Hybrid SVM-LR Classifier for Powdery Mildew Disease Prediction in Tomato Plant

Bhatia, Anshul; Chug, Anuradha; Singh, Amit Prakash

doi:10.1109/spin48934.2020.9071202

Cited by 30 publications

(14 citation statements)

References 13 publications

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“…Ref. [15] outlined the combined application of the SVM and logistic regression classifier together on real-time data of the Tomato Powdery Mildew Disease dataset. Before applying the hybrid SVM-LR classifier, it first balances the dataset using random oversampling and, afterward, divides the dataset into training (70%) and test (30%) datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

A Systematic Analysis of Machine Learning and Deep Learning Based Approaches for Plant Leaf Disease Classification: A Review

et al. 2022

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Crops’ production and quality of yields are heavily affected by crop diseases which cause adverse impacts on food security as well as economic losses. In India, agriculture is a prime source of income in most rural areas. Hence, there is an intense need to employ novel and accurate computer vision-based techniques for automatic crop disease detection and their classification so that prophylactic actions can be recommended in a timely manner. In literature, numerous computer vision-based techniques by utilizing divergent combinations of machine learning, deep learning, CNN, and various image-processing techniques along with their associated merits and demerits have already been discussed. In this study, we systematically reviewed recent research studies undertaken by a variety of scholars and researchers of fungal and bacterial plant disease detection and classification and summarized them based on vital parameters like type of crop utilized, deep learning/machine learning architecture used, dataset utilized for experiments, performance matrices, types of disease detected and classified, and highest accuracy achieved by the model. As per the analysis carried out, in the category of machine learning-based approaches, 70% of studies utilized real-field plant leaf images and 30% utilized laboratory condition plant leaf images for disease classification while in the case of deep learning-based approaches, 55% studied employed laboratory-conditioned images from the PlantVillage dataset, 25% utilized real-field images, and 20% utilized open image datasets. The average accuracy attained with deep learning-based approaches is quite higher at 98.8% as compared to machine learning-based approaches at 92.2%. In the case of deep learning-based methods, we also analyzed the performances of pretrained and training from scratch models that have been utilized in various studies for plant leaf disease classification. Pretrained models perform better with 99.64% classification accuracy compared to training from scratch models which achieved 98.64% average accuracy. We also highlighted some major issues encountered in the computer vision-based disease detection and classification approach used in literature and provided recommendations that will help and guide researchers to explore new dimensions in crop disease recognition.

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Section: Literature Reviewmentioning

confidence: 99%

A Systematic Analysis of Machine Learning and Deep Learning Based Approaches for Plant Leaf Disease Classification: A Review

et al. 2022

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“…Many scientific papers, due to the limited size of the data [3,20], have addressed the problem of unbalanced data. To increase the number of examples in the training set and avoid overfitting situations that would have penalized performance and limited the ability of the model to generalize, the following methods were applied: Synthetic Minority Over-Sampling Technique (SMOTE) [3,14], Random Oversampling (RO) [56], Random Undersampling (RUS), and Importance Sampling (IMPS) [57].…”

Section: Pre-processingmentioning

confidence: 99%

“…Bhatia et al [56] developed a hybrid of the Support Vector Machine (SVM) and Logistic Regression (LR) algorithms to predict powdery mildew disease in tomato plant. The SVM was used to minimize the noise in data with the help of the Adaptive Sampling-Based Noise Reduction (ANR) method.…”

Section: Forecast Models Based On Weather Datamentioning

confidence: 99%

“…In this paper, the SVM was more accurate in distinguishing the low and high severity levels, while ANN classified the medium severity level better. In the prediction of powdery mildew disease in tomato plant [56], the SVM achieved an accuracy of 89.31%, and Linear Regression recorded an accuracy of 87.02%. However, both models were found to be efficient when using a hybrid approach involving two steps: the first was noise removal by ANR and SVM techniques, and the second was the training of the LR model followed by 10-fold cross validation of the modified dataset.…”

Section: Overall Performance and Comparisonmentioning

confidence: 99%

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Forecasting Plant and Crop Disease: An Explorative Study on Current Algorithms

Fenu

Malloci

2021

BDCC

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Every year, plant diseases cause a significant loss of valuable food crops around the world. The plant and crop disease management practice implemented in order to mitigate damages have changed considerably. Today, through the application of new information and communication technologies, it is possible to predict the onset or change in the severity of diseases using modern big data analysis techniques. In this paper, we present an analysis and classification of research studies conducted over the past decade that forecast the onset of disease at a pre-symptomatic stage (i.e., symptoms not visible to the naked eye) or at an early stage. We examine the specific approaches and methods adopted, pre-processing techniques and data used, performance metrics, and expected results, highlighting the issues encountered. The results of the study reveal that this practice is still in its infancy and that many barriers need to be overcome.

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“…(Jones and Thomson, 1987). Powdery mildew is also one of the dangerous diseases caused by a fungal pathogen 'LeveillulaTaurica' found in all major growing parts of a tomato plant (Bhatia et al, 2020b). It can harm the tomato plant's quality and may reduce its yield by up to 40% (Haroun, 2002).…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis of machine learning techniques for predicting powdery mildew disease in tomato plants

Chug¹,

Bhatia²,

Singh³

2021

IJIEI

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Powdery mildew is a dangerous disease that reduces the quality and the yield of tomato fruit rapidly. Its early prediction is a prior requirement for obtaining good quality fruit. Therefore, in this study, the best classifier amongst various classifiers has been discovered using different machine learning algorithms. This classifier can precisely classify whether the meteorological conditions of a particular day are conducive to the development of powdery mildew disease or not. Tomato powdery mildew disease dataset has been tested using various performance measures and the results computed for all the classifiers are promising. Friedman test has been used to rank multiple classifiers and post hoc analysis has also been done using the Nemenyi test. It has been observed in comparison that 62.05% of the total pairs of classifiers perform significantly different from each other, and medium Gaussian support vector machine (MGSVM) is the best classifier with 94.74% accuracy.

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Hybrid SVM-LR Classifier for Powdery Mildew Disease Prediction in Tomato Plant

Cited by 30 publications

References 13 publications

A Systematic Analysis of Machine Learning and Deep Learning Based Approaches for Plant Leaf Disease Classification: A Review

A Systematic Analysis of Machine Learning and Deep Learning Based Approaches for Plant Leaf Disease Classification: A Review

Forecasting Plant and Crop Disease: An Explorative Study on Current Algorithms

Statistical analysis of machine learning techniques for predicting powdery mildew disease in tomato plants

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