A variety of fungal and bacterial leaf ailments wreak havoc on the paddy plant in the agricultural field. Early diagnosis of leaf infection can improve the yield of the crop. The modeling of an automatic disease classifier aids farmers in handling the spread of leaf disease in the agricultural field. This work cogitates three paddy leaf diseases (Bacterial blight, leaf smut, and leaf blast) for the creation of an AI-based robust detection and classification model. The dataset is collected from a variety of standard online repositories. GAN-based augmentation technique was used for increasing the size of the dataset. A novel approach to the convolutional neural network is proposed with the combination of augmentation and a CNN model tuner. The performance of CNN is evaluated in terms of accuracy achieved is 98.23\% in the classification process.

GAN based image augmentation for increased CNN performance in Paddy leaf disease classification

Hypothesis: Due to the increase in the losses in paddy yield as a result of various paddy diseases, researchers are working tirelessly for a technological solution to assist farmers in making decisions about disease severity and potential danger to the crop. Early prediction of infection severity would facilitate resources for the treatment of the infection and prevent contamination to the whole field. Methodology: In this study, a hybrid prediction model was developed to predict various levels of severity of blast disease based on diseased plant images. The proposed model is a four-fold severity prediction model. The level of severity is defined based on the percentage of leaf area affected by the disease. The image dataset is derived from both primary and secondary resources. Tools: The features are first extracted with the help of the Convolutional Neural Network (CNN) approach. Then the identification and classification of the severity level of blast disease are conducted using a Support Vector Machine (SVM). Conclusion: Mendeley, Kaggle, GitHub, and UCI are the secondary resources used for dataset generation. The number of images in the dataset is 1908. The proposed hybrid model achieves 97% accuracy.

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A Novel Hybrid Severity Prediction Model for Blast Paddy Disease Using Machine Learning

The demand for agricultural products increased exponentially as the global population grew. The rapid development of computer vision-based artificial intelligence and deep learning-related technologies has impacted a wide range of industries, including disease detection and classification. This paper introduces a novel neural network-based hybrid model (GCL). GCL is a dataset-augmentation fusion of long-short term memory (LSTM) and convolutional neural network (CNN) with generative adversarial network (GAN). GAN is used for the augmentation of the dataset, CNN extracts the features and LSTM classifies the various paddy diseases. The GCL model is being investigated to improve the classification model’s accuracy and reliability. The dataset was compiled using secondary resources such as Mendeley, Kaggle, UCI, and GitHub, having images of bacterial blight, leaf smut, and rice blast. The experimental setup for proving the efficacy of the GCL model demonstrates that the GCL is suitable for disease classification and works with 97% testing accuracy. GCL can further be used for the classification of more diseases of paddy. 

/pdf/a-novel-gcl-hybrid-classification-model-for-paddy-diseases-3g7bsv7p.pdf

A novel GCL hybrid classification model for paddy diseases

Traditional models for predicting diseases in sugarcane crops show some drawbacks, including expensive costs for getting the data input needed to execute the model, a lack of spatial data, or a poor dataset. These problems are discussed in this work, which also develops a yield prediction fusion model. Convolutional neural networks (CNN) and support vector machines (SVM). make up the prediction model.In this work, the leaf smut infection of sugarcane is discussed. The sick plant is first photographed utilizing secondary sources. For feature extraction and classification of the various levels of severity of the smut infection, the best features of the deep learning techniques CNN and SVM are applied. Mild, Average, Severe, and Profound are the four seriousness prediction levels used in the study. Mendeley and Kaggle are the data repositories that were utilized, and the total size of the dataset was 950. The four severity level forecasts made by the suggested framework are 98% accurate.

Deep Learning-based Hybrid Model for Severity Prediction of Leaf Smut Sugarcane Infection

Python is an object-oriented, scripting, and interpretive programming language that may be used for mentoring and real-world applications. This paper focusses primarily on Python software packages used in data science, pattern recognition, and IoT. This paper will first explain Python as a language, then introduce Data Science, Machine learning, and IOT, describing prominent packages in the Data Science and Machine learning community, such as NumPy, SciPy, TensorFlow, Keras, Matplotlib. This paper will also demonstrate the significance of Python in the development of the industry. Throughout, we shall utilize many code samples. We review so many research papers to analyze the usage of python in different fields and easily import packages in the programming software.

An Empirical Analysis of Python Programming for Advance Computing

The wide variety of diseases in the tomato plant affects the quality and quantity of the production. To counteract the problem of disease in tomato plants deep learning (DL) based convolutional neural networks (CNN) model has been presented in this paper that classify the real-time and self-captured 3000 images of healthy and tomato spotted wilt (TSW) disease plants. TSW is a type of infected virus that turns the upper sides of young tomato leaves as bronze and eventually acquires prominent, necrotic spots. Binary and multi-classification of the collected dataset have been made based on three different types of severity levels of TSW disease. In the case of binary classification, the accuracy is recorded at 91.56% and on the other hand, the best accuracy of multi-classification is recorded at 95.23% in the case of middle severity level. The model shows the least accuracy 94.5% and middle accuracy 95.2% in the case of early-stage severity and late severity level respectively. The proposed work will make a significant addition to the field of employing DL techniques to detect and classify tomato diseases.

Khadim Moin Siddiqui

Papers

Tomato Spotted Wilt Disease Severity Levels Detection: A Deep Learning Methodology