Greenhouse Automation

Authors

  • Kalash Milind Waingankar Student, Department of Computer Engineering, Saraswati College of Engineering, Kharghar, Navi Mumbai, Maharashtra, India
  • Rahul Rajendra Waghmare Student, Department of Computer Engineering, Saraswati College of Engineering, Kharghar, Navi Mumbai, Maharashtra, India
  • Akshay Rajendra Rakh Student, Department of Computer Engineering, Saraswati College of Engineering, Kharghar, Navi Mumbai, Maharashtra, India
  • Sujata Bhairanallykar Professor, Department of Computer Engineering, Saraswati College of Engineering, Kharghar, Navi Mumbai, Maharashtra, India

DOI:

https://doi.org/10.37591/joosdt.v7i2.2560

Abstract

The demand has been increasing in the automation sector, to successfully nurture and grow a plant yield. The most important thing here is to moniter the plants during their growth cycle. In this system image processing is used to monitor the disease on leaf after the image of the infected leaf is captured by a robot. For the classification purpose convolutional neural network is used. It can work with a huge number of classes. This system divides the dataset into Training and Testing set for implementation of query images. Our framework utilizes the Resnet-50 which represents remaining system 50. Resnet-50 is essentially a capacity accessible in matlab. Support Vector Machine (SVM) which is an administered AI calculation is utilized for include extraction. The main role of Resnet-50 is to compare the features of images and classify the input image into a corresponding category.

Keywords:Image processing, leaf, convolutional neural network, Resnet-50, SVM, feature extraction.

Cite this Article: Kalash Milind Waingankar, Rahul Rajendra Waghmare, Akshay Rajendra Rakh, Sujata Bhairanallykar.Greenhouse Automation. Journal of Operating Systems Development & Trends. 2020; 7(2): 14–18p.

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Published

2020-08-06

Issue

Vol. 7 No. 2 (2020)

Section

Research Articles

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