Design of Efficient Methods for the Detection of Tomato Leaf Disease Utilizing Proposed Ensemble CNN Model

Ulutaş, Hasan; Aslantaş, Veysel

doi:10.3390/electronics12040827

Cited by 35 publications

(14 citation statements)

References 41 publications

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“…The curves (Figs. 13 and 14) demonstrate the diagnostic ability of various classifiers on tomato health status at the discriminating threshold [31]. A perfect classifier should have a true positive rate of 1 and a false positive rate of 0.…”

Section: Performance Comparison Of Classification Algorithms On Embed...mentioning

confidence: 97%

A Fast and Accurate Method for Classifying Tomato Plant Health Status Using Machine Learning and Image Processing

Ulutaş

Aslantaş

2023

ELEKTRON ELEKTROTECH

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Agriculture is crucial to economic growth and development, and maintaining high-quality, disease-free plants is crucial to its success. Early detection of plant diseases, which can be caused by environmental factors, fungi, bacteria, and viruses, is essential to implement appropriate treatments. Tomatoes, which are one of the most vital food crops, are susceptible to diseases that can result in significant economic losses in agriculture. This study introduces a method to evaluate the health of tomato leaf using image processing techniques and machine learning algorithms. A dataset of 1,778 images of healthy and infected tomato leaves was collected from tomato planting areas in the Turkish provinces of Samsun and Mersin. Sixteen advanced machine learning algorithms were used for classification, and the optimal hyperparameters for each algorithm were determined using a grid search approach. The classifiers were executed on Jetson Nano and TX2 embedded systems. The experimental results indicate that the Random Forest classifier outperformed other algorithms, achieving approximately 99 % accuracy in detecting and classifying the health status of tomato leaves. The proposed system enables faster and more accurate detection, allowing farmers to classify plants as infected or healthy, ultimately improving decision-making on treatment and pest management strategies.

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Section: Performance Comparison Of Classification Algorithms On Embed...mentioning

confidence: 97%

A Fast and Accurate Method for Classifying Tomato Plant Health Status Using Machine Learning and Image Processing

Ulutaş

Aslantaş

2023

ELEKTRON ELEKTROTECH

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“…The majority of previous approaches to disease detection relied on artificial recognition techniques, such as (1) making predictions about the sorts of illnesses based on farmers' years of crop cultivation experience or (2) consulting agricultural information books. To (2) research available options, one may collect images of illness specimens and do an online search. Third, get in touch with experts to help you explore the signs of sickness.…”

Section: Figure 1 Tomato Leaf Samples With Various Diseasementioning

confidence: 99%

“…Karthik et al (2020), for example, created an attention-embedded residual CNN for disease identification in tomato leaves and attained an overall accuracy of 98% in a 5-fold cross-validation [1]. The remarkable accuracy of 99.60% has been achieved by Ulutaş and Aslantaş's (2023) ensemble CNN model for the identification of tomato leaf diseases [2]. Gehlot and Saini (2020) examined many CNN architectures for tomato leaf disease categorization and found that DenseNet-121, VGG16, and ResNet-101 performed well with comparable accuracy, precision, recall, and F1-score [3].…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Enhanced Resnet-50 Based Deep Learning Classifier for Tomato Leaf Disease Detection and Classification

Akash Saxena

2024

jes

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This research presents a comprehensive assessment of a sophisticated approach for the accurate detection and classification of various tomato leaf diseases using an improved ResNet-50 based deep learning classifier. The alarming increase in plant diseases has prompted the need for advanced technologies that can promptly identify and categorize these ailments to ensure agricultural sustainability. The proposed method harnesses the potential of deep convolutional neural networks (CNNs) and builds upon the ResNet-50 architecture, renowned for its depth and performance. However, the approach's innovation lies in the incorporation of enhancements such as advanced data augmentation techniques and transfer learning from a vast plant disease dataset. These modifications empower the model to learn intricate disease-specific features and patterns, leading to heightened accuracy and robustness. The evaluation of the approach is conducted on an extensive dataset encompassing high-resolution images of tomato leaves affected by a range of diseases. The dataset is meticulously preprocessed to ensure consistency and quality, followed by a rigorous training regimen that fine-tunes the improved ResNet-50 model.The results underscore the efficacy of the proposed method in accurate disease detection and classification. The improved ResNet-50 based classifier demonstrates exceptional performance, achieving an impressive accuracy exceeding 95%. Notably, the model showcases resilience against variations in lighting conditions, angles, and disease severity, highlighting its applicability in real-world agricultural scenarios. The implications of this research are significant, offering an efficient and reliable tool for early detection and classification of tomato leaf diseases. The integration of advanced deep learning techniques and the enhancements introduced in this work signify a substantial advancement in precision agriculture and sustainable crop management practices. As future work, this approach can be extended to address diseases in other plant species, contributing to a versatile framework that can safeguard global food production and alleviate the challenges posed by plant diseases.

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“…The ResNet152V2 model was selected due to its prevalence in image classification literature in a variety of different fields from disease ratings in agriculture (Kanchanadevi & Sandhia, 2023;Nigam et al, 2023) to medical research (Sulaiman et al, 2023). The final model was the pre-trained EfficientNetV2L model (Tan & Le, 2021), which was selected due to its recent use in plant disease detection (Shovon et al, 2023;Ulutaş & Aslantaş, 2023). Mean squared error (MSE) was used as the loss function for each model, which evaluates differences between values predicted by the model and their true value.…”

Section: Deep Learningmentioning

confidence: 99%

Phenotyping and predicting wheat spike characteristics using image analysis and machine learning

Hammers,

Winn,

Ben‐Hur

et al. 2023

The Plant Phenome Journal

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Improvements in trait phenotyping are needed to increase the quantity and quality of data available for genetic improvement of crops. In this study, we used moderate throughput image analysis and machine learning as a pipeline for phenotyping a key wheat spike characteristic: spikelet number per spike. A population of 594 soft red winter wheat inbred lines was evaluated in the field for 2 years and images of wheat spikes were taken and used to train deep‐learning algorithms to predict spikelet number. A total of 12,717 images were used to train, test, and validate a basic regression convolutional neural network (CNN), a visual geometry group application regression model, VGG16, the ResNet152V2 model, and the EfficientNetV2L model. The EfficientNetV2L model was the most accurate, having the lowest mean absolute error, second lowest root mean square error, and highest coefficient of determination (mean absolute error [MAE] = 0.60, root mean square error [RMSE] = 0.79, and R2 = 0.90). The ResNet152V2 model was slightly less accurate with a slightly better fit (MAE = 0.61,m RMSE = 0.78, and R2 = 0.87), followed by the basic CNN (MAE = 0.75, RMSE = 1.00, and R2 = 0.74) and finally by the VGG16 (MAE = 1.51, RMSE = 1.29, and R2 = 0.076). With an average error of just above one half of a spikelet, utilizing image analysis and machine learning counting methods could be used for multiple breeding applications, including direct selection of spikelet number, to provide data to identify quantitative trait loci, or for training whole genome selection models.

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Design of Efficient Methods for the Detection of Tomato Leaf Disease Utilizing Proposed Ensemble CNN Model

Cited by 35 publications

References 41 publications

A Fast and Accurate Method for Classifying Tomato Plant Health Status Using Machine Learning and Image Processing

A Fast and Accurate Method for Classifying Tomato Plant Health Status Using Machine Learning and Image Processing

Evaluation of Enhanced Resnet-50 Based Deep Learning Classifier for Tomato Leaf Disease Detection and Classification

Phenotyping and predicting wheat spike characteristics using image analysis and machine learning

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