Hybrid Deep Model for Automated Detection of Tomato Leaf Diseases

Abstract

Tomatoes are preferred by farmers because of their high productivity. This fruit has a fibrous structure and contains plenty of vitamins. Tomato diseases are generally observed on stem, fruit, and leaves. Early diagnosis of the disease in plants is of vital importance for the plant. This is very important for farmers who expect economic gain from that plant. Because if the disease is not treated early, these tomatoes should be destroyed. For these reasons, systems to diagnose the disease early are very important. In this study, a tomato leaf diseases classification model developed with deep learning methods, which is one of the most popular artificial intelligence techniques, is proposed in order to eliminate the possibility of the human eye being mistaken. In this study, 6 different Convolutional Neural Network (CNN) architectures were used. In the first stage of this study, which consists of two stages, the classification process was carried out with the Alexnet, Googlenet, Shufflenet, Efficientb0, Resnet50, and Inceptionv3 architectures that were previously trained. In the second stage, feature maps of tomato leaf images in the dataset were obtained using the six pre-trained deep learning architectures. In the hybrid model proposed in this study, the feature maps extracted using the best two of the six deep learning models are concatenated. Then, the Neighborhood Component Analysis (NCA) method was applied to the extracted features in order to speed up the system, unnecessary features were removed and optimized. The optimized feature map is classified by traditional intelligent classification models. As a result of experimental studies, the average accuracy rate of the proposed model is 99.50 percent.

Multi-classification of disease induced in plant leaf using chronological Flamingo search optimization with transfer learning

“…However, it did not adopt an ensemble network for detecting disease in plant leaves. Bayram, Bingol & Alatas (2022) established an artificial intelligence technique for automatically detecting tomato leaf disease. Here, for the classification Inceptionv3, Resnet50, Efficientb0, Shufflenet, Googlenet, and Alexnet models were used.…”

“…There exist several issues that arise in farming because of several environmental aspects and this disease in plant leaves is termed to be a strong aspect that causes deficiency in the quality of agricultural products. The aim is to alleviate this problem with machine learning (ML) models ( Bayram, Bingol & Alatas, 2022 ; Bingöl, 2022a , 2022b ). Several ML and segmentation models are devised for the categorization and discovery of diseases in plants through leaf images ( Subramanian et al, 2022 ; Krishnamoorthy et al, 2021 ; Sathishkumar et al, 2020 ).…”

Optimizing Hyperparameters for Thai Cuisine Recognition via Convolutional Neural Networks

“…Pretrained versions of these networks on comprehensive public datasets like ImageNet [10] can be directly applied to tasks with labels overlapping with the original dataset or indirectly via transfer learning when labels differ. CNNs have been employed for image classification and object detection across various domains, including medical image classification [11,12], plant disease recognition [13][14][15], face recognition [16], and document classification [17,18].…”

Evaluation of Machine Learning Models for Plant Disease Classification Using Modified GLCM and Wavelet Based Statistical Features

“…Sabrol and Satish [7] have used a classification tree for the purpose of tomato plant disease classification based on five types of tomato disease. A review on different ML classifiers such as SVM, KNN, RF, Naive Bayes (NB), Fuzzy classifier and artificial neural network is done in the plant disease research [8][9][10][11][12][13][14]. Bauer et al [15] have used high-resolution multi-spectral images for the classification of diseases in sugar beet leaves based on conditional random fields.…”