scholarly journals A Survey on Diseases Detection and Classification of Agriculture Products using Image Processing and Machine Learning

2018 ◽  
Vol 180 (13) ◽  
pp. 7-12 ◽  
Author(s):  
Nilay Ganatra ◽  
Atul Patel
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Agriculture Products

The Classification of Skateboarding Trick Manoeuvres Through the Integration of Image Processing Techniques and Machine Learning

2020 ◽  
pp. 347-356
Author(s):  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Ar Rahim Ibrahim ◽  
Mohd Azraai Mohd Razman ◽  
Muhammad Amirul Abdullah ◽  
Rabiu Muazu Musa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Image Processing Techniques ◽  
Processing Techniques

scholarly journals Recognition and classification of pomegranate leaves diseases by image processing and machine learning techniques

2021 ◽  
Vol 66 (3) ◽  
pp. 2939-2955
Author(s):  
Mangena Venu Madhavan ◽  
Dang Ngoc Hoang Thanh ◽  
Aditya Khamparia ◽  
Sagar Pande ◽  
Rahul Malik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Real Time IoT Application for Classification of Crop Diseases using Machine Learning in Cloud Environment

2020 ◽  
Vol 6 (4) ◽  
pp. 1-4
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Real Time ◽  
Clustering Algorithm ◽  
Agricultural Land ◽  
Image Processing Technique ◽  
Raspberry Pi ◽  
Crop Diseases ◽  
Internet Of Things Technology

India is an agricultural country. A total of 61.5% of the people cultivate in India. Due to lack of agricultural land and change of weather, manytypes of diseases occur on crops and insects are born.Therefore, the production of crops is coming down. To reduce this problem, Internet of Things technology will prove to be an important role. In this system, a sensor network will be created on agricultural land using Raspberry Pi 3 model. The images of the crops will be taken by sensor cameras and these images will be sent to the cloud server via Raspberry Pi 3 model. In this proposed methodology, various image processing techniques willbe apply on acquired images for classification of crop diseases using k-means clustering algorithm with unsupervised machine learning. This paper will also shows the method of image processing technique such as image acquisition, image pre-processing, image segmentation and feature extraction for classification of crop diseases.In bad natural environment, the farmers can produce quality crops and people will get healthy foodby this proposed methodologyand make more profit.In real time treatme

scholarly journals Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning

2020 ◽  
Vol 14 (11) ◽  
pp. 2442-2456
Author(s):  
Kazy Noor e Alam Siddiquee ◽  
Md. Shabiul Islam ◽  
Mohammad Yasin Ud Dowla ◽  
Karim Mohammed Rezaul ◽  
Vic Grout
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Comparative Analysis

scholarly journals Smart farming: disease detection in crops

2018 ◽  
Vol 7 (2.7) ◽  
pp. 33
Author(s):  
G Balram ◽  
K Kiran Kumar
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Energy Saving ◽  
Structural Design ◽  
Learning Mechanisms ◽  
High Productivity ◽  
The Core ◽  
Broad Concept ◽  
Smart Farming

Automation in the market has been for decades now in the market. These systems run all through the day but there has not been any design which regards to identifying and classifying objects in specific and giving apt solution to them. Latest technologies like image processing and advanced machine learning mechanisms have become the core of the science and innovations. Our project’s idea is a broad concept that integrates various subjects and generates solutions to energy saving, cooling, automation and structural design.  To get the desired results, above are the few constraints which play a major role in yielding high productivity from the crops. And besides them, the classification of objects through image processing and machine learning is used instead of the man power to accomplish the target to reduce the amount of hardware used

scholarly journals Accident Detection and Number Plate Recognition using Image Processing and Machine Learning

2020 ◽  
Vol 8 (5) ◽  
pp. 5079-5083
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Classification Algorithms ◽  
Machine Learning Classification ◽  
Image Enhancements ◽  
Canny Edge ◽  
Continuous Frames ◽  
Processing Techniques ◽  
Accident Detection

The purpose of this project is to detect the accident before it happens along with theextraction the number plate. Different image processing techniques along with morphological operators and Canny Edge Detection are used for image enhancements and object outline detections. With analysis of continuous frames, the relative velocity and the distance from which the leading vehicles are moving could be computed which is further helpful in accident detection and thus prevention too. Histogram of Oriented Gradients (HOG features) are used for feature extraction. Different machine learning classification algorithms like SVM, MLP, and XGBoost are used for classification of the object. Different standard OCR tools like Pytesseract, PyOCR, TesserOCR are used for the retrieval of the vehicle number from the extracted licence plate sub-image.

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