Automated Plant Disease Analysis (APDA): Performance Comparison of Machine Learning Techniques

Akhtar, Asma; Khanum, Aasia; Khan, Shoab Ahmed; Shaukat, Arslan

doi:10.1109/fit.2013.19

Cited by 82 publications

(26 citation statements)

References 7 publications

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“…The wavelet transformation can analysis the detail of image like vertical, diagonal and horizontal subbands of the image [15][16][17]. The detailed information of the image can be retrieved by low pass and high pass filtering [18][19][20]. In the experiment, the discrete wavelet transform used for extracting features from the disease affected the area of the plant.…”

Section: Feature Extraction Using Discrete Wavelet Transformationmentioning

confidence: 99%

Recognition of Tomato Late Blight by using DWT and Component Analysis

Sabrol

Kumar

2017

IJECE

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Plant disease recognition concept is one of the successful and important applications of image processing and able to provide accurate and useful information to timely prediction and control of plant diseases. In the study, the wavelet based features computed from RGB images of late blight infected images and healthy images. The extracted features submitted to Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA) and Independent Component Analysis performed (ICA) for reducing dimensions in feature data processing and classification. To recognize and classify late blight from healthy plant images are classified into two classes i.e. late blight infected or healthy. The Euclidean Distance measure is used to compute the distance by these two classes of training and testing dataset for tomato late blight recognition and classification. Finally, the three-component analysis is compared for late blight recognition accuracy. The Kernel Principal Component Analysis (KPCA) yielded overall recognition accuracy with 96.4%.

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Section: Feature Extraction Using Discrete Wavelet Transformationmentioning

confidence: 99%

Recognition of Tomato Late Blight by using DWT and Component Analysis

Sabrol

Kumar

2017

IJECE

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“…Asma Akhtar [10] has presented a work on the disease detection and prediction. Author defined a machine learning approach to perform the plant disease detection and prediction under pattern analysis.…”

Section: Flower Identification and Classificationmentioning

confidence: 99%

A study on agricultural image processing along with classification model

Chahal

2015

2015 IEEE International Advance Computing Conference (IACC)

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Agricultural image processing is one of most innovative and important image processing areas recognized in last few years. Because of the vast range of associated sub domain it is having the current attention of the researchers. In this paper, the exploration of different domains associated with agricultural image processing is defined. The paper has also explored the recognition model with broader view. The paper has presented a generalized framework for plant disease classification and recognition. The paper has also defined a study on some of the effective classification approaches including SVM, Neural Network, KMeans and PCA.

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“…Then grape leaf disease segmentation is done using K-means clustering. Asma Akhtar [8] has presented an automated way for disease detection and analysis. Author implied the intelligent learning model to generate the leaf feature patterns at the early stage and later on generate the feature segment based on the region level classification model.…”

Section: Related Workmentioning

confidence: 99%

A Clustering Adaptive Neural Network Approach for Leaf Disease Identification

Chahal¹,

Anuradha²

2015

IJCA

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Agricultural image processing is having its significance to classify the agricultural objects as well as to identify the disease. These diseases are specific to the agricultural object or generic in nature. In this paper, an effective way is defined to identify the leaf disease over the plants. The work is here defined based on moment based analysis defined under clustering improved neural network approach. The work is here improved as a layered clustered and classification approach. In first stage, the identification of disease ROI is done. This identification is done using the CMeans Clustering Approach. Once the disease ROI is identified, the next work is to perform neural network approach for disease identification. The obtained results shows the effective recognition of disease area over the leaf images.

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Automated Plant Disease Analysis (APDA): Performance Comparison of Machine Learning Techniques

Cited by 82 publications

References 7 publications

Recognition of Tomato Late Blight by using DWT and Component Analysis

Recognition of Tomato Late Blight by using DWT and Component Analysis

A study on agricultural image processing along with classification model

A Clustering Adaptive Neural Network Approach for Leaf Disease Identification

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