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

Sujatha, R.; Chatterjee, Jyotir Moy; Jhanjhi, N. Z.; Brohi, Sarfraz Nawaz

doi:10.1016/j.micpro.2020.103615

Cited by 332 publications

(98 citation statements)

References 29 publications

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“…In a confusion matrix plot, the abscissa is the true label and the ordinate is the predicted label. The diagonal of the confusion matrix holds the data of the correctly classified instances, and the values above and below the diagonal are the incorrectly classified instances [49]. As shown in Figure 7, with the three machine learning methods (kNN, SVM, and RF) there were many cases (<13.0%) where the bacterial spot and early blight diseases were identified as leaf mold disease.…”

Section: Discussionmentioning

confidence: 99%

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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Section: Discussionmentioning

confidence: 99%

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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“…Recent developments in the field of data science and artificial intelligence have opened a lot of opportunities in developing new methods of diagnosis and detection of diseases by deploying sophisticated algorithms to these problems. Several attempts have been made in diseases affecting humans, including many other species of plants and animals [19,[27][28][29][30].…”

Section: Discussionmentioning

confidence: 99%

MasPA: A Machine Learning Application to Predict Risk of Mastitis in Cattle from AMS Sensor Data

Ghafoor

Sitkowska

2021

AgriEngineering

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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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“…Rapid improvements in the computational execution of GPUs, availability of larger datasets, and growth in the assisting software libraries gave rise to an expeditious spurt to experimentations based upon deep learning architectures. The potential of deep learning lies in its ability to handle high dimensional data, its ability to extract relevant features from the data, and its high performance [54][55][56].…”

Section: Discussionmentioning

confidence: 99%

A Review on Advances in Automated Plant Disease Detection

Bhagwat

Dandawate

2021

Int. j. eng. technol. innov.

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Plant diseases cause major yield and economic losses. To detect plant disease at early stages, selecting appropriate techniques is imperative as it affects the cost, diagnosis time, and accuracy. This research gives a comprehensive review of various plant disease detection methods based on the images used and processing algorithms applied. It systematically analyzes various traditional machine learning and deep learning algorithms used for processing visible and spectral range images, and comparatively evaluates the work done in literature in terms of datasets used, various image processing techniques employed, models utilized, and efficiency achieved. The study discusses the benefits and restrictions of each method along with the challenges to be addressed for rapid and accurate plant disease detection. Results show that for plant disease detection, deep learning outperforms traditional machine learning algorithms while visible range images are more widely used compared to spectral images.

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

Cited by 332 publications

References 29 publications

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

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

MasPA: A Machine Learning Application to Predict Risk of Mastitis in Cattle from AMS Sensor Data

A Review on Advances in Automated Plant Disease Detection

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