A Deep Learning Approach to Classify the Potato Leaf Disease

Islam, Md. Ashraful; Sikder, Md. Hanif

doi:10.9734/jamcs/2022/v37i121735

Cited by 12 publications

(2 citation statements)

References 16 publications

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“…Figure 1. Potato Leaf Diseases [5] Late blight harms potato leaves, branches, and roots, appearing bubbled and dried. When drying out, the leaves turn brown or black.…”

Section: Introductionmentioning

confidence: 99%

Optimized Matrix Feature Analysis – Convolutional Neural Network (OMFA-CNN) Model for Potato Leaf Diseases Detection System

Rohilla,

Rai

2023

IJRITCC

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One of the most often grown crops is the potato. As a main food, potatoes are prioritised for cultivation worldwide. Because potatoes are such a rich source of vitamins and minerals, we can create a robust system for food security. However, a number of illnesses delay the growth of agriculture and harm potato output. Consequently, early disease identification can offer a better answer for effective crop production. In this research work aim is to classify and detect the potato leave (PL) diseases using OMFA-CNN deep learning model. An optimized matrix feature analysis-CNN deep learning model for PL disease detection is implemented. In the first phase, the PLs features are extracted from the potato leave images using K-means clustering image segmentation method. At the last phase, a new OMFA-CNN model are proposed using CNN to classify virus, and bacterial diseases of PLs, The PL disease dataset consists 2351 images gathered in real-time and from the Kaggle (PlantVillage) dataset. The implemented OMFA-CNN model attained 99.3 % precision and 99 % recall on potato disease detection. The implemented method is also compared with MASK RCNN,SVM and other models and attained significantly high precision and recall.

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“…Figure 1. Potato Leaf Diseases [5] Late blight harms potato leaves, branches, and roots, appearing bubbled and dried. When drying out, the leaves turn brown or black.…”

Section: Introductionmentioning

confidence: 99%

Optimized Matrix Feature Analysis – Convolutional Neural Network (OMFA-CNN) Model for Potato Leaf Diseases Detection System

Rohilla,

Rai

2023

IJRITCC

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“…Traditional machine learning methods, such as naïve Bayes [16][17][18], logistic regression [19], and support vector machine [20][21][22], are not suitable for recognizing edible fungi diseases in the fruit body period due to their shortcomings in high computational complexity, slow convergence rate, and difficulty in processing a large number of complex samples [23,24]. In recent years, deep learning methods have been widely studied in crop disease recognition [25][26][27][28][29][30]. For instance, Nurul Nabilah et al [31] took 974 pepper disease images collected by themselves and used traditional methods and deep learning methods for experimental comparison.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Edible Fungi Fruit Body Diseases Based on Improved ShuffleNetV2

et al. 2023

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Early recognition of fruit body diseases in edible fungi can effectively improve the quality and yield of edible fungi. This study proposes a method based on improved ShuffleNetV2 for edible fungi fruit body disease recognition. First, the ShuffleNetV2+SE model is constructed by deeply integrating the SE module with the ShuffleNetV2 network to make the network pay more attention to the target area and improve the model’s disease classification performance. Second, the network model is optimized and improved. To simplify the convolution operation, the 1 × 1 convolution layer after the 3 × 3 depth convolution layer is removed, and the ShuffleNetV2-Lite+SE model is established. The experimental results indicate that the accuracy, precision, recall, and Macro-F1 value of the ShuffleNetV2-Lite+SE model on the test set are, respectively, 96.19%, 96.43%, 96.07%, and 96.25%, which are 4.85, 4.89, 3.86, and 5.37 percent higher than those before improvement. Meanwhile, the number of model parameters and the average iteration time are 1.6 MB and 41 s, which is 0.2 MB higher and 4 s lower than that before the improvement, respectively. Compared with the common lightweight convolutional neural networks MobileNetV2, MobileNetV3, DenseNet, and EfficientNet, the proposed model achieves higher recognition accuracy, and its number of model parameters is significantly reduced. In addition, the average iteration time is reduced by 37.88%, 31.67%, 33.87%, and 42.25%, respectively. The ShuffleNetV2-Lite+SE model proposed in this paper has a good balance among performance, number of parameters, and real-time performance. It is suitable for deploying on resource-limited devices such as mobile terminals and helps in realization of real-time and accurate recognition of fruit body diseases of edible fungi.

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