Paddy leaf diseases recognition and classification using PCA and BFO-DNN algorithm by image processing

Nigam, Aakrati; Tiwari, Avdhesh Kumar; Pandey, Akhilesh Kumar

doi:10.1016/j.matpr.2020.08.397

Cited by 20 publications

(13 citation statements)

References 8 publications

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“…The authors found that the decision tree algorithm achieved an accuracy of more than 97% after applying it to a test database of 10 cross-tests. Nigam et al (2020) introduced the detection and classification of rice leaf diseases using PCA and PFO-DNN algorithm. This approach used the taxonomic feature of rice leaf diseases and the BFO-DNN method.…”

Section: Related Workmentioning

confidence: 99%

Reliable detection of blast disease in rice plant using optimized artificial neural network

Dubey,

Choubey

2023

Agronomy Journal

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Early Plant disease diagnosis is necessary for several purposes, including reducing yield losses, monitoring and predicting infections, detecting host resistance, and studying basic host‐pathogen biological processes. However early detection has been limited a trained workforce and the ability to identify the problem early in the growing season. Artificial intelligence / machine learning algorithms can help fill this gap. Automatic leaf detection using machine learning is proposed in this study. The presented approach of three stages namely, pre‐processing, feature extraction, and classification. Initially, the input image is transformed into the red, green, and blue format, and the noise in the green band is removed using a median filter. Then, important features of the green band are extracted. After feature extraction, the extracted features are fed to the Optimized Artificial Neural Network (OANN) classifier to classify an image as normal or diseased. To improve Artificial Neural Network (ANN) performance, the Artificial Neural Network (ANN) parameters are chosen optimally using the Adaptive Sunflower Optimization (ASFO) algorithm. Then, the infected region is separated using a level set segmentation algorithm. The efficiency of our work is analyzed based on accuracy, sensitivity, and specificity, the proposed method reached the maximum accuracy of 97.94% for plant disease prediction.This article is protected by copyright. All rights reserved

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Section: Related Workmentioning

confidence: 99%

Reliable detection of blast disease in rice plant using optimized artificial neural network

Dubey,

Choubey

2023

Agronomy Journal

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“…Nigam et al (2020) have offered a detection and classification technique to study the different leaf infections using image processing. The digital pictures of paddy leaves were gathered for preprocessing phase.…”

Section: Literature Surveymentioning

confidence: 99%

“…Though, this model is not suitable for real‐time detection. BFO‐DNN (Nigam et al, 2020) is efficient for processing the HD images and increases the performance in terms of accuracy. Nevertheless, it takes more time for processing.…”

Section: Literature Surveymentioning

confidence: 99%

Hybridization of ResNet with YOLO classifier for automated paddy leaf disease recognition: An optimized model

Ganesan

Jayakumar

2022

Journal of Field Robotics

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Paddy is the most significant crop utilized by more than 2.6 billion people. The paddy crops are affected by various diseases that are unidentified and reduced the production of crop yield. Nowadays, the plants diseases and pests spread increasingly due to the climate change, trade, and globalization. The plant pathogens can be viral, fungal, nematodes or bacterial that affects all parts of the plants. The challenging tasks are to determine the symptoms and identify the controlling measures of the plant diseases. The plant leaves can be affected by numerous diseases, which results in destruction in terms of crop field to various social and economic aspects. The deep structured architectures and machine learning are implemented in the conventional models for detecting the leaf diseases.Hence, the main intention of this study is to develop the novel model for paddy leaf disease recognition using the hybrid deep learning. Initially, the input paddy leaf images are collected from standard sources that undergo filtering and contrast enhancement approaches. Further, the segmentation of the abnormal region of the paddy leaf is done by "adaptive K-means clustering." This is also accomplished by the Fitness Sorted-Shark Smell Optimization (FS-SSO). With the segmented images, the recognition of the disease is performed by the hybrid deep learning using the Resnet and YOLO classifier. As the modification, the fully connected layer of the ResNet model is replaced by the YOLO classifier for disease recognition. The significant parameters of the hybrid deep learning are optimized by the FS-SSO for attaining the high recognition rate. Experimental analysis is performed for computing the performance metrics and the accuracy of the classification for evaluating the efficiency of the suggested method.

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“…Nigam et al [11] developed a new idea for the recognition and classification of paddy leaf diseases. First, different paddy leaves were collected as digital images and then the RGB model was transformed into a Hue, Saturation, Value (HSV) model to resize the image by applying k-means clustering with image segmentation.…”

Section: Literature Surveymentioning

confidence: 99%

Multi-Channel Convolutional Neural Network for Prediction of Leaf Disease and Soil Properties

2022

IJIES

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Prediction of leaf disease and soil property helps farmers improve crop production quality through maintaining the soil property and taking proper actions for leaf disease. Various techniques have been developed to predict leaf disease and soil properties. Support Vector Machine (SVM) is one of the machine learning techniques that was used to predict leaf disease and soil properties. To predict the leaf disease and soil property, SVM processed the extracted features from leaf images and soil images. Deep learning can be used for prediction, which has the advantage of machine learning is that one does not need to be concerned about domain knowledge as no feature engineering is required in this, unlike SVM-based prediction. In addition to this, SVM-based prediction is not very effective for handling multiple inputs. A Convolutional Neural Network (CNN) is a deep learner which was applied for the prediction of leaf disease and soil property. Even though this method has better performance, the information from leaf and soil images is mixed together, which may affect the prediction accuracy. So, in this paper, a Multichannel CNN (MCNN) method is introduced in which individual channels are used for leaf and soil images. In MCNN, the feature learning using MCNN for leaf images is kept distinct from the soil image to avoid data fusion between the leaf and soil images. The features related to leaf and soil images are paired and transferred over the corresponding channels for the prediction of leaf disease and soil property. After the prediction of leaf disease and soil property, the correlation between leaf disease and soil property is identified using the Pearson correlation coefficient and it is sent to the farmers using mobile phones to improve their crop production. Finally, these methods are validated by using different leaf infections and soil images for 3 types of crops. The experimental results show that the MCNN method achieves an average accuracy of 87.77% for leaf disease prediction and 90.38% for soil property prediction compared to the classical methods.

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Paddy leaf diseases recognition and classification using PCA and BFO-DNN algorithm by image processing

Cited by 20 publications

References 8 publications

Reliable detection of blast disease in rice plant using optimized artificial neural network

Reliable detection of blast disease in rice plant using optimized artificial neural network

Hybridization of ResNet with YOLO classifier for automated paddy leaf disease recognition: An optimized model

Multi-Channel Convolutional Neural Network for Prediction of Leaf Disease and Soil Properties

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