A Deep Learning-Based Approach in Classification and Validation of Tomato Leaf Disease

Wagle, Shivali Amit

doi:10.18280/ts.380317

Cited by 25 publications

(11 citation statements)

References 38 publications

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“…e information obtained in the process of learning is very helpful to the interpretation of data such as words, images, and sounds [1,2]. Its ultimate goal is to enable machines, like humans, to analyze and learn, and recognize data such as words, images, and sounds.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of the Historical Process of Cultural Confidence in Ideological and Political Education Based on Deep Learning

Wang

2022

Mathematical Problems in Engineering

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Ideological and political education can improve people’s ideological and moral character and psychological quality, improve social governance, and promote the harmonious development of society. However, the current data of ideological and political education have the trend of mass and diversification. In order to improve the effect of ideological and political education, this paper proposes an analysis method of the historical process of cultural self-confidence of ideological and political education based on in-depth learning. By preprocessing the ideological and political education data to obtain text keywords, the principal component analysis method is improved to reduce the dimension of the ideological and political education data, and the vector space model is used to complete the text balance processing of the reduced dimension data. The data sensitivity after dimensionality reduction is analyzed by data training, the historical text data mining model is constructed to extract data features, and the bidirectional recurrent neural network is used to complete data extraction. The semantic features of the extracted data are obtained by using the forward and reverse feature generation methods, and the historical process of the cultural confidence of ideological and political education is analyzed by using the two-way generation method. The experimental results show that the accuracy rate of the historical process analysis method of cultural self-confidence in ideological and political education based on deep learning is 95%, the recall rate is 94%, and the degree of collaborative performance is good.

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Section: Introductionmentioning

confidence: 99%

An Analysis of the Historical Process of Cultural Confidence in Ideological and Political Education Based on Deep Learning

Wang

2022

Mathematical Problems in Engineering

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“…To further refine the processing of these two feature maps, we use BConvLSTMin BCDU-Net. First, χd is sent to an upconvolutional layer (see Figure 3), where a 2 x 2 convolution and an upsampling function are used to decrease the number of feature channels while simultaneously expanding the size of each feature map [40]. After splitting the input into forward and backward paths using two ConvLSTMs, BConvLSTM makes a judgement for the current input by taking into account the dependencies in both directions of the data.…”

Section: Figure 1 Convlstm U-net With Bidirectional Connectivity and ...mentioning

confidence: 99%

Plant Disease Classification Based on ConvLSTM U-Net with Fully Connected Convolutional Layers

Alharbi¹,

Rajagopal²,

Rajendran³

et al. 2023

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Plants are susceptible to a variety of illnesses throughout their growth stages. One of the trickiest issues in agriculture is the early diagnosis of plant diseases. The entire output may be negatively impacted by infections if they are not discovered early on, which would lower farmers' profitability. Numerous researchers have proposed numerous cutting-edge solutions based on Deep Learning and Machine Learning techniques to address this issue. However, the majority of these systems either has poor classification accuracy rates or utilizes millions of training parameters. In this research, a novel model using ConvLSTM U Net-based automatic detection of plant disease is proposed. To the best of our knowledge, no state-of-the-art systems described in the literature have a hybrid system based on CAE and CNN to automatically identify plant diseases. The proposed model employed in this study is to identify the presence of Bacterial Spot disease in medicinal plants using the image of their leaves, but it may be extended to identifying any plant disease. The work conducted for this research employ a dataset that is readily accessible to get images of medicinal plant leaves. In comparison to previous methods described in the literature, the proposed ConLSTM U-Net model requires for less training parameters. As a consequence of this, the amount of time necessary to train the model for automatic plant disease detection and the amount of time required to diagnose the disease in plants using the trained model are both significantly decreased.

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“…Other measures such as accuracy, precision, recall, and F1-score were included in the evaluation. The CNN models used in the study of Wagle [19] were AlexNet, AlexNetOWTBn, GoogLeNet, Overfeat and VGG16. A comparison was made between all five models to determine which of the following would produce the best result.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of a Web and Mobile Applications-Based Cassava Disease Classification Interface Using Convolutional Neural Network

Okokpujie¹,

Okokpujie²,

Ayomikun³

et al. 2023

MMEP

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Cassava is one of the six food items identified as a critical food product for Africa, owing to its importance to African farmers' lives and ability to alter African economies. However, Cassava plant diseases have affected the yield of farmers significantly which has led to a decline in the agricultural production of cassava. Therefore, the aim of this research work is to develop a web and mobile applications-based system that would be able to detect cassava diseases based on its leaf images. To achieve this aim, pre-trained Convolutional Neural Network (CNN) models were selected using their previous performance and the application of transfer learning technique, new models were developed to classify cassava diseases based on the dataset curated and pre-processed. The best three models were selected: MobileNetV2, VGG16 and ResNet50. After training, the accuracy for each model was: 98%, 92% and 75% for MobileNetV2, VGG16 and ResNet50 respectively. Following evaluation of performance, the model with the best accuracy (MobileNetV2) was deployed using a web application interface. After deploying as a web and mobile apps interface, it was further tested to see how it would perform on the field. This research work was found capable of aiding farmers in being able to timely detect the type of disease affecting their cassava plants and the correct treatment to utilize; this also contribute towards Sustainable development goals (SDG) 2 and 12.

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A Deep Learning-Based Approach in Classification and Validation of Tomato Leaf Disease

Cited by 25 publications

References 38 publications

An Analysis of the Historical Process of Cultural Confidence in Ideological and Political Education Based on Deep Learning

An Analysis of the Historical Process of Cultural Confidence in Ideological and Political Education Based on Deep Learning

Plant Disease Classification Based on ConvLSTM U-Net with Fully Connected Convolutional Layers

Development of a Web and Mobile Applications-Based Cassava Disease Classification Interface Using Convolutional Neural Network

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