Identification and Classification of Tomato Leaf Diseases Using Machine Learning Techniques

Tomato is cultivated in all countries of the world in fields, glasshouses etc. China, India, USA, Turkey, Egypt, Iran, Italy, Spain and Brazil are the important countries which are cultivating tomatoes. It is most commonly and widely cultivated in India. India is one of the countries in harvesting tomatoes. Tomato is a vital vegetable yield with respect to both income and food. Tomatoes are for the most part summer crops, yet it tends to improve steadily. Naturally, it contains A and C of vitamins which also acts as an antioxidant to prevent cancerous cells. Since the organic product contains novel features, the demand remains the same. A significant and unique feature with high nutrients gains the importance in tomatoes cultivation. Challenges towards the cultivation of tomato made us to plan for an automated machine to detect infection and to increase the productivity. This system automatically detects the infected parts and classify the types of disease which occur on the leaf like early blight, bacterial wilt, Leaf Spot, tomato mosaic virus, septoria leaf spot, leaf curl virus, and tomato spotted wilt disease using gradient anisotropic diffusion filter for pre-processing and then features are extracted using GLCM from the pre-processed leaf

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Classification of Hepatocellular Carcinoma Using Machine Learning

10.5772/intechopen.99841 ◽

2021 ◽

Author(s):

Lekshmi Kalinathan ◽

Deepika Sivasankaran ◽

Janet Reshma Jeyasingh ◽

Amritha Sennappa Sudharsan ◽

Hareni Marimuthu

Keyword(s):

Machine Learning ◽

Hepatocellular Carcinoma ◽

Support Vector Machine ◽

Deep Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Hepatic Cancer ◽

Learning Techniques ◽

Cancerous Cells

Hepatocellular Carcinoma (HCC) proves to be challenging for detection and classification of its stages mainly due to the lack of disparity between cancerous and non cancerous cells. This work focuses on detecting hepatic cancer stages from histopathology data using machine learning techniques. It aims to develop a prototype which helps the pathologists to deliver a report in a quick manner and detect the stage of the cancer cell. Hence we propose a system to identify and classify HCC based on the features obtained by deep learning using pre-trained models such as VGG-16, ResNet-50, DenseNet-121, InceptionV3, InceptionResNet50 and Xception followed by machine learning using support vector machine (SVM) to learn from these features. The accuracy obtained using the system comprised of DenseNet-121 for feature extraction and SVM for classification gives 82% accuracy.

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A Brief Survey on Text Classification Using Various Machine Learning Techniques

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v8i1.521 ◽

2018 ◽

Vol 8 (1) ◽

pp. 14

Author(s):

Padmavathi .S ◽

M. Chidambaram

Keyword(s):

Machine Learning ◽

Text Classification ◽

Fixed Number ◽

Machine Learning Techniques ◽

Online Information ◽

Rule Based ◽

Learning Techniques ◽

Machine Learning Approach ◽

Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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