A Framework for Crop Disease Detection Using Feature Fusion Method

Bhagwat, Radhika; Dandawate, Yogesh H.

doi:10.46604/ijeti.2021.7346

Cited by 19 publications

(12 citation statements)

References 28 publications

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“…Bhagwat et al [10] found that the use of complex models (such as ensemble and deep learning models) enabled data scientists to achieve the best possible accuracy in heart disease diagnosis. Their study was conducted using benchmark datasets such as the heart disease data repository of the University of California Irvine (UCI).…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation for Chronic Heart Disease Detection

Admassu

2023

Proc. eng. technol. innov.

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This study aims to investigate the effectiveness of local interpretable model-agnostic explanation (LIME) and Shapley additive explanation (SHAP) approaches for chronic heart disease detection. The efficiency of LIME and SHAP are evaluated by analyzing the diagnostic results of the XGBoost model and the stability and quality of counterfactual explanations. Firstly, 1025 heart disease samples are collected from the University of California Irvine. Then, the performance of LIME and SHAP is compared by using the XGBoost model with various measures, such as consistency and proximity. Finally, Python 3.7 programming language with Jupyter Notebook integrated development environment is used for simulation. The simulation result shows that the XGBoost model achieves 99.79% accuracy, indicating that the counterfactual explanation of the XGBoost model describes the smallest changes in the feature values for changing the diagnosis outcome to the predefined output.

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Section: Literature Reviewmentioning

confidence: 99%

Evaluation of Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation for Chronic Heart Disease Detection

Admassu

2023

Proc. eng. technol. innov.

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“…The other image enhancement method using image resizing, ltering, color space conversion, and histogram equalization was discussed by Ngugi et al [31]. Moreover, resizing [32] and data augmentation [33] approaches were also opted for image enhancement for maize leaves as well as for multiple crops. The other leaf disease detection system that detects diseases in sorghum leaf utilized basic image processing methods including, Edge detection, thresholding, and noise reduction [34].…”

Section: Related Workmentioning

confidence: 99%

Image Quality Enhancement using CLAHlet RetiGaussian Filter for Maize Leaf Images

Handa

Krishan

2023

Preprint

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In this world of digitization, most of the data is in the form of images acquired using camera. Image enhancement plays a vital role in the quality improvement of digital images. In this work, a combined approach based on the contrast limited adaptive histogram equalization (CLAHE) and Retinex algorithm is proposed. It is a wavelet based Retinex algorithm with adaptive histogram equalization and gaussian filter. First, image is enhanced using CLAHE, image is decomposed using Daubechies wavelet and then followed by the Retinex algorithm, which used low frequency components to enhance the image. Lastly, a gaussian filter is used to smoothen the image. The dataset of maize leaf disease is used for the analysis of quality enhancement and denoising. It is clear from the results that the proposed method improves the quality by reducing the noise of the maize leaf images. Theses refined images can be used for maize leaves disease detection and classification system to achieve high accuracy.

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“…However, it failed to de-noised and de-blur the motion images. A fusion method for crop disease detection was proposed in [27]. Decimation of Plant village dataset having total of 54,308 images and digipathos dataset having total of 43,106 images are done on the GLCM and Gabor lter for feature extraction.…”

Section: Background Studiesmentioning

confidence: 99%

Wheat Leaf Rust Detection Based on Multi-Scale DWT and LCS Color Thresholding Methods

Khan

Ullah

Rehman³

et al. 2023

Preprint

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Health, Technology, education, and food production are the four main issues facing developing nations like Pakistan, and it is undeniable that agriculture is the most important factor behind economic growth. In addition, implementing a strategy for food production is crucial for citizens to ensure their survival, and it is assumed that these initiatives will result in sufficient farm productivity. One strategy to make a field productive is to take significant care of its components, which starts with cultivating healthy plants or crops. Wheat leaf rust is a fatal condition that attacks young seedlings. It is a significant fungi disease. Leaf rust has 25% effect on the productivity of wheat. To mitigate this issue, a Multi-Scale Discrete Wavelet Transform (MsclDWT) using hybrid fusion rules method is proposed to obtain the complementary information from multiple input images. In second phase, Lab color space followed by color thresholding method is applied to detect and segment wheat leaf rust disease in wheat crop. The proposed model also computes the rust-affected area of the wheat crop, which assists the farmers in the post-medication (anti rust spray) process. The empirical results show that the proposed model achieved 97% of accuracy in rusted pixels detection and classification and outperformed the existing comparative methods.

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A Framework for Crop Disease Detection Using Feature Fusion Method

Cited by 19 publications

References 28 publications

Evaluation of Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation for Chronic Heart Disease Detection

Evaluation of Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation for Chronic Heart Disease Detection

Image Quality Enhancement using CLAHlet RetiGaussian Filter for Maize Leaf Images

Wheat Leaf Rust Detection Based on Multi-Scale DWT and LCS Color Thresholding Methods

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