 212000, China
| | - Ying Gu
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China; (G.F.); (Y.G.); (C.W.); (Y.Z.); (S.H.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Cheng Wang
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China; (G.F.); (Y.G.); (C.W.); (Y.Z.); (S.H.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- College of Agricultural Engineering, Jiangsu University, Zhenjiang 212000, China
| | - Yanan Zhou
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China; (G.F.); (Y.G.); (C.W.); (Y.Z.); (S.H.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Shuo Huang
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China; (G.F.); (Y.G.); (C.W.); (Y.Z.); (S.H.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Bin Luo
- Research Center of Intelligent Equipment, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100089, China; (G.F.); (Y.G.); (C.W.); (Y.Z.); (S.H.)
- National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- College of Agricultural Engineering, Jiangsu University, Zhenjiang 212000, China
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Isinkaye FO, Olusanya MO, Singh PK. Deep learning and content-based filtering techniques for improving plant disease identification and treatment recommendations: A comprehensive review. Heliyon 2024; 10:e29583. [PMID: 38737274 PMCID: PMC11088271 DOI: 10.1016/j.heliyon.2024.e29583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 03/30/2024] [Accepted: 04/10/2024] [Indexed: 05/14/2024] Open
Abstract
The importance of identifying plant diseases has risen recently due to the adverse effect they have on agricultutal production. Plant diseases have been a big concern in agriculture, as they affect crop production, and constitute a major threat to global food security. In the domain of modern agriculture, effective plant disease management is vital to ensure healthy crop yields and sustainable practices. Traditional means of identifying plant disease are faced with lots of challenges and the need for better and efficient detection methods cannot be overemphazised. The emergence of advanced technologies, particularly deep learning and content-based filtering techniques, if integrated together can changed the way plant diseases are identified and treated. Such as speedy and correct identification of plant diseases and efficient treatment recommendations which are keys for sustainable food production. In this work, We try to investigate the current state of research, identified gaps and limitations in knowledge, and suggests future directions for researchers, experts and farmers that could help to provide better ways of mitigating plant disease problems.
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Affiliation(s)
- Folasade Olubusola Isinkaye
- Department of Computer Science and Information Technology, Sol Plaatje University Kimberley, 8301, South Africa
| | - Michael Olusoji Olusanya
- Department of Computer Science and Information Technology, Sol Plaatje University Kimberley, 8301, South Africa
| | - Pramod Kumar Singh
- Department of Computer Science and Engineering, ABV-Indian Institute of Information Technology and Management Gwalior, Gwalior, 474015, MP, India
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Lu Y, Lu X, Zheng L, Sun M, Chen S, Chen B, Wang T, Yang J, Lv C. Application of Multimodal Transformer Model in Intelligent Agricultural Disease Detection and Question-Answering Systems. PLANTS (BASEL, SWITZERLAND) 2024; 13:972. [PMID: 38611501 PMCID: PMC11013167 DOI: 10.3390/plants13070972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/22/2024] [Accepted: 03/24/2024] [Indexed: 04/14/2024]
Abstract
In this study, an innovative approach based on multimodal data and the transformer model was proposed to address challenges in agricultural disease detection and question-answering systems. This method effectively integrates image, text, and sensor data, utilizing deep learning technologies to profoundly analyze and process complex agriculture-related issues. The study achieved technical breakthroughs and provides new perspectives and tools for the development of intelligent agriculture. In the task of agricultural disease detection, the proposed method demonstrated outstanding performance, achieving a precision, recall, and accuracy of 0.95, 0.92, and 0.94, respectively, significantly outperforming the other conventional deep learning models. These results indicate the method's effectiveness in identifying and accurately classifying various agricultural diseases, particularly excelling in handling subtle features and complex data. In the task of generating descriptive text from agricultural images, the method also exhibited impressive performance, with a precision, recall, and accuracy of 0.92, 0.88, and 0.91, respectively. This demonstrates that the method can not only deeply understand the content of agricultural images but also generate accurate and rich descriptive texts. The object detection experiment further validated the effectiveness of our approach, where the method achieved a precision, recall, and accuracy of 0.96, 0.91, and 0.94. This achievement highlights the method's capability for accurately locating and identifying agricultural targets, especially in complex environments. Overall, the approach in this study not only demonstrated exceptional performance in multiple tasks such as agricultural disease detection, image captioning, and object detection but also showcased the immense potential of multimodal data and deep learning technologies in the application of intelligent agriculture.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Chunli Lv
- China Agricultural University, Beijing 100083, China
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Mathieu L, Reder M, Siah A, Ducasse A, Langlands-Perry C, Marcel TC, Morel JB, Saintenac C, Ballini E. SeptoSympto: a precise image analysis of Septoria tritici blotch disease symptoms using deep learning methods on scanned images. PLANT METHODS 2024; 20:18. [PMID: 38297386 PMCID: PMC10832182 DOI: 10.1186/s13007-024-01136-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 01/07/2024] [Indexed: 02/02/2024]
Abstract
BACKGROUND Investigations on plant-pathogen interactions require quantitative, accurate, and rapid phenotyping of crop diseases. However, visual assessment of disease symptoms is preferred over available numerical tools due to transferability challenges. These assessments are laborious, time-consuming, require expertise, and are rater dependent. More recently, deep learning has produced interesting results for evaluating plant diseases. Nevertheless, it has yet to be used to quantify the severity of Septoria tritici blotch (STB) caused by Zymoseptoria tritici-a frequently occurring and damaging disease on wheat crops. RESULTS We developed an image analysis script in Python, called SeptoSympto. This script uses deep learning models based on the U-Net and YOLO architectures to quantify necrosis and pycnidia on detached, flattened and scanned leaves of wheat seedlings. Datasets of different sizes (containing 50, 100, 200, and 300 leaves) were annotated to train Convolutional Neural Networks models. Five different datasets were tested to develop a robust tool for the accurate analysis of STB symptoms and facilitate its transferability. The results show that (i) the amount of annotated data does not influence the performances of models, (ii) the outputs of SeptoSympto are highly correlated with those of the experts, with a similar magnitude to the correlations between experts, and (iii) the accuracy of SeptoSympto allows precise and rapid quantification of necrosis and pycnidia on both durum and bread wheat leaves inoculated with different strains of the pathogen, scanned with different scanners and grown under different conditions. CONCLUSIONS SeptoSympto takes the same amount of time as a visual assessment to evaluate STB symptoms. However, unlike visual assessments, it allows for data to be stored and evaluated by experts and non-experts in a more accurate and unbiased manner. The methods used in SeptoSympto make it a transferable, highly accurate, computationally inexpensive, easy-to-use, and adaptable tool. This study demonstrates the potential of using deep learning to assess complex plant disease symptoms such as STB.
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Affiliation(s)
- Laura Mathieu
- PHIM Plant Health Institute, Univ Montpellier, INRAE, CIRAD, Institut Agro, IRD, Montpellier, France.
| | - Maxime Reder
- PHIM Plant Health Institute, Univ Montpellier, INRAE, CIRAD, Institut Agro, IRD, Montpellier, France
| | - Ali Siah
- BioEcoAgro, Junia, Lille University, Liège University, UPJV, Artois University, ULCO, INRAE, Lille, France
| | - Aurélie Ducasse
- PHIM Plant Health Institute, Univ Montpellier, INRAE, CIRAD, Institut Agro, IRD, Montpellier, France
| | | | | | - Jean-Benoît Morel
- PHIM Plant Health Institute, Univ Montpellier, INRAE, CIRAD, Institut Agro, IRD, Montpellier, France
| | | | - Elsa Ballini
- PHIM Plant Health Institute, Univ Montpellier, CIRAD, INRAE, IRD, Institut Agro, Montpellier, France.
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Liu B, Wei S, Zhang F, Guo N, Fan H, Yao W. Tomato leaf disease recognition based on multi-task distillation learning. FRONTIERS IN PLANT SCIENCE 2024; 14:1330527. [PMID: 38352252 PMCID: PMC10862124 DOI: 10.3389/fpls.2023.1330527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/28/2023] [Indexed: 02/16/2024]
Abstract
Introduction Tomato leaf diseases can cause major yield and quality losses. Computer vision techniques for automated disease recognition show promise but face challenges like symptom variations, limited labeled data, and model complexity. Methods Prior works explored hand-crafted and deep learning features for tomato disease classification and multi-task severity prediction, but did not sufficiently exploit the shared and unique knowledge between these tasks. We present a novel multi-task distillation learning (MTDL) framework for comprehensive diagnosis of tomato leaf diseases. It employs knowledge disentanglement, mutual learning, and knowledge integration through a multi-stage strategy to leverage the complementary nature of classification and severity prediction. Results Experiments show our framework improves performance while reducing model complexity. The MTDL-optimized EfficientNet outperforms single-task ResNet101 in classification accuracy by 0.68% and severity estimation by 1.52%, using only 9.46% of its parameters. Discussion The findings demonstrate the practical potential of our framework for intelligent agriculture applications.
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Affiliation(s)
- Bo Liu
- College of Information Science and Technology, Hebei Agricultural University, Baoding, China
- Hebei Key Laboratory of Agricultural Big Data, Baoding, China
| | - Shusen Wei
- College of Information Science and Technology, Hebei Agricultural University, Baoding, China
- Hebei Key Laboratory of Agricultural Big Data, Baoding, China
| | - Fan Zhang
- College of Information Science and Technology, Hebei Agricultural University, Baoding, China
- Hebei Key Laboratory of Agricultural Big Data, Baoding, China
| | - Nawei Guo
- College of Information Science and Technology, Hebei Agricultural University, Baoding, China
- Hebei Key Laboratory of Agricultural Big Data, Baoding, China
| | - Hongyu Fan
- College of Information Science and Technology, Hebei Agricultural University, Baoding, China
- Hebei Key Laboratory of Agricultural Big Data, Baoding, China
| | - Wei Yao
- College of Information Science and Technology, Hebei Agricultural University, Baoding, China
- Hebei Key Laboratory of Agricultural Big Data, Baoding, China
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Bedi P, Gole P, Marwaha S. PDSE-Lite: lightweight framework for plant disease severity estimation based on Convolutional Autoencoder and Few-Shot Learning. FRONTIERS IN PLANT SCIENCE 2024; 14:1319894. [PMID: 38259916 PMCID: PMC10800669 DOI: 10.3389/fpls.2023.1319894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 12/12/2023] [Indexed: 01/24/2024]
Abstract
Plant disease diagnosis with estimation of disease severity at early stages still remains a significant research challenge in agriculture. It is helpful in diagnosing plant diseases at the earliest so that timely action can be taken for curing the disease. Existing studies often rely on labor-intensive manually annotated large datasets for disease severity estimation. In order to conquer this problem, a lightweight framework named "PDSE-Lite" based on Convolutional Autoencoder (CAE) and Few-Shot Learning (FSL) is proposed in this manuscript for plant disease severity estimation with few training instances. The PDSE-Lite framework is designed and developed in two stages. In first stage, a lightweight CAE model is built and trained to reconstruct leaf images from original leaf images with minimal reconstruction loss. In subsequent stage, pretrained layers of the CAE model built in the first stage are utilized to develop the image classification and segmentation models, which are then trained using FSL. By leveraging FSL, the proposed framework requires only a few annotated instances for training, which significantly reduces the human efforts required for data annotation. Disease severity is then calculated by determining the percentage of diseased leaf pixels obtained through segmentation out of the total leaf pixels. The PDSE-Lite framework's performance is evaluated on Apple-Tree-Leaf-Disease-Segmentation (ATLDS) dataset. However, the proposed framework can identify any plant disease and quantify the severity of identified diseases. Experimental results reveal that the PDSE-Lite framework can accurately detect healthy and four types of apple tree diseases as well as precisely segment the diseased area from leaf images by using only two training samples from each class of the ATLDS dataset. Furthermore, the PDSE-Lite framework's performance is compared with existing state-of-the-art techniques, and it is found that this framework outperformed these approaches. The proposed framework's applicability is further verified by statistical hypothesis testing using Student t-test. The results obtained from this test confirm that the proposed framework can precisely estimate the plant disease severity with a confidence interval of 99%. Hence, by reducing the reliance on large-scale manual data annotation, the proposed framework offers a promising solution for early-stage plant disease diagnosis and severity estimation.
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Affiliation(s)
- Punam Bedi
- Department of Computer Science, University of Delhi, New Delhi, India
| | - Pushkar Gole
- Department of Computer Science, University of Delhi, New Delhi, India
| | - Sudeep Marwaha
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
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Li Y, Guo J, Qiu H, Chen F, Zhang J. Denoising Diffusion Probabilistic Models and Transfer Learning for citrus disease diagnosis. FRONTIERS IN PLANT SCIENCE 2023; 14:1267810. [PMID: 38146275 PMCID: PMC10749533 DOI: 10.3389/fpls.2023.1267810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 11/14/2023] [Indexed: 12/27/2023]
Abstract
Problems Plant Disease diagnosis based on deep learning mechanisms has been extensively studied and applied. However, the complex and dynamic agricultural growth environment results in significant variations in the distribution of state samples, and the lack of sufficient real disease databases weakens the information carried by the samples, posing challenges for accurately training models. Aim This paper aims to test the feasibility and effectiveness of Denoising Diffusion Probabilistic Models (DDPM), Swin Transformer model, and Transfer Learning in diagnosing citrus diseases with a small sample. Methods Two training methods are proposed: The Method 1 employs the DDPM to generate synthetic images for data augmentation. The Swin Transformer model is then used for pre-training on the synthetic dataset produced by DDPM, followed by fine-tuning on the original citrus leaf images for disease classification through transfer learning. The Method 2 utilizes the pre-trained Swin Transformer model on the ImageNet dataset and fine-tunes it on the augmented dataset composed of the original and DDPM synthetic images. Results and conclusion The test results indicate that Method 1 achieved a validation accuracy of 96.3%, while Method 2 achieved a validation accuracy of 99.8%. Both methods effectively addressed the issue of model overfitting when dealing with a small dataset. Additionally, when compared with VGG16, EfficientNet, ShuffleNet, MobileNetV2, and DenseNet121 in citrus disease classification, the experimental results demonstrate the superiority of the proposed methods over existing approaches to a certain extent.
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Affiliation(s)
| | - Jianwen Guo
- School of Mechanical Engineering, Dongguan University of Technology, Dongguan, Guangdong, China
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Xu Y, Mao Y, Li H, Sun L, Wang S, Li X, Shen J, Yin X, Fan K, Ding Z, Wang Y. A deep learning model for rapid classification of tea coal disease. PLANT METHODS 2023; 19:98. [PMID: 37689676 PMCID: PMC10492339 DOI: 10.1186/s13007-023-01074-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 08/29/2023] [Indexed: 09/11/2023]
Abstract
BACKGROUND The common tea tree disease known as "tea coal disease" (Neocapnodium theae Hara) can have a negative impact on tea yield and quality. The majority of conventional approaches for identifying tea coal disease rely on observation with the human naked eye, which is labor- and time-intensive and frequently influenced by subjective factors. The present study developed a deep learning model based on RGB and hyperspectral images for tea coal disease rapid classification. RESULTS Both RGB and hyperspectral could be used for classifying tea coal disease. The accuracy of the classification models established by RGB imaging using ResNet18, VGG16, AlexNet, WT-ResNet18, WT-VGG16, and WT-AlexNet was 60%, 58%, 52%, 70%, 64%, and 57%, respectively, and the optimal classification model for RGB was the WT-ResNet18. The accuracy of the classification models established by hyperspectral imaging using UVE-LSTM, CARS-LSTM, NONE-LSTM, UVE-SVM, CARS-SVM, and NONE-SVM was 80%, 95%, 90%, 61%, 77%, and 65%, respectively, and the optimal classification model for hyperspectral was the CARS-LSTM, which was superior to the model based on RGB imaging. CONCLUSIONS This study revealed the classification potential of tea coal disease based on RGB and hyperspectral imaging, which can provide an accurate, non-destructive, and efficient classification method for monitoring tea coal disease.
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Affiliation(s)
- Yang Xu
- Tea Research Institute, Qingdao Agricultural University, Qingdao, 266109, China
| | - Yilin Mao
- Tea Research Institute, Qingdao Agricultural University, Qingdao, 266109, China
| | - He Li
- Tea Research Institute, Qingdao Agricultural University, Qingdao, 266109, China
| | - Litao Sun
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Shuangshuang Wang
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Xiaojiang Li
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Jiazhi Shen
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Xinyue Yin
- Tea Research Institute, Qingdao Agricultural University, Qingdao, 266109, China
| | - Kai Fan
- Tea Research Institute, Qingdao Agricultural University, Qingdao, 266109, China
| | - Zhaotang Ding
- Tea Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China.
| | - Yu Wang
- Tea Research Institute, Qingdao Agricultural University, Qingdao, 266109, China.
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Wang H, Ding J, He S, Feng C, Zhang C, Fan G, Wu Y, Zhang Y. MFBP-UNet: A Network for Pear Leaf Disease Segmentation in Natural Agricultural Environments. PLANTS (BASEL, SWITZERLAND) 2023; 12:3209. [PMID: 37765373 PMCID: PMC10537337 DOI: 10.3390/plants12183209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/23/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023]
Abstract
The accurate prevention and control of pear tree diseases, especially the precise segmentation of leaf diseases, poses a serious challenge to fruit farmers globally. Given the possibility of disease areas being minute with ambiguous boundaries, accurate segmentation becomes difficult. In this study, we propose a pear leaf disease segmentation model named MFBP-UNet. It is based on the UNet network architecture and integrates a Multi-scale Feature Extraction (MFE) module and a Tokenized Multilayer Perceptron (BATok-MLP) module with dynamic sparse attention. The MFE enhances the extraction of detail and semantic features, while the BATok-MLP successfully fuses regional and global attention, striking an effective balance in the extraction capabilities of both global and local information. Additionally, we pioneered the use of a diffusion model for data augmentation. By integrating and analyzing different augmentation methods, we further improved the model's training accuracy and robustness. Experimental results reveal that, compared to other segmentation networks, MFBP-UNet shows a significant improvement across all performance metrics. Specifically, MFBP-UNet achieves scores of 86.15%, 93.53%, 90.89%, and 0.922 on MIoU, MP, MPA, and Dice metrics, marking respective improvements of 5.75%, 5.79%, 1.08%, and 0.074 over the UNet model. These results demonstrate the MFBP-UNet model's superior performance and generalization capabilities in pear leaf disease segmentation and its inherent potential to address analogous challenges in natural environment segmentation tasks.
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Affiliation(s)
- Haoyu Wang
- School of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China; (H.W.); (J.D.); (C.Z.); (G.F.)
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei 230036, China; (S.H.); (C.F.)
| | - Jie Ding
- School of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China; (H.W.); (J.D.); (C.Z.); (G.F.)
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei 230036, China; (S.H.); (C.F.)
| | - Sifan He
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei 230036, China; (S.H.); (C.F.)
- School of Natural Science, Anhui Agricultural University, Hefei 230036, China
| | - Cheng Feng
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei 230036, China; (S.H.); (C.F.)
- School of Natural Science, Anhui Agricultural University, Hefei 230036, China
| | - Cheng Zhang
- School of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China; (H.W.); (J.D.); (C.Z.); (G.F.)
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei 230036, China; (S.H.); (C.F.)
| | - Guohua Fan
- School of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China; (H.W.); (J.D.); (C.Z.); (G.F.)
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei 230036, China; (S.H.); (C.F.)
| | - Yunzhi Wu
- School of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China; (H.W.); (J.D.); (C.Z.); (G.F.)
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei 230036, China; (S.H.); (C.F.)
| | - Youhua Zhang
- School of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China; (H.W.); (J.D.); (C.Z.); (G.F.)
- Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei 230036, China; (S.H.); (C.F.)
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11
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Xu W, Wang R. ALAD-YOLO:an lightweight and accurate detector for apple leaf diseases. FRONTIERS IN PLANT SCIENCE 2023; 14:1204569. [PMID: 37662152 PMCID: PMC10472937 DOI: 10.3389/fpls.2023.1204569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 06/12/2023] [Indexed: 09/05/2023]
Abstract
Suffering from various apple leaf diseases, timely preventive measures are necessary to take. Currently, manual disease discrimination has high workloads, while automated disease detection algorithms face the trade-off between detection accuracy and speed. Therefore, an accurate and lightweight model for apple leaf disease detection based on YOLO-V5s (ALAD-YOLO) is proposed in this paper. An apple leaf disease detection dataset is collected, containing 2,748 images of diseased apple leaves under a complex environment, such as from different shooting angles, during different spans of the day, and under different weather conditions. Moreover, various data augmentation algorithms are applied to improve the model generalization. The model size is compressed by introducing the Mobilenet-V3s basic block, which integrates the coordinate attention (CA) mechanism in the backbone network and replacing the ordinary convolution with group convolution in the Spatial Pyramid Pooling Cross Stage Partial Conv (SPPCSPC) module, depth-wise convolution, and Ghost module in the C3 module in the neck network, while maintaining a high detection accuracy. Experimental results show that ALAD-YOLO balances detection speed and accuracy well, achieving an accuracy of 90.2% (an improvement of 7.9% compared with yolov5s) on the test set and reducing the floating point of operations (FLOPs) to 6.1 G (a decrease of 9.7 G compared with yolov5s). In summary, this paper provides an accurate and efficient detection method for apple leaf disease detection and other related fields.
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Affiliation(s)
- Weishi Xu
- School of Intelligent Science and Technology, East China University of Science and Technology, Shanghai, China
| | - Runjie Wang
- School of Geological Engineering, Tongji University, Shanghai, China
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12
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Kumar TA, Rajmohan R, Adeola Ajagbe S, Gaber T, Zeng XJ, Masmoudi F. A novel CNN gap layer for growth prediction of palm tree plantlings. PLoS One 2023; 18:e0289963. [PMID: 37566602 PMCID: PMC10420369 DOI: 10.1371/journal.pone.0289963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 07/28/2023] [Indexed: 08/13/2023] Open
Abstract
Monitoring palm tree seedlings and plantlings presents a formidable challenge because of the microscopic size of these organisms and the absence of distinguishing morphological characteristics. There is a demand for technical approaches that can provide restoration specialists with palm tree seedling monitoring systems that are high-resolution, quick, and environmentally friendly. It is possible that counting plantlings and identifying them down to the genus level will be an extremely time-consuming and challenging task. It has been demonstrated that convolutional neural networks, or CNNs, are effective in many aspects of image recognition; however, the performance of CNNs differs depending on the application. The performance of the existing CNN-based models for monitoring and predicting plantlings growth could be further improved. To achieve this, a novel Gap Layer modified CNN architecture (GL-CNN) has been proposed with an IoT effective monitoring system and UAV technology. The UAV is employed for capturing plantlings images and the IoT model is utilized for obtaining the ground truth information of the plantlings health. The proposed model is trained to predict the successful and poor seedling growth for a given set of palm tree plantling images. The proposed GL-CNN architecture is novel in terms of defined convolution layers and the gap layer designed for output classification. There are two 64×3 conv layers, two 128×3 conv layers, two 256×3 conv layers and one 512×3 conv layer for processing of input image. The output obtained from the gap layer is modulated using the ReLU classifier for determining the seedling classification. To evaluate the proposed system, a new dataset of palm tree plantlings was collected in real time using UAV technology. This dataset consists of images of palm tree plantlings. The evaluation results showed that the proposed GL-CNN model performed better than the existing CNN architectures with an average accuracy of 95.96%.
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Affiliation(s)
- T. Ananth Kumar
- Computer Science and Engineering, IFET College of Engineering, Valavanur, Viluppuram, India
| | - R. Rajmohan
- Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India
| | - Sunday Adeola Ajagbe
- Department of Computer & Industrial Production Engineering, First Technical University Ibadan, Ibadan, Nigeria
| | - Tarek Gaber
- Computer Science & Software Engineering, University of Salford, Manchester, United Kingdom
- Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt
| | - Xiao-Jun Zeng
- Department of Computer Science, University of Manchester, Manchester, United Kingdom
| | - Fatma Masmoudi
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
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13
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Mittal U, Wadhawan A, Singla J, Jhanjhi NZ, Ghoniem RM, Ray SK, Abdelmaboud A. Plant Disease Detection and Classification: A Systematic Literature Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:4769. [PMID: 37430683 DOI: 10.3390/s23104769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 05/06/2023] [Accepted: 05/10/2023] [Indexed: 07/12/2023]
Abstract
A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy.
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Affiliation(s)
- Usha Mittal
- Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India
| | - Ankita Wadhawan
- Department of Computer Science and Engineering, Lovely Professional University, Phagwara 144411, Punjab, India
| | - Jimmy Singla
- School of Engineering and Technology, CT University, Ludhiana 142024, Punjab, India
| | - N Z Jhanjhi
- School of Computer Science, SCS, Taylor's University, Subang Jaya 47500, Malaysia
| | - Rania M Ghoniem
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Riyadh, Saudi Arabia
| | - Sayan Kumar Ray
- School of Computer Science, SCS, Taylor's University, Subang Jaya 47500, Malaysia
| | - Abdelzahir Abdelmaboud
- Department of Information Systems, King Khalid University, Abha 61913, Muhayel Aseer, Saudi Arabia
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14
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Ogidi FC, Eramian MG, Stavness I. Benchmarking Self-Supervised Contrastive Learning Methods for Image-Based Plant Phenotyping. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0037. [PMID: 37040288 PMCID: PMC10079263 DOI: 10.34133/plantphenomics.0037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 02/28/2023] [Indexed: 06/19/2023]
Abstract
The rise of self-supervised learning (SSL) methods in recent years presents an opportunity to leverage unlabeled and domain-specific datasets generated by image-based plant phenotyping platforms to accelerate plant breeding programs. Despite the surge of research on SSL, there has been a scarcity of research exploring the applications of SSL to image-based plant phenotyping tasks, particularly detection and counting tasks. We address this gap by benchmarking the performance of 2 SSL methods-momentum contrast (MoCo) v2 and dense contrastive learning (DenseCL)-against the conventional supervised learning method when transferring learned representations to 4 downstream (target) image-based plant phenotyping tasks: wheat head detection, plant instance detection, wheat spikelet counting, and leaf counting. We studied the effects of the domain of the pretraining (source) dataset on the downstream performance and the influence of redundancy in the pretraining dataset on the quality of learned representations. We also analyzed the similarity of the internal representations learned via the different pretraining methods. We find that supervised pretraining generally outperforms self-supervised pretraining and show that MoCo v2 and DenseCL learn different high-level representations compared to the supervised method. We also find that using a diverse source dataset in the same domain as or a similar domain to the target dataset maximizes performance in the downstream task. Finally, our results show that SSL methods may be more sensitive to redundancy in the pretraining dataset than the supervised pretraining method. We hope that this benchmark/evaluation study will guide practitioners in developing better SSL methods for image-based plant phenotyping.
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15
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Dracatos PM, Lück S, Douchkov DK. Diversifying Resistance Mechanisms in Cereal Crops Using Microphenomics. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0023. [PMID: 37040289 PMCID: PMC10076052 DOI: 10.34133/plantphenomics.0023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 01/04/2023] [Indexed: 06/19/2023]
Affiliation(s)
- Peter M. Dracatos
- Department of Animal, Plant and Soil Sciences, AgriBio, La Trobe University, Bundoora, VIC 3086, Australia
| | - Stefanie Lück
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstrasse 3, 06466 Seeland OT Gatersleben, Germany
| | - Dimitar K. Douchkov
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Corrensstrasse 3, 06466 Seeland OT Gatersleben, Germany
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16
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Sharma V, Tripathi AK, Mittal H. DLMC-Net: Deeper lightweight multi-class classification model for plant leaf disease detection. ECOL INFORM 2023. [DOI: 10.1016/j.ecoinf.2023.102025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
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17
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Patnaik V, Mohanty M, Subudhi AK. Identification of healthy biological leafs using hybrid-feature classifier. THE IMAGING SCIENCE JOURNAL 2022. [DOI: 10.1080/13682199.2022.2157533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Vijaya Patnaik
- Department of ECE, ITER, SOA Deemed to be University, Odisha, India
| | - Monalisa Mohanty
- Department of ECE, ITER, SOA Deemed to be University, Odisha, India
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18
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TomFusioNet: A tomato crop analysis framework for mobile applications using the multi-objective optimization based late fusion of deep models and background elimination. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
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19
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Automated Detection of Mechanical Damage in Flaxseeds Using Radiographic Imaging and Machine Learning. FOOD BIOPROCESS TECH 2022. [DOI: 10.1007/s11947-022-02939-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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20
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An Improved Deep Residual Convolutional Neural Network for Plant Leaf Disease Detection. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5102290. [PMID: 36156945 PMCID: PMC9492343 DOI: 10.1155/2022/5102290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 08/15/2022] [Accepted: 08/16/2022] [Indexed: 11/22/2022]
Abstract
In this research, we proposed a novel deep residual convolutional neural network with 197 layers (ResNet197) for the detection of various plant leaf diseases. Six blocks of layers were used to develop ResNet197. ResNet197 was trained and tested using a combined plant leaf disease image dataset. Scaling, cropping, flipping, padding, rotation, affine transformation, saturation, and hue transformation techniques were used to create the augmentation data of the plant leaf disease image dataset. The dataset consisted of 103 diseased and healthy image classes of 22 plants and 154,500 images of healthy and diseased plant leaves. The evolutionary search technique was used to optimise the layers and hyperparameter values of ResNet197. ResNet197 was trained on the combined plant leaf disease image dataset using a graphics processing unit (GPU) environment for 1000 epochs. It produced a 99.58 percentage average classification accuracy on the test dataset. The experimental results were superior to existing ResNet architectures and recent transfer learning techniques.
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21
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Sutaji D, Yıldız O. LEMOXINET: Lite ensemble MobileNetV2 and Xception models to predict plant disease. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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22
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Islam MS, Sultana S, Farid FA, Islam MN, Rashid M, Bari BS, Hashim N, Husen MN. Multimodal Hybrid Deep Learning Approach to Detect Tomato Leaf Disease Using Attention Based Dilated Convolution Feature Extractor with Logistic Regression Classification. SENSORS (BASEL, SWITZERLAND) 2022; 22:6079. [PMID: 36015839 PMCID: PMC9414454 DOI: 10.3390/s22166079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/07/2022] [Accepted: 07/11/2022] [Indexed: 06/15/2023]
Abstract
Automatic leaf disease detection techniques are effective for reducing the time-consuming effort of monitoring large crop farms and early identification of disease symptoms of plant leaves. Although crop tomatoes are seen to be susceptible to a variety of diseases that can reduce the production of the crop. In recent years, advanced deep learning methods show successful applications for plant disease detection based on observed symptoms on leaves. However, these methods have some limitations. This study proposed a high-performance tomato leaf disease detection approach, namely attention-based dilated CNN logistic regression (ADCLR). Firstly, we develop a new feature extraction method using attention-based dilated CNN to extract most relevant features in a faster time. In our preprocessing, we use Bilateral filtering to handle larger features to make the image smoother and the Ostu image segmentation process to remove noise in a fast and simple way. In this proposed method, we preprocess the image with bilateral filtering and Otsu segmentation. Then, we use the Conditional Generative Adversarial Network (CGAN) model to generate a synthetic image from the image which is preprocessed in the previous stage. The synthetic image is generated to handle imbalance and noisy or wrongly labeled data to obtain good prediction results. Then, the extracted features are normalized to lower the dimensionality. Finally, extracted features from preprocessed data are combined and then classified using fast and simple logistic regression (LR) classifier. The experimental outcomes show the state-of-the-art performance on the Plant Village database of tomato leaf disease by achieving 100%, 100%, 96.6% training, testing, and validation accuracy, respectively, for multiclass. From the experimental analysis, it is clearly demonstrated that the proposed multimodal approach can be utilized to detect tomato leaf disease precisely, simply and quickly. We have a potential plan to improve the model to make it cloud-based automated leaf disease classification for different plants.
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Affiliation(s)
- Md Shofiqul Islam
- Faculty of Computing, Universiti Malaysia Pahang, Kuantan 26300, Pahang, Malaysia
| | - Sunjida Sultana
- Department of Computer Science and Engineeering, Islamic University, Kushtia 7003, Bangladesh
| | - Fahmid Al Farid
- Faculty of Computing and Informatics, Multimedia University, Cyberjaya 63100, Selangor, Malaysia
| | - Md Nahidul Islam
- Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan 26600, Pahang, Malaysia
| | - Mamunur Rashid
- Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan 26600, Pahang, Malaysia
| | - Bifta Sama Bari
- Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pekan 26600, Pahang, Malaysia
| | - Noramiza Hashim
- Faculty of Computing and Informatics, Multimedia University, Cyberjaya 63100, Selangor, Malaysia
| | - Mohd Nizam Husen
- Malaysian Institute of Information Technology, Universiti Kuala Lumpur, Kuala Lumpur 50250, Selangor, Malaysia
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23
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Bouguettaya A, Zarzour H, Kechida A, Taberkit AM. A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images. CLUSTER COMPUTING 2022; 26:1297-1317. [PMID: 35968221 PMCID: PMC9362359 DOI: 10.1007/s10586-022-03627-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 04/12/2022] [Accepted: 05/10/2022] [Indexed: 06/15/2023]
Abstract
The agricultural crop productivity can be affected and reduced due to many factors such as weeds, pests, and diseases. Traditional methods that are based on terrestrial engines, devices, and farmers' naked eyes are facing many limitations in terms of accuracy and the required time to cover large fields. Currently, precision agriculture that is based on the use of deep learning algorithms and Unmanned Aerial Vehicles (UAVs) provides an effective solution to achieve agriculture applications, including plant disease identification and treatment. In the last few years, plant disease monitoring using UAV platforms is one of the most important agriculture applications that have gained increasing interest by researchers. Accurate detection and treatment of plant diseases at early stages is crucial to improving agricultural production. To this end, in this review, we analyze the recent advances in the use of computer vision techniques that are based on deep learning algorithms and UAV technologies to identify and treat crop diseases.
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Affiliation(s)
- Abdelmalek Bouguettaya
- Research Centre in Industrial Technologies (CRTI), P.O. Box 64, Cheraga, 16014 Algiers, Algeria
| | - Hafed Zarzour
- LIM Research, Department of Mathematics and Computer Science, Souk Ahras University, 41000 Souk Ahras, Algeria
| | - Ahmed Kechida
- Research Centre in Industrial Technologies (CRTI), P.O. Box 64, Cheraga, 16014 Algiers, Algeria
| | - Amine Mohammed Taberkit
- Research Centre in Industrial Technologies (CRTI), P.O. Box 64, Cheraga, 16014 Algiers, Algeria
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24
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Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images. REMOTE SENSING 2022. [DOI: 10.3390/rs14132966] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
This article aims at performing maritime target classification in SAR images using machine learning (ML) and deep learning (DL) techniques. In particular, the targets of interest are oil platforms and ships located in the Campos Basin, Brazil. Two convolutional neural networks (CNNs), VGG-16 and VGG-19, were used for attribute extraction. The logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (kNN), decision tree (DT), naive Bayes (NB), neural networks (NET), and AdaBoost (ADBST) schemes were considered for classification. The target classification methods were evaluated using polarimetric images obtained from the C-band synthetic aperture radar (SAR) system Sentinel-1. Classifiers are assessed by the accuracy indicator. The LR, SVM, NET, and stacking results indicate better performance, with accuracy ranging from 84.1% to 85.5%. The Kruskal–Wallis test shows a significant difference with the tested classifier, indicating that some classifiers present different accuracy results. The optimizations provide results with more significant accuracy gains, making them competitive with those shown in the literature. There is no exact combination of methods for SAR image classification that will always guarantee the best accuracy. The optimizations performed in this article were for the specific data set of the Campos Basin, and results may change depending on the data set format and the number of images.
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25
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SK-MobileNet: A Lightweight Adaptive Network Based on Complex Deep Transfer Learning for Plant Disease Recognition. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-022-06987-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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26
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Franchetti B, Pirri F. Detection and Localization of Tip-Burn on Large Lettuce Canopies. FRONTIERS IN PLANT SCIENCE 2022; 13:874035. [PMID: 35646012 PMCID: PMC9133957 DOI: 10.3389/fpls.2022.874035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 03/29/2022] [Indexed: 06/15/2023]
Abstract
Recent years have seen an increased effort in the detection of plant stresses and diseases using non-invasive sensors and deep learning methods. Nonetheless, no studies have been made on dense plant canopies, due to the difficulty in automatically zooming into each plant, especially in outdoor conditions. Zooming in and zooming out is necessary to focus on the plant stress and to precisely localize the stress within the canopy, for further analysis and intervention. This work concentrates on tip-burn, which is a plant stress affecting lettuce grown in controlled environmental conditions, such as in plant factories. We present a new method for tip-burn stress detection and localization, combining both classification and self-supervised segmentation to detect, localize, and closely segment the stressed regions. Starting with images of a dense canopy collecting about 1,000 plants, the proposed method is able to zoom into the tip-burn region of a single plant, covering less than 1/10th of the plant itself. The method is crucial for solving the manual phenotyping that is required in plant factories. The precise localization of the stress within the plant, of the plant within the tray, and of the tray within the table canopy allows to automatically deliver statistics and causal annotations. We have tested our method on different data sets, which do not provide any ground truth segmentation mask, neither for the leaves nor for the stresses; therefore, the results on the self-supervised segmentation is even more impressive. Results show that the accuracy for both classification and self supervised segmentation is new and efficacious. Finally, the data set used for training test and validation is currently available on demand.
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Affiliation(s)
| | - Fiora Pirri
- Alcor Lab, DIAG, Sapienza University of Rome, Rome, Italy
- Deep Plants, Rome, Italy
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27
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FF-PCA-LDA: Intelligent Feature Fusion Based PCA-LDA Classification System for Plant Leaf Diseases. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073514] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Crop leaf disease management and control pose significant impact on enhancement in yield and quality to fulfill consumer needs. For smart agriculture, an intelligent leaf disease identification system is inevitable for efficient crop health monitoring. In this view, a novel approach is proposed for crop disease identification using feature fusion and PCA-LDA classification (FF-PCA-LDA). Handcrafted hybrid and deep features are extracted from RGB images. TL-ResNet50 is used to extract the deep features. Fused feature vector is obtained by combining handcrafted hybrid and deep features. After fusing the image features, PCA is employed to select most discriminant features for LDA model development. Potato crop leaf disease identification is used as a case study for the validation of the approach. The developed system is experimentally validated on a potato crop leaf benchmark dataset. It offers high accuracy of 98.20% on an unseen dataset which was not used during the model training process. Performance comparison of the proposed technique with other approaches shows its superiority. Owing to the better discrimination and learning ability, the proposed approach overcomes the leaf segmentation step. The developed approach may be used as an automated tool for crop monitoring, management control, and can be extended for other crop types.
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28
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Hybrid Feature-Based Disease Detection in Plant Leaf Using Convolutional Neural Network, Bayesian Optimized SVM, and Random Forest Classifier. J FOOD QUALITY 2022. [DOI: 10.1155/2022/2845320] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Plant diseases are unfavourable factors that cause a significant decrease in the quality and quantity of crops. Experienced biologists or farmers often observe plants with the naked eye for disease, but this method is often imprecise and can take a long time. In this study, we use artificial intelligence and computer vision techniques to achieve the goal of designing and developing an intelligent classification mechanism for leaf diseases. This paper follows two methodologies and their simulation outcomes are compared for performance evaluation. In the first part, data augmentation is performed on the PlantVillage data set images (for apple, corn, potato, tomato, and rice plants), and their deep features are extracted using convolutional neural network (CNN). These features are classified by a Bayesian optimized support vector machine classifier and the results attained in terms of precision, sensitivity, f-score, and accuracy. The above-said methodologies will enable farmers all over the world to take early action to prevent their crops from becoming irreversibly damaged, thereby saving the world and themselves from a potential economic crisis. The second part of the methodology starts with the preprocessing of data set images, and their texture and color features are extracted by histogram of oriented gradient (HoG), GLCM, and color moments. Here, the three types of features, that is, color, texture, and deep features, are combined to form hybrid features. The binary particle swarm optimization is applied for the selection of these hybrid features followed by the classification with random forest classifier to get the simulation results. Binary particle swarm optimization plays a crucial role in hybrid feature selection; the purpose of this Algorithm is to obtain the suitable output with the least features. The comparative analysis of both techniques is presented with the use of the above-mentioned evaluation parameters.
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29
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Shafi U, Mumtaz R, Haq IU, Hafeez M, Iqbal N, Shaukat A, Zaidi SMH, Mahmood Z. Wheat Yellow Rust Disease Infection Type Classification Using Texture Features. SENSORS (BASEL, SWITZERLAND) 2021; 22:146. [PMID: 35009689 PMCID: PMC8747460 DOI: 10.3390/s22010146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 12/12/2021] [Accepted: 12/16/2021] [Indexed: 06/14/2023]
Abstract
Wheat is a staple crop of Pakistan that covers almost 40% of the cultivated land and contributes almost 3% in the overall Gross Domestic Product (GDP) of Pakistan. However, due to increasing seasonal variation, it was observed that wheat is majorly affected by rust disease, particularly in rain-fed areas. Rust is considered the most harmful fungal disease for wheat, which can cause reductions of 20-30% in wheat yield. Its capability to spread rapidly over time has made its management most challenging, becoming a major threat to food security. In order to counter this threat, precise detection of wheat rust and its infection types is important for minimizing yield losses. For this purpose, we have proposed a framework for classifying wheat yellow rust infection types using machine learning techniques. First, an image dataset of different yellow rust infections was collected using mobile cameras. Six Gray Level Co-occurrence Matrix (GLCM) texture features and four Local Binary Patterns (LBP) texture features were extracted from grayscale images of the collected dataset. In order to classify wheat yellow rust disease into its three classes (healthy, resistant, and susceptible), Decision Tree, Random Forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and CatBoost were used with (i) GLCM, (ii) LBP, and (iii) combined GLCM-LBP texture features. The results indicate that CatBoost outperformed on GLCM texture features with an accuracy of 92.30%. This accuracy can be further improved by scaling up the dataset and applying deep learning models. The development of the proposed study could be useful for the agricultural community for the early detection of wheat yellow rust infection and assist in taking remedial measures to contain crop yield.
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Affiliation(s)
- Uferah Shafi
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; (U.S.); (I.U.H.); (N.I.); (S.M.H.Z.)
| | - Rafia Mumtaz
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; (U.S.); (I.U.H.); (N.I.); (S.M.H.Z.)
| | - Ihsan Ul Haq
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; (U.S.); (I.U.H.); (N.I.); (S.M.H.Z.)
| | - Maryam Hafeez
- Department of Engineering and Technology, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK;
| | - Naveed Iqbal
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; (U.S.); (I.U.H.); (N.I.); (S.M.H.Z.)
| | - Arslan Shaukat
- College of Electrical and Mechanical Engineering (CEME), National University of Sciences and Technology(NUST), Islamabad 44000, Pakistan;
| | - Syed Mohammad Hassan Zaidi
- School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan; (U.S.); (I.U.H.); (N.I.); (S.M.H.Z.)
| | - Zahid Mahmood
- Wheat Programme, Crop Sciences Institute, National Agricultural Research Centre (NARC), Islamabad 44000, Pakistan;
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Semantic Segmentation of Tree-Canopy in Urban Environment with Pixel-Wise Deep Learning. REMOTE SENSING 2021. [DOI: 10.3390/rs13163054] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Urban forests are an important part of any city, given that they provide several environmental benefits, such as improving urban drainage, climate regulation, public health, biodiversity, and others. However, tree detection in cities is challenging, given the irregular shape, size, occlusion, and complexity of urban areas. With the advance of environmental technologies, deep learning segmentation mapping methods can map urban forests accurately. We applied a region-based CNN object instance segmentation algorithm for the semantic segmentation of tree canopies in urban environments based on aerial RGB imagery. To the best of our knowledge, no study investigated the performance of deep learning-based methods for segmentation tasks inside the Cerrado biome, specifically for urban tree segmentation. Five state-of-the-art architectures were evaluated, namely: Fully Convolutional Network; U-Net; SegNet; Dynamic Dilated Convolution Network and DeepLabV3+. The experimental analysis showed the effectiveness of these methods reporting results such as pixel accuracy of 96,35%, an average accuracy of 91.25%, F1-score of 91.40%, Kappa of 82.80% and IoU of 73.89%. We also determined the inference time needed per area, and the deep learning methods investigated after the training proved to be suitable to solve this task, providing fast and effective solutions with inference time varying from 0.042 to 0.153 minutes per hectare. We conclude that the semantic segmentation of trees inside urban environments is highly achievable with deep neural networks. This information could be of high importance to decision-making and may contribute to the management of urban systems. It should be also important to mention that the dataset used in this work is available on our website.
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MasPA: A Machine Learning Application to Predict Risk of Mastitis in Cattle from AMS Sensor Data. AGRIENGINEERING 2021. [DOI: 10.3390/agriengineering3030037] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Mastitis is a common disease that prevails in cattle owing mainly to environmental pathogens; they are also the most expensive disease for cattle in dairy farms. Several prevention and treatment methods are available, although most of these options are quite expensive, especially for small farms. In this study, we utilized a dataset of 6600 cattle along with several of their sensory parameters (collected via inexpensive sensors) and their prevalence to mastitis. Supervised machine learning approaches were deployed to determine the most effective parameters that could be utilized to predict the risk of mastitis in cattle. To achieve this goal, 26 classification models were built, among which the best performing model (the highest accuracy in the shortest time) was selected. Hyper parameter tuning and K-fold cross validation were applied to further boost the top model’s performance, while at the same time avoiding bias and overfitting of the model. The model was then utilized to build a GUI application that could be used online as a web application. The application can predict the risk of mastitis in cattle from the inhale and exhale limits of their udder and their temperature with an accuracy of 98.1% and sensitivity and specificity of 99.4% and 98.8%, respectively. The full potential of this application can be utilized via the standalone version, which can be easily integrated into an automatic milking system to detect the risk of mastitis in real time.
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Genaev MA, Skolotneva ES, Gultyaeva EI, Orlova EA, Bechtold NP, Afonnikov DA. Image-Based Wheat Fungi Diseases Identification by Deep Learning. PLANTS 2021; 10:plants10081500. [PMID: 34451545 PMCID: PMC8399806 DOI: 10.3390/plants10081500] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/07/2021] [Accepted: 07/15/2021] [Indexed: 11/19/2022]
Abstract
Diseases of cereals caused by pathogenic fungi can significantly reduce crop yields. Many cultures are exposed to them. The disease is difficult to control on a large scale; thus, one of the relevant approaches is the crop field monitoring, which helps to identify the disease at an early stage and take measures to prevent its spread. One of the effective control methods is disease identification based on the analysis of digital images, with the possibility of obtaining them in field conditions, using mobile devices. In this work, we propose a method for the recognition of five fungal diseases of wheat shoots (leaf rust, stem rust, yellow rust, powdery mildew, and septoria), both separately and in case of multiple diseases, with the possibility of identifying the stage of plant development. A set of 2414 images of wheat fungi diseases (WFD2020) was generated, for which expert labeling was performed by the type of disease. More than 80% of the images in the dataset correspond to single disease labels (including seedlings), more than 12% are represented by healthy plants, and 6% of the images labeled are represented by multiple diseases. In the process of creating this set, a method was applied to reduce the degeneracy of the training data based on the image hashing algorithm. The disease-recognition algorithm is based on the convolutional neural network with the EfficientNet architecture. The best accuracy (0.942) was shown by a network with a training strategy based on augmentation and transfer of image styles. The recognition method was implemented as a bot on the Telegram platform, which allows users to assess plants by lesions in the field conditions.
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Affiliation(s)
- Mikhail A. Genaev
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia; (M.A.G.); (E.S.S.)
- Faculty of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Ekaterina S. Skolotneva
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia; (M.A.G.); (E.S.S.)
- Faculty of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Elena I. Gultyaeva
- All Russian Institute of Plant Protection, Pushkin, 196608 St. Petersburg, Russia;
| | - Elena A. Orlova
- Siberian Research Institute of Plant Production and Breeding, Branch of the Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630501 Krasnoobsk, Russia; (E.A.O.); (N.P.B.)
| | - Nina P. Bechtold
- Siberian Research Institute of Plant Production and Breeding, Branch of the Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630501 Krasnoobsk, Russia; (E.A.O.); (N.P.B.)
| | - Dmitry A. Afonnikov
- Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia; (M.A.G.); (E.S.S.)
- Faculty of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
- Correspondence: ; Tel.: +7-(383)-363-49-63
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Tomato Leaf Diseases Classification Based on Leaf Images: A Comparison between Classical Machine Learning and Deep Learning Methods. AGRIENGINEERING 2021. [DOI: 10.3390/agriengineering3030035] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Tomato production can be greatly reduced due to various diseases, such as bacterial spot, early blight, and leaf mold. Rapid recognition and timely treatment of diseases can minimize tomato production loss. Nowadays, a large number of researchers (including different institutes, laboratories, and universities) have developed and examined various traditional machine learning (ML) and deep learning (DL) algorithms for plant disease classification. However, through pass survey analysis, we found that there are no studies comparing the classification performance of ML and DL for the tomato disease classification problem. The performance and outcomes of different traditional ML and DL (a subset of ML) methods may vary depending on the datasets used and the tasks to be solved. This study generally aimed to identify the most suitable ML/DL models for the PlantVillage tomato dataset and the tomato disease classification problem. For machine learning algorithm implementation, we used different methods to extract disease features manually. In our study, we extracted a total of 52 texture features using local binary pattern (LBP) and gray level co-occurrence matrix (GLCM) methods and 105 color features using color moment and color histogram methods. Among all the feature extraction methods, the COLOR+GLCM method obtained the best result. By comparing the different methods, we found that the metrics (accuracy, precision, recall, F1 score) of the tested deep learning networks (AlexNet, VGG16, ResNet34, EfficientNet-b0, and MobileNetV2) were all better than those of the measured machine learning algorithms (support vector machine (SVM), k-nearest neighbor (kNN), and random forest (RF)). Furthermore, we found that, for our dataset and classification task, among the tested ML/DL algorithms, the ResNet34 network obtained the best results, with accuracy of 99.7%, precision of 99.6%, recall of 99.7%, and F1 score of 99.7%.
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Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images. ECOL INFORM 2021. [DOI: 10.1016/j.ecoinf.2021.101289] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Abstract
The world population is expected to reach over 9 billion by 2050, which will require an increase in agricultural and food production by 70% to fit the need, a serious challenge for the agri-food industry. Such requirement, in a context of resources scarcity, climate change, COVID-19 pandemic, and very harsh socioeconomic conjecture, is difficult to fulfill without the intervention of computational tools and forecasting strategy. Hereby, we report the importance of artificial intelligence and machine learning as a predictive multidisciplinary approach integration to improve the food and agriculture sector, yet with some limitations that should be considered by stakeholders.
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