Image-Based Plant Disease Detection: A Comparison of Deep Learning and Classical Machine Learning Algorithms

Radovanovic, Drasko; Dukanovic, Slobodan

doi:10.1109/it48810.2020.9070664

Cited by 26 publications

(25 citation statements)

References 4 publications

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“…[1], [30], [31], [19], [34], [23], [43], [46], [48], [53], [57], [61], [75], [78], [81], [82], [83], [90], [97], [111], [115], [116] [58], [59], [60], [66], [67], [79], [102], [107], [108], [109], [117] Table 1 shows a summary of research articles analyzed in this research. The table portrays that machine learning techniques are being are applied to address problems in diverse fields.…”

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confidence: 99%

Research Methods in Machine Learning: A Content Analysis

Kamiri

Mariga

2021

IJCIT

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Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research. To achieve this the researchers analyzed a total of 100 articles published since 2019 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research.

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confidence: 99%

Research Methods in Machine Learning: A Content Analysis

Kamiri

Mariga

2021

IJCIT

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“…The image data were collected from a publicly available plant image database called "PlantVillage" [28]. It comprises images of plant leaves taken in a controlled environment [29]. The dataset includes over 50,000 images of 14 crops, such as tomato, grape, apple, corn, and soybean.…”

Section: Image Acquisitionmentioning

confidence: 99%

“…For the regularized process of data collection in the case of the PlanVillage dataset, which was judged to have the potential to introduce some inherent bias in the dataset [31], all images were segmented. Following Radovanovic et al (2020) [29], in order to extract protentional infected tomato leaf areas, we removed all pixels for which green channel value exceeded those of the red and blue channels. Disease symptoms, like color change, are obvious in some diseases (e.g., early blight, late blight, TYLCV).…”

Section: Image Acquisitionmentioning

confidence: 99%

“…The color histogram can simply describe the global distribution of colors in an image; that is, the proportion of different colors in the image that are not affected by image rotation and translation changes. We calculated a color histogram with 32 buckets per channel and used the pixel count per bucket as the features, which, multiplied by 3 channels, gave us 96 features [29].…”

Section: Color Features Extractionmentioning

confidence: 99%

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Tomato Leaf Diseases Classification Based on Leaf Images: A Comparison between Classical Machine Learning and Deep Learning Methods

Tan

Jiang

2021

AgriEngineering

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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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“…e work was based on the "2018 AI Challenger Global Challenge" dataset. For other contributions, the reader is referred to [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Using Multioutput Learning to Diagnose Plant Disease and Stress Severity

Fenu

2021

Complexity

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Early diagnosis of leaf diseases is a fundamental tool in precision agriculture, thanks to its high correlation with food safety and environmental sustainability. It is proven that plant diseases are responsible for serious economic losses every year. The aim of this work is to study an efficient network capable of assisting farmers in recognizing pear leaf symptoms and providing targeted information for rational use of pesticides. The proposed model consists of a multioutput system based on convolutional neural networks. The deep learning approach considers five pretrained CNN architectures, namely, VGG-16, VGG-19, ResNet50, InceptionV3, MobileNetV2, and EfficientNetB0, as feature extractors to classify three diseases and six severity levels. Computational experiments are conducted to evaluate the model on the DiaMOS Plant dataset, a self-collected dataset in the field. The results obtained confirm the robustness of the proposed model in automatically extracting the discriminating features of diseased leaves by adopting the multitasking learning paradigm.

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Image-Based Plant Disease Detection: A Comparison of Deep Learning and Classical Machine Learning Algorithms

Cited by 26 publications

References 4 publications

Research Methods in Machine Learning: A Content Analysis

Research Methods in Machine Learning: A Content Analysis

Tomato Leaf Diseases Classification Based on Leaf Images: A Comparison between Classical Machine Learning and Deep Learning Methods

Using Multioutput Learning to Diagnose Plant Disease and Stress Severity

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