Development of Concise Convolutional Neural Network for Tomato Plant Disease Classification Based on Leaf Images

Sembiring, Arnes; Away, Yuwaldi; Arnia, Fitri; Muharar, Rusdha

doi:10.1088/1742-6596/1845/1/012009

Cited by 30 publications

(14 citation statements)

References 15 publications

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“…A lightweight CNN framework comprising four layers was used to compute the deep features of suspected samples and classify them into ten different classes. This work [44] is computationally efficient, however, exhibits lower performance for real-world scenarios. In [45], Turkoglu et al proposed an ensemble technique in which several DL-based models namely AlexNet, GoogleNet, DenseNet201, ResNet50, and ResNet101 frameworks were used to compute the deep keypoints of several plants.…”

Section: Related Workmentioning

confidence: 99%

“…The approach in [43] provides a low-cost solution for tomato crop disease classification, however, unable to show robust performance for noisy samples. Sembiring et al [44] proposed a solution to classify the tomato plant leaf diseases. A lightweight CNN framework comprising four layers was used to compute the deep features of suspected samples and classify them into ten different classes.…”

Section: Related Workmentioning

confidence: 99%

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A novel deep learning method for detection and classification of plant diseases

Albattah

Nawaz

Javed

et al. 2021

Complex Intell. Syst.

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The agricultural production rate plays a pivotal role in the economic development of a country. However, plant diseases are the most significant impediment to the production and quality of food. The identification of plant diseases at an early stage is crucial for global health and wellbeing. The traditional diagnosis process involves visual assessment of an individual plant by a pathologist through on-site visits. However, manual examination for crop diseases is restricted because of less accuracy and the small accessibility of human resources. To tackle such issues, there is a demand to design automated approaches capable of efficiently detecting and categorizing numerous plant diseases. Precise identification and classification of plant diseases is a tedious job due because of the occurrence of low-intensity information in the image background and foreground, the huge color resemblance in the healthy and diseased plant areas, the occurrence of noise in the samples, and changes in the position, chrominance, structure, and size of plant leaves. To tackle the above-mentioned problems, we have introduced a robust plant disease classification system by introducing a Custom CenterNet framework with DenseNet-77 as a base network. The presented method follows three steps. In the first step, annotations are developed to get the region of interest. Secondly, an improved CenterNet is introduced in which DenseNet-77 is proposed for deep keypoints extraction. Finally, the one-stage detector CenterNet is used to detect and categorize several plant diseases. To conduct the performance analysis, we have used the PlantVillage Kaggle database, which is the standard dataset for plant diseases and challenges in terms of intensity variations, color changes, and differences found in the shapes and sizes of leaves. Both the qualitative and quantitative analysis confirms that the presented method is more proficient and reliable to identify and classify plant diseases than other latest approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A novel deep learning method for detection and classification of plant diseases

Albattah

Nawaz

Javed

et al. 2021

Complex Intell. Syst.

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“…Sembiring et al [32] utilized Convolution Neural Network basic model to build a simple and effective CNN architecture for recognizing infections in tomato leaves. The proposed model was trained using 9 diseased classes and 1 healthy class of tomato leaves.…”

Section: Literature Reviewmentioning

confidence: 99%

Early-Stage Brown Spot Disease Recognition in Paddy Using Image Processing and Deep Learning Techniques

Upadhyay¹,

Kumar²

2021

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India is an agricultural country. Paddy is the main crop here on which the livelihood of millions of people depends. Brown spot disease caused by fungus is the most predominant infection that appears as oval and round lesions on the paddy leaves. If not addressed on time, it might result in serious crop loss. Pesticide use for plant disease treatment should be limited because it raises costs and pollutes the environment. Usage of pesticide and crop loss both can be minimized if we recognize the disease in a timely manner. Our aim is to develop a simple, fast, and effective deep learning structure for early-stage brown spot disease detection by utilizing infection severity estimation using image processing techniques. The suggested approach consists of two phases. In the first phase, the brown spot infected leaf image dataset is partitioned into two sets named as early-stage brown spot and developed stage brown spot. This partition is done on the basis of calculated infection severity. Infection severity is computed as a ratio of infected pixel count to total leaf pixel count. Total leaf pixel counts are determined by segmenting the leaf region from the background image using Otsu's thresholding technique. Infected pixel counts are determined by segmenting infected regions from leaf regions using Triangle thresholding segmentation. In the second phase, a fully connected CNN architecture is built for automatic feature extraction and classification. The CNN-based classification model is trained and validated using early-stage brown spot, developed stage brown spot, and healthy leaves images of rice plants. Early-stage brown spot and developed stage brown spot images used in training and validation are the same images that are obtained in phase 1. The experimental analysis shows that the proposed fully connected CNN-based early-stage brown spot disease recognition model is an effective approach. The classification accuracy of the suggested model is found to be 99.20%. The result of the suggested method is compared with those existing CNN-based disease recognition and classification methods that have used leaf images to recognize the diseases. It is observed that the performance of our method is significantly better than compared methods.

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“…And we have used CNN model architecture for this work because it increases the depth of the network to achieve high accuracy, training efficiency, generality and it is computationally efficient in the utilization of resources and the number of parameters. It's a local connection, weight sharing and pooling operation which make it potential to decrease complexity of the network efficiently (9,10) . The purpose of this study is to apply the state of art convolutional neural network architecture for the identification of visible tomato leaf diseases and pest symptoms on various parts of the plant leaves.…”

Section: Introductionmentioning

confidence: 99%

Image-based Tomato Disease Identification Using Convolutional Neural Network

Gardie¹,

Azezew²,

Asemie³

2021

IJST

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Objectives: Agriculture is the main food source and farmers are challenging a great production loss annually due to plant leaf disease. Early identification of tomato plant diseases help farmers to take preventive measure to reduce production loss. As a result, to recognize tomato plant leaf diseases in its early stage, a deep learning approach is discussed. Methods: For tomato disease identification and classification a convolutional neural network model is used in this study. CNN is capable for fine-grained disease identification as a technique which avoids feature engineering and threshold segmentation through automatic feature extraction. Findings: In this experiment, we have used 22,930 leaf image dataset are taken from plant village dataset, some are collected from Awash Melkasa tomato cultivation area in various seasons. Image processing is conducted along with pixel with operations it enhance the image data followed with feature extraction of patterns of collected leaves to detect the leaf diseases. The extracted patterns are fit into the neural network model with 100 epochs, 80/20 splitting ratio, and 0.001 learning rate. Hence the tomato disease network model achieves an overall 98.3% accuracy performance. Novelty: In order to detect tomato leave disease, we performed image processing with pixel-wise operation to enhance the leaf images that can be followed by feature extraction to classify patterns. We extend, and adopt neural network using local images collected under challenging environment datasets and optimization is performed in Adam optimizer with categorical entropy as loss function.

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Development of Concise Convolutional Neural Network for Tomato Plant Disease Classification Based on Leaf Images

Cited by 30 publications

References 15 publications

A novel deep learning method for detection and classification of plant diseases

A novel deep learning method for detection and classification of plant diseases

Early-Stage Brown Spot Disease Recognition in Paddy Using Image Processing and Deep Learning Techniques

Image-based Tomato Disease Identification Using Convolutional Neural Network

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