Cross-dataset learning for performance improvement of leaf disease detection using reinforced generative adversarial networks

Nerkar, Bhavana; Talbar, Sanjay N.

doi:10.1007/s41870-021-00772-1

Cited by 13 publications

(6 citation statements)

References 21 publications

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“…These deep learning models [ 20 – 23 ] were mainly designed to offer an optimal balance between the model's performance and complexity. Ensemble models [ 15 ] do have the overfitting issue, and the ensemble model fails to work with unknown discrepancies.…”

Section: Discussionmentioning

confidence: 99%

“…Using batch normalization often complicates the process even more. The existing deep learning architectures have a complex design and struggle with real-time execution [ 20 , 21 ].…”

Section: Discussionmentioning

confidence: 99%

“…Figure 13 provides an accuracy comparison of the proposed model with these techniques. When compared to the state-of-art methodologies, we can observe that the These deep learning models [20][21][22][23] were mainly designed to offer an optimal balance between the model's performance and complexity. Ensemble models [15] do have the overfitting issue, and the ensemble model fails to work with unknown discrepancies.…”

Section: Discussionmentioning

confidence: 99%

“…Nerkar et al [ 20 ] presented a 2-level non-intrusive leaf disease detection approach that combines generative adversarial networks (GANs) with reinforcement learning. GAN uses various datasets to combine the learning models and apply them to new plant leaf images.…”

Section: Literature Reviewmentioning

confidence: 99%

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EOS-3D-DCNN: Ebola optimization search-based 3D-dense convolutional neural network for corn leaf disease prediction

Ashwini

Sellam

2023

Neural Comput & Applic

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Corn disease prediction is an essential part of agricultural productivity. This paper presents a novel 3D-dense convolutional neural network (3D-DCNN) optimized using the Ebola optimization search (EOS) algorithm to predict corn disease targeting the increased prediction accuracy than the conventional AI methods. Since the dataset samples are generally insufficient, the paper uses some preliminary pre-processing approaches to increase the sample set and improve the samples for corn disease. The Ebola optimization search (EOS) technique is used to reduce the classification errors of the 3D-CNN approach. As an outcome, the corn disease is predicted and classified accurately and more effectually. The accuracy of the proposed 3D-DCNN-EOS model is improved, and some necessary baseline tests are performed to project the efficacy of the anticipated model. The simulation is performed in the MATLAB 2020a environment, and the outcomes specify the significance of the proposed model over other approaches. The feature representation of the input data is learned effectually to trigger the model's performance. When the proposed method is compared to other existing techniques, it outperforms them in terms of precision, the area under receiver operating characteristics (AUC), f1 score, Kappa statistic error (KSE), accuracy, root mean square error value (RMSE), and recall.

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Section: Discussionmentioning

confidence: 99%

“…Using batch normalization often complicates the process even more. The existing deep learning architectures have a complex design and struggle with real-time execution [ 20 , 21 ].…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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EOS-3D-DCNN: Ebola optimization search-based 3D-dense convolutional neural network for corn leaf disease prediction

Ashwini

Sellam

2023

Neural Comput & Applic

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“…Nerkar et al (Nerkar and Talbar, 2021) proposed a method to detect leaf disease using a two-level nonintrusive method. This model combines generative adversarial network and reinforcement learning.…”

Section: Literature Surveymentioning

confidence: 99%

Improved tomato leaf disease classification through adaptive ensemble models with exponential moving average fusion and enhanced weighted gradient optimization

V.,

Kumar,

Praveen

et al. 2024

Front. Plant Sci.

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Tomato is one of the most popular and most important food crops consumed globally. The quality and quantity of yield by tomato plants are affected by the impact made by various kinds of diseases. Therefore, it is essential to identify these diseases early so that it is possible to reduce the occurrences and effect of the diseases on tomato plants to improve the overall crop yield and to support the farmers. In the past, many research works have been carried out by applying the machine learning techniques to segment and classify the tomato leaf images. However, the existing machine learning-based classifiers are not able to detect the new types of diseases more accurately. On the other hand, deep learning-based classifiers with the support of swarm intelligence-based optimization techniques are able to enhance the classification accuracy, leading to the more effective and accurate detection of leaf diseases. This research paper proposes a new method for the accurate classification of tomato leaf diseases by harnessing the power of an ensemble model in a sample dataset of tomato plants, containing images pertaining to nine different types of leaf diseases. This research introduces an ensemble model with an exponential moving average function with temporal constraints and an enhanced weighted gradient optimizer that is integrated into fine-tuned Visual Geometry Group-16 (VGG-16) and Neural Architecture Search Network (NASNet) mobile training methods for providing improved learning and classification accuracy. The dataset used for the research consists of 10,000 tomato leaf images categorized into nine classes for training and validating the model and an additional 1,000 images reserved for testing the model. The results have been analyzed thoroughly and benchmarked with existing performance metrics, thus proving that the proposed approach gives better performance in terms of accuracy, loss, precision, recall, receiver operating characteristic curve, and F1-score with values of 98.7%, 4%, 97.9%, 98.6%, 99.97%, and 98.7%, respectively.

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Deep learning implementation of image segmentation in agricultural applications: a comprehensive review

Lei,

Yang,

Yang

et al. 2024

Artif Intell Rev

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Image segmentation is a crucial task in computer vision, which divides a digital image into multiple segments and objects. In agriculture, image segmentation is extensively used for crop and soil monitoring, predicting the best times to sow, fertilize, and harvest, estimating crop yield, and detecting plant diseases. However, image segmentation faces difficulties in agriculture, such as the challenges of disease staging recognition, labeling inconsistency, and changes in plant morphology with the environment. Consequently, we have conducted a comprehensive review of image segmentation techniques based on deep learning, exploring the development and prospects of image segmentation in agriculture. Deep learning-based image segmentation solutions widely used in agriculture are categorized into eight main groups: encoder-decoder structures, multi-scale and pyramid-based methods, dilated convolutional networks, visual attention models, generative adversarial networks, graph neural networks, instance segmentation networks, and transformer-based models. In addition, the applications of image segmentation methods in agriculture are presented, such as plant disease detection, weed identification, crop growth monitoring, crop yield estimation, and counting. Furthermore, a collection of publicly available plant image segmentation datasets has been reviewed, and the evaluation and comparison of performance for image segmentation algorithms have been conducted on benchmark datasets. Finally, there is a discussion of the challenges and future prospects of image segmentation in agriculture.

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Cross-dataset learning for performance improvement of leaf disease detection using reinforced generative adversarial networks

Cited by 13 publications

References 21 publications

EOS-3D-DCNN: Ebola optimization search-based 3D-dense convolutional neural network for corn leaf disease prediction

EOS-3D-DCNN: Ebola optimization search-based 3D-dense convolutional neural network for corn leaf disease prediction

Improved tomato leaf disease classification through adaptive ensemble models with exponential moving average fusion and enhanced weighted gradient optimization

Deep learning implementation of image segmentation in agricultural applications: a comprehensive review

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