BookPDF Available

ISTANBUL INTERNATIONAL MODERN SCIENTIFIC RESEARCH CONGRESS Istanbul, Turkey

June 2021

June 2021

Edition: first edition
Publisher: İKSAD GLOBAL PUBLISHING
Editor: Dr. Vedat AKMAN, Assist. Prof. Radhika Arora
ISBN: 978-605-70671-3-5

Authors:

Otto-von-Guericke-Universität Magdeburg

University of Tehran

Herbal plants have many medicinal properties. Distinguishing different types of medicinal plants from other similar plants and weeds is essential in agriculture. At present, the identification of herbal plants is done visually by experts, which is a tedious, time-consuming and energy intensive. Therefore, the use of new technologies can be a good alternative to laboratory and visual methods. In this research, in order to accurately and quickly identify herbal plants, a machine vision system and machine learning algorithm were performed. After preparing the sample images by the machine vision system, an intelligent image processing algorithm was utilized to extract the features. An optimal SVM algorithm was implemented to classify the features extracted from the plant sample images. 98.99%, which indicated the potential of the proposed method in the diagnosis of herbal plants.

Content uploaded by Pourya Bazyar

Content may be subject to copyright.

A preview of the PDF is not available

ResearchGate has not been able to resolve any citations for this publication.

ResearchGate has not been able to resolve any references for this publication.

Article

Developing an automated monitoring system for fast and accurate prediction of soil texture using an...

December 2021 · Measurement

To guarantee proper seedbed preparation, it is important to assess and control soil aggregate size in tillage operations. Doing so would lead to higher crop yield and more efficient resource use. This study proposes a portable smartphone-based machine vision system using convolutional neural network (CNN) for the classification of soil texture images taken from 20, 40 and 60 cm heights. The ... [Show full abstract] proposed CNN model consists of two blocks with several different layers. The first block (feature extraction) includes Conv, Max-pooling, drop out and batch normalization layers. The second block (classifier) consists of fully connected layers, flatten and SVM classifier. Also in this study, ANN, SVM, RF and KNN algorithms were used to compare the proposed CNN results with other classifiers. The proposed CNN model was able to successfully predict soil images in distances of 20, 40 and 60 cm with the accuracies of 99.89, 99.81 and 99.58 %, respectively. The results showed that the best performance was obtained when using fully preprocessed images at the height of 20 cm. Ultimately, a graphical user interface was designed in form of a user-friendly software to predict soil texture based on CNN model. The results revealed the proposed CNN method could quickly and accurately predict the type of soil texture on large scale farms and thus be a good alternative to the costly and time-consuming laboratory methods.

Article

Evaluation of hawthorns maturity level by developing an automated machine learning-based algorithm

September 2022 · Ecological Informatics

Marketability of agricultural products depends heavily on appearance attributes such as color, size, and ripeness. Sorting plays an important role in increasing marketability by separating crop classes according to appearance attributes, thus reducing waste. As an expert technique, image processing and artificial intelligence (AI) techniques have been applied to classify hawthorns based on ... [Show full abstract] maturity levels (unripe, ripe, and overripe). A total of 600 hawthorns were categorized by an expert and the images were taken by an imaging box. The geometric properties, color and, texture features were extracted from segmented hawthorns using the Gray Level Co-occurrence Matrix (GLCM) and evaluation of various color spaces. The efficient feature vector was created by QDA feature reduction method and then classified using two classical machine learning algorithms: Artificial Neural Network (ANN) and Support Vector Machine (SVM). The obtained results indicated that the efficient feature-based ANN model with the configuration of 14–10-3 resulted in the accuracy of 99.57, 99.16, and 98.16% and the least means square error (MSE) of 1 × 10⁻³, 8 × 10⁻³, and 3 × 10⁻³ for training, validation and test phases, respectively. The machine vision system combined with the machine learning algorithms can successfully classify hawthorns according to their maturity levels.

Book

Full-text available

An overview of using image processing and hyperspectral Remote Sensing applications in Agriculture

November 2021

Pourya Bazyar

One of the crucial factors for improving agricultural productivity is using image processing techniques and hyperspectral Remote Sensing applications. It is presented a short inquiry on usage of image processing techniques and hyperspectral sensors for helping researchers and farmers to precise agricultural practices including weed and herbicide technologies, monitoring plant growth and plant ... [Show full abstract] nutrition management. Hyperspectral sensors are devices that acomplishes images over hundreds of spectral bands, thereby enabling the extraction of spectral signatures for objects or materials observed. Hyperspectral remote sensing has been used over a sheer variety of applications, such as agriculture, forestry, geology, ecological monitoring and disaster monitoring. Observations is used to optimize the utilization of resources and management of farming practices such as usage of hyperspectral image processing in the monitoring of plant diseases, insect pests and invasive plant species; the estimation of crop yield; and the fine classification of crop distributions. This paper presents an in-depth overview of hyperspectral data processing techniques and suggestions for making progress in the agricultural applications of hyperspectral technologies.

Article

Waste Management from Hawthorn Fruit Based on Ripeness Level Using Machine Vision and Deep Learning...

January 2021 · SSRN Electronic Journal