Recent machine learning based approaches for disease detection and classification of agricultural products

2016 ◽  
Author(s):  
Mukesh Kumar Tripathi ◽  
Dhananjay D. Maktedar
Keyword(s):  
Machine Learning ◽  
Agricultural Products ◽  
Disease Detection

scholarly journals CLASSIFICATION OF STRAWBERRY FRUIT SHAPE BY MACHINE LEARNING

2018 ◽  
Vol XLII-2 ◽  
pp. 463-470 ◽  
Author(s):  
T. Ishikawa ◽  
A. Hayashi ◽  
S. Nagamatsu ◽  
Y. Kyutoku ◽  
I. Dan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Similarity Index ◽  
Fruit Shape ◽  
Agricultural Products ◽  
Human Knowledge ◽  
Chain Code ◽  
Machine Learning Methods ◽  
Chain Codes ◽  
Elliptic Fourier Descriptors

Shape is one of the most important traits of agricultural products due to its relationships with the quality, quantity, and value of the products. For strawberries, the nine types of fruit shape were defined and classified by humans based on the sampler patterns of the nine types. In this study, we tested the classification of strawberry shapes by machine learning in order to increase the accuracy of the classification, and we introduce the concept of computerization into this field. Four types of descriptors were extracted from the digital images of strawberries: (1) the Measured Values (MVs) including the length of the contour line, the area, the fruit length and width, and the fruit width/length ratio; (2) the Ellipse Similarity Index (ESI); (3) Elliptic Fourier Descriptors (EFDs), and (4) Chain Code Subtraction (CCS). We used these descriptors for the classification test along with the random forest approach, and eight of the nine shape types were classified with combinations of MVs + CCS + EFDs. CCS is a descriptor that adds human knowledge to the chain codes, and it showed higher robustness in classification than the other descriptors. Our results suggest machine learning's high ability to classify fruit shapes accurately. We will attempt to increase the classification accuracy and apply the machine learning methods to other plant species.

scholarly journals Leaf Disease Classification Using Convolutional Neural Network

2021 ◽  
Author(s):  
Mitali V. Shewale ◽  
Rohin Daruwala
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Agricultural Products ◽  
Disease Classification ◽  
Agricultural Fields ◽  
Human Intervention ◽  
Feasible Solutions ◽  
Major Domain

Agriculture is a major domain that contributes a lot for building up the country’s Economy; contributing to the GDP area synthesis of 17.9%. India stands second in production of agricultural products. Promising technologies such as Internet of Things, Machine Learning, Deep learning, Artificial neural networks contributes towards the most effective and reliable solutions by providing the most feasible solutions in making of different domain modernization through automation in monitoring and maintenance of agricultural fields with minimum human intervention. This paper presents a convolutional neural network based customized VGG framework and a lightweight architecture for the classification of tomato leaves affected with various diseases. Experimental analysis is performed on publically available PlantVillage dataset. After rigorous experiment we fined tuned the CNN model to obtain mAP of 83.33%.

A Review on Heart Disease Detection Techniques

2017 ◽  
Vol 7 (7) ◽  
pp. 395
Author(s):  
. Anika ◽  
Navpreet Kaur
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Review Paper ◽  
Vascular Diseases ◽  
Disease Detection ◽  
Automated Classification ◽  
Feature Extraction Method ◽  
Detection Techniques ◽  
Cardio Vascular ◽  
Public Domain Software

The paper exhibits a formal audit on early detection of heart disease which are the major cause of death. Computational science has potential to detect disease in prior stages automatically. With this review paper we describe machine learning for disease detection. Machine learning is a method of data analysis that automates analytical model building.Various techniques develop to predict cardiac disease based on cases through MRI was developed. Automated classification using machine learning. Feature extraction method using Cell Profiler and GLCM. Cell Profiler a public domain software, freely available is flourished by the Broad Institute's Imaging Platform and Glcm is a statistical method of examining texture .Various techniques to detect cardio vascular diseases.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

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.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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