Meta-Heuristic Based Deep Learning Model for Leaf Diseases Detection

Ruth, J. Anitha; Uma, R.; Meenakshi, A.; Ramkumar, P.

doi:10.1007/s11063-022-10880-z

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…Similarly, with study [34] ,where a swarm intelligence algorithm (SSAFS) is employed for feature selection, resulting in an accuracy of 84.23%, our approach outperforms it with a superior accuracy rate. In contrast to the methodology proposed in work [35], which introduces the optimal deep neural network (ODNN) for automatic plant leaf disease detection using Convolutional Neural Network (CNN) and the Improved Butterfly Optimization Algorithm to optimize weights, achieving an overall accuracy of 99%, our methodology surpasses this accuracy rate, showcasing its efficacy in plant disease classification. In research article [36], an ensemble method incorporating pre-trained CNN models, VGG16 and VGG19, achieved an accuracy of 98.2%.…”

Section: Comparison and Discussionmentioning

confidence: 80%

“…The research emphasizes the potential of SSAFS as a computational tool to enhance plant disease recognition accuracy and processing time by identifying an optimal combination of handcrafted features. The authors of [35] implemented a framework, known as the optimal deep neural network (ODNN), designed to address the automatic detection of plant leaf diseases, aiming to enhance agricultural production. The framework utilizes leaf images from healthy and diseased plants, employing a Convolutional Neural Network for feature extraction and the ODNN for disease detection.…”

Section: Related Workmentioning

confidence: 99%

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An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification

Taji,

Sohail,

Shahzad

et al. 2024

IEEE Access

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A stable and sustainable food supply chain is only possible with effective agriculture management. The application of technology in agriculture has recently produced encouraging outcomes in terms of improving agricultural yield and quality. Early detection and characterization of plant leaf diseases, which have a substantial influence on agricultural output and the general well-being of farmers and consumers, is a crucial component. The automatic identification and categorization of illnesses affecting apple and maize plants is the focus of this paper's investigation into the implementation of cutting-edge technical solutions, notably machine learning algorithms. Deep learning has recently made significant contributions to the automatic detection and classification of plants diseases specifically in fruits and vegetables. As a result, the production and quality of fruits and vegetables have both improved. Many diseases have a negative impact on crop quality and output. This research proposes an ensemble deep learning based technique for classifying plant leaf diseases. In this work, two separate pre-trained deep learning models have been utilized and a hybrid approach is proposed. In this research work we have proposed a hybrid framework based on the hybrid preprocessing algorithm, an ensemble features engineering phase based on the texture features and two types of deep features extraction. The extracted Convolutional Neural Network (CNN) features are selected and fused with the LBP features. The ensemble feature vector is optimized using three different meta-heuristic algorithms that are Binary Dragonfly algorithm (BDA), Ant Colony Optimization algorithm and Moth Flame Optimization algorithm (MFO). The optimized feature vector is classified using state-of-the-art machine learning algorithms. Notably, the study addresses the research gap by providing a comprehensive analysis of plant diseases affecting fruits and vegetables. Additionally, it acknowledges the limitations of the proposed approach, paving the way for future research and improvements. The utilization of the proposed technique yields a remarkable accuracy of 99.8%, surpassing current state-ofthe-art methods. This research contributes to the ongoing efforts to enhance food security and sustainability by providing an effective solution for the early identification and classification of plant diseases, thereby mitigating the negative impact on crop productivity and quality.

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Section: Comparison and Discussionmentioning

confidence: 80%

Section: Related Workmentioning

confidence: 99%

An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification

Taji,

Sohail,

Shahzad

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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“…Datasets are instrumental in addressing complex research questions [ 34 ], and they play a vital role in advancing research across various disciplines [ 35 ]. The availability of high-quality datasets is crucial for pattern and trend analysis, driving research advancements [ 36 ]. In this work, we present a challenging open-source segmentation dataset of WRD.…”

Section: Discussionmentioning

confidence: 99%

The NWRD Dataset: An Open-Source Annotated Segmentation Dataset of Diseased Wheat Crop

Anwar

Khan

Ghaffar

et al. 2023

Sensors

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Wheat stripe rust disease (WRD) is extremely detrimental to wheat crop health, and it severely affects the crop yield, increasing the risk of food insecurity. Manual inspection by trained personnel is carried out to inspect the disease spread and extent of damage to wheat fields. However, this is quite inefficient, time-consuming, and laborious, owing to the large area of wheat plantations. Artificial intelligence (AI) and deep learning (DL) offer efficient and accurate solutions to such real-world problems. By analyzing large amounts of data, AI algorithms can identify patterns that are difficult for humans to detect, enabling early disease detection and prevention. However, deep learning models are data-driven, and scarcity of data related to specific crop diseases is one major hindrance in developing models. To overcome this limitation, in this work, we introduce an annotated real-world semantic segmentation dataset named the NUST Wheat Rust Disease (NWRD) dataset. Multileaf images from wheat fields under various illumination conditions with complex backgrounds were collected, preprocessed, and manually annotated to construct a segmentation dataset specific to wheat stripe rust disease. Classification of WRD into different types and categories is a task that has been solved in the literature; however, semantic segmentation of wheat crops to identify the specific areas of plants and leaves affected by the disease remains a challenge. For this reason, in this work, we target semantic segmentation of WRD to estimate the extent of disease spread in wheat fields. Sections of fields where the disease is prevalent need to be segmented to ensure that the sick plants are quarantined and remedial actions are taken. This will consequently limit the use of harmful fungicides only on the targeted disease area instead of the majority of wheat fields, promoting environmentally friendly and sustainable farming solutions. Owing to the complexity of the proposed NWRD segmentation dataset, in our experiments, promising results were obtained using the UNet semantic segmentation model and the proposed adaptive patching with feedback (APF) technique, which produced a precision of 0.506, recall of 0.624, and F1 score of 0.557 for the rust class.

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“…The results are evaluated in terms of F1-score, recall rate, and accuracy. Ruth et al (2022) proposed a deep learning model for disease detection using the meta-heuristic algorithm. CNN is used for feature extraction.…”

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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Meta-Heuristic Based Deep Learning Model for Leaf Diseases Detection

Cited by 8 publications

References 25 publications

An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification

An Ensemble Hybrid Framework: A Comparative Analysis of Metaheuristic Algorithms for Ensemble Hybrid CNN Features for Plants Disease Classification

The NWRD Dataset: An Open-Source Annotated Segmentation Dataset of Diseased Wheat Crop

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

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