A Novel method to detect Disease in leaf using Deep Learning Approach

Sophia, S; Devi, D; Prabha, K. Lakshmi; Keerthana, V.; Kavin, V

doi:10.1109/icaccs51430.2021.9441892

Cited by 3 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The early and accurate detection of leaf diseases in crops like C. asiatica (CAU) is crucial for maintaining agricultural productivity and food security. Recent advancements in this field have primarily centered around deep learning techniques, notably convolutional neural networks (CNNs), which have shown promising results in identifying and diagnosing plant leaf diseases with high accuracy [17][18][19]. These modern approaches, including the use of transfer learning with pre-trained models and image processing techniques, have marked a significant shift in disease detection methodologies [20,21].…”

Section: Contemporary Methods For Detecting Leaf Diseases In Cau: Lim...mentioning

confidence: 99%

“…After conducting a thorough analysis of the results presented in Tables 5 and 6, it is evident that the proposed method (He-Meta) outperforms other methods proposed in the literature by an average of 4.85%. Specifically, the He-Meta model provides significantly better accuracy when compared to other models, such as Ho-Mo [19], Ho-SQ [17], and Ho-Sh [18], which are homogenous ensemble models, as well as single models, including EfficientNet-B2 [21], EfficientNet-B3 [21], ResNet-50 [22], DenseNet121 [58], and Inception-ResNet-v2 [21]. The He-Meta model yielded a 4.02%, 3.90%, 5.49%, 4.95%, 4.89%, 6.21%, 8.16%, and 1.16% higher accuracy than the aforementioned models, respectively.…”

Section: Comparison Of Optimal Proposed Model With the State-of-art M...mentioning

confidence: 99%

“…This observation suggests the model's ability to handle variations in datasets and its adaptability to different data distributions. In contrast, when adapting existing methods from the literature, e.g., [17][18][19],…”

Section: Enhancing Leaf Abnormality Detection In C Asiatica: An Ensem...mentioning

confidence: 99%

See 2 more Smart Citations

Two-Stage Ensemble Deep Learning Model for Precise Leaf Abnormality Detection in Centella asiatica

Buakum,

Kosacka-Olejnik,

Pitakaso

et al. 2024

AgriEngineering

View full text Add to dashboard Cite

Leaf abnormalities pose a significant threat to agricultural productivity, particularly in medicinal plants such as Centella asiatica (Linn.) Urban (CAU), where they can severely impact both the yield and the quality of leaf-derived substances. In this study, we focus on the early detection of such leaf diseases in CAU, a critical intervention for minimizing crop damage and ensuring plant health. We propose a novel parallel-Variable Neighborhood Strategy Adaptive Search (parallel-VaNSAS) ensemble deep learning method specifically designed for this purpose. Our approach is distinguished by a two-stage ensemble model, which combines the strengths of advanced image segmentation and Convolutional Neural Networks (CNNs) to detect leaf diseases with high accuracy and efficiency. In the first stage, we employ U-net, Mask-R-CNN, and DeepNetV3++ for the precise image segmentation of leaf abnormalities. This step is crucial for accurately identifying diseased regions, thereby facilitating a focused and effective analysis in the subsequent stage. The second stage utilizes ShuffleNetV2, SqueezeNetV2, and MobileNetV3, which are robust CNN architectures, to classify the segmented images into different categories of leaf diseases. This two-stage methodology significantly improves the quality of disease detection over traditional methods. By employing a combination of ensemble segmentation and diverse CNN models, we achieve a comprehensive and nuanced analysis of leaf diseases. Our model’s efficacy is further enhanced through the integration of four decision fusion strategies: unweighted average (UWA), differential evolution (DE), particle swarm optimization (PSO), and Variable Neighborhood Strategy Adaptive Search (VaNSAS). Through extensive evaluations of the ABL-1 and ABL-2 datasets, which include a total of 14,860 images encompassing eight types of leaf abnormalities, our model demonstrates its superiority. The ensemble segmentation method outperforms single-method approaches by 7.34%, and our heterogeneous ensemble model excels by 8.43% and 14.59% compared to the homogeneous ensemble and single models, respectively. Additionally, image augmentation contributes to a 5.37% improvement in model performance, and the VaNSAS strategy enhances solution quality significantly over other decision fusion methods. Overall, our novel parallel-VaNSAS ensemble deep learning method represents a significant advancement in the detection of leaf diseases in CAU, promising a more effective approach to maintaining crop health and productivity.

show abstract