Crossover-based wind-driven optimized convolutional neural network model for tomato leaf disease classification

Indu, V.; Suja, S.

doi:10.1007/s41348-021-00528-w

Cited by 18 publications

(6 citation statements)

References 35 publications

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“…Indu and Priyadharsini 5 proposed a CNN powered by crossover-based wind-driven optimization (CROWDO) algorithm for identification of diseases at an earlier stage in tomato plants. The CROWDO algorithm was used to improve the accuracy of pretrained CNN models and was capable of identifying four tomato leaf diseases (leaf mold, early blight, target spot, and tomato yellow leaf curl).…”

Section: Related Workmentioning

confidence: 99%

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Optimized deep feature selective concatenation of recurrent encoder model and pretrained convolutional neural network model for leaf disease identification

Indu

Priyadharsini

2023

J. Electron. Imag.

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.Automated plant disease identification is one of the significant tasks in the agricultural sector. With the advent of deep-learning models, classification and segmentation have been completely automated using a convolutional neural network (CNN). This paper proposes the UV-Net model, with a recurrent encoder and an up-sampling decoder for multiclass image segmentation and generation of masks. The improvement of the segmentation model is the stacking of conv2D layers before the down convolution operation, concatenation of features extracted from the convolution process, up-sampling along with transpose convolution in the expansion path, and exponential linear unit (ELU) activation. This model is combined with an optimized, pretrained, and self-trained CNN for generating the classes of diseased leaves. The classification is further enhanced by concatenating the high-level features extracted from the pretrained CNN model along with the low-level features from the encoder part of the UV-Net model. The features are concatenated using the hybrid wind-driven particle swarm optimized (WDPSO) deep feature selective concatenation mechanism (DFSCM). The proposed model is trained and the performance is tested on multiclass diseases of tomato plants like bacterial spot, early blight, leaf mold, and target spot. The proposed architecture is also compared with some of the well-known CNN models such as FCN, FPN, U-Net, V-Net, and Mask R-CNN. The model proves to be a robust system for detecting diseases from images of varying sizes, shapes, and lighting. The proposed model achieved an average training accuracy of 98.20% with a dice coefficient of 0.8938 and an Intersection over union coefficient (IoU) of 0.7458. The model also generates accurate masks from which the intensity and pattern of diseases could be identified for earlier prevention and treatment of diseases.

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Section: Related Workmentioning

confidence: 99%

“…Transfer learning refers to the process of using pretrained networks on tiny datasets. Here, we have used our previous work, CROWDO optimized AlexNet 5 which was trained on the Plant Village dataset for extracting the high-level features. Visual tasks like agricultural monitoring also need localization of pixels 6 .…”

Section: Introductionmentioning

confidence: 99%

Optimized deep feature selective concatenation of recurrent encoder model and pretrained convolutional neural network model for leaf disease identification

Indu

Priyadharsini

2023

J. Electron. Imag.

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“…Many deep learning-based entity recognition methods have been presented to identify crop diseases, pests, drug names, and other nouns related to disease-pests. It is a basic part of agricultural knowledge graph, question and answer, and will be implemented as a web application to provide the public with solutions for the prevention and control of crop pest-diseases (Nandhini and Ashokkumar, 2021;Thanammal Indu and Suja Priyadharsini, 2022). The named entities of crop pest-diseases have the common phenomena of complex word formation, word combination, and entity embedding.…”

Section: Related Workmentioning

confidence: 99%

Classification of crop disease-pest questions based on BERT-BiGRU-CapsNet with attention pooling

Zhang,

Wang

2023

Front. Plant Sci.

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Crop disease-pest question classification is an essential part of pest knowledge intelligent question answering system. A crop disease-pest question classification method is proposed on the basis of bidirectional encoder representations from transformers (BERT), bidirectional gated unit (BiGRU), capsule network (CapsNet), and BERT-BiGRU-CapsNet with attention pooling (BBGCAP). In BBGCAP, the unstructured text data are preprocessed vectorically using BERT, BiGRU is used to extract the deep features of the text, attention pooling is used to assign the corresponding weights to the extracted deep information, and CapsNet is used to route the right alternative. BBGCAP is a synthetic model by integrating the advantages of BERT, BiGRU, CapsNet, and attention pooling. The experimental results on the cucumber-pest question database show that the proposed method is superior to the methods based on traditional template matching, support vector machines (SVM), and convolutional neural network–long short-term memory (LSTM), and the accuracy rates of precision, recall, and F1 are all above 902.15%. This method provides technical support for intelligent question answering system of crop disease-pests.

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“…By using advanced image processing as well as machine learning methods is possible to recognize and observe crop illness in real-time [7]. This can greatly benefit farmers and agricultural researchers by helping them detect diseases early and take appropriate measures to prevent crop loss [8]. Additionally, automated disease identification can aid to reduce the need for toxic pesticides and other chemicals, resulting to a more sustainable and environmentally-friendly method for farming.…”

Section: Introductionmentioning

confidence: 99%

Constitutive Artificial Neural Network espoused Plant Leaf Disease Detection

Kanagaraj,

Kulandaivel,

Shajin

et al. 2024

Preprint

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The emergence of new diseases on plant leaves poses a substantial threat to global food safety and agricultural productivity. To mitigate this risk, accurate and swift detection of plant illnesses is crucial, reducing unnecessary expenses and minimizing financial losses and environmental damage. This study proposes a method called Plant Leaf Disease Detection with a Constitutive Artificial Neural Network (PLDD-CANN) to provide advancements in deep learning. The approach begins by gathering data from the Plant Village dataset and subjecting it to pre-processing techniques. This includes noise removal and image enhancement using a Variational Marginalized Particle Filter (AVMPF). Next, an Adaptive Convex Clustering (ACC) method is employed for image segmentation, followed by feature extraction using Fast Fourier and Continuous Wavelet (FFCWT) transforms. Finally, a Constitutive Artificial Neural Network (CANN) is utilized to categorize the input image to one of several categories, including healthy and various disease types like Yellow Leaf Curl Virus, Septoria Leaf Spot, Two-Spotted Spider Mite, Bacterial Spot, Target Spot, Leaf Mold, Mosaic Virus, Early Blight, and Late Blight. Then, the proposed technique is simulated using Python under several performance metrics including precision, f1-score, error rate accuracy, sensitivity, specificity and ROC. The proposed PLDD-CANN method provides 26.75%, 25.83% and 27.46% higher accuracy comparing with existing methods an enhanced CNN technique for plant leaves disease diagnosis in tomato (CNN-PLDD), A Novel Approach for Plant Leaf Disease Predictions with Recurrent Neural Network RNN Classification Method (RNN-PLDD), Detection of tomato leaf diseases for agro-based industries (FRCNN-PLDD) respectively.

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Crossover-based wind-driven optimized convolutional neural network model for tomato leaf disease classification

Cited by 18 publications

References 35 publications

Optimized deep feature selective concatenation of recurrent encoder model and pretrained convolutional neural network model for leaf disease identification

Optimized deep feature selective concatenation of recurrent encoder model and pretrained convolutional neural network model for leaf disease identification

Classification of crop disease-pest questions based on BERT-BiGRU-CapsNet with attention pooling

Constitutive Artificial Neural Network espoused Plant Leaf Disease Detection

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