Estimation of Chlorophyll Concentration Index at Leaves using Artificial Neural Networks

Odabas, Mehmet Serhat; Kayhan, Gökhan; Ergun, Erhan

doi:10.1142/s0218126617500268

Cited by 10 publications

(7 citation statements)

References 17 publications

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“…Artificial neural network (ANN) is a computational modeling tool that used to solve many complex real world problems due to its remarkable learning and generalization capabilities [8]. ANN is also non-linear data driven self-adaptive systems, and they can identify and learn correlated patterns between input data sets and corresponding target values, even when the underlying data relationship is unknown.…”

Section: Resultsmentioning

confidence: 99%

“…Particularly in Black Sea Region of Turkey, population density is in the coastal area due to the vacant land problem. Each outfall is characterized according to length (m), maximum depth (m), treatment levels such as untreated, pre-treated, primary treatment, secondary or better treatment, flow (m 3 .day −1 ) and organic matter mass discharged (kg.day −1 ) [8]. Deep sea discharge, which is location 33 domestic deep sea discharges along the Black Sea coast of Turkey, is preferred for wastewater by municipalities because of the land issue [4].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Deep Sea Discharge Systems Efficiency in the Eastern Black Sea Using Artificial Neural Network: a Case Study for Trabzon, Turkey

Odabas

et al. 2022

Braz. arch. biol. technol.

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The aim of this study is to evaluate the parameters such as pH, dissolved oxygen, temperature, conductivity, salinity, biological oxygen demand (BOD), total suspended solid, ammonia, chlorophyll-a and heavy metals affecting total coliform values in seawater using Artificial Neural Network (ANN) modelling at the Eastern Black Sea coast of Turkey. The results obtained from ANN model were compared with actual total coliform values. The samples were taken from the different points selected along the deep sea discharge systems starting from the diffuser end of three domestic deep sea discharge systems at Turkey's Eastern Black Sea coast. ANN model was developed for estimating the relationship between total coliform and other parameters. The parameters measured in seawater samples were analyzed by using the ANN model for prediction of coliform values. The results showed that neural network model is capable of estimating the sea pollution with a reasonable accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Deep Sea Discharge Systems Efficiency in the Eastern Black Sea Using Artificial Neural Network: a Case Study for Trabzon, Turkey

Odabas

et al. 2022

Braz. arch. biol. technol.

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“…A Medicinal and Aromatic Plant (MAP) database of 752 images was used in this research. We randomly collected 200 St. John's wort leaves, 200 melissa leaves, 88 echinacea leaves, 112 mint leaves, and 152 thyme leaves from in the experiment field populations of the MAP 17 …”

Section: Methodsmentioning

confidence: 99%

Comparison of the performance of different learning algorithms in leaf feature extraction and recognition using convolution neural network

Kayhan

2022

Concurrency and Computation

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Summary Plant identification with computer systems has been developed with image processing tools and has helped researchers to identify unknown plant species with high accuracy. In this study, the leaves of five different plants were classified according to their shapes using deep learning. A database was created with leaf images of mint, echinacea, St. John's wort, melissa, and thyme plants. Images in this database were classified with a convolution neural network (CNN). For this classification, 70% training and 30% testing were randomly selected in the database. The parameters of the CNN layer consist of a set of 120prefix×160prefix×3$$ 120\times 160\times 3 $$ learnable filters. In the CNN, 10 3prefix×3$$ 3\times 3 $$ kernel matrices with stride [1 1] were used. A rectified linear unit was chosen as the activation function. Maximum pooling was performed using a 2prefix×2$$ 2\times 2 $$ filter with stride [2 2]. In this classification, five fully connected layers were created. Using CNN, the performance of different learning algorithms was compared. It was observed that CNN achieved more successful results than traditional attribute methods.

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“…Leafsnap currently covers all 185 tree species from the Northeastern United States [19]. Odabas et al [23] set up a MAP database, contains 752 scanned images belonging to 5 kinds of medical and aromatic plants. Main objective of this work was to classification of MAP species using computer vision for precision agriculture.…”

Section: Introductionmentioning

confidence: 99%

“…Leafsnap currently covers all 185 tree species from the Northeastern United States [19]. Odabas et al [23]…”

Section: Introductionmentioning

confidence: 99%

Medicinal and Aromatic Plants Identification Using Machine Learning Methods

Kayhan¹,

Ergun²

2020

Balkan Journal of Electrical and Computer Engineering

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In this study, different machine learning (ML) methods were used to classify medicinal and aromatic plants (MAP) namely St. John's wort (Hypericum perforatum L.), Melissa (Melissa officinalis L.), Echinacea (Echinacea purpurea L.), Thyme (Thymus sp.) and Mint (Mentha angustifolia L.) based on leaf shape, gray and fractal features. Naive Bayes Classifier (NBC), Classification and Regression Tree (CART), K-Nearest Neighbor (KNN), and Probabilistic Neural Network (PNN) classification were used as methods. The results indicated that plant species were successfully recognized the average of correct classification rate. The best classification rate on the NBC was taken: training data for classification rate 98.39% and test data classification rate for 98.00% are obtained. ML could be accurate tools for MAP classification tasks.

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Estimation of Chlorophyll Concentration Index at Leaves using Artificial Neural Networks

Cited by 10 publications

References 17 publications

Evaluation of Deep Sea Discharge Systems Efficiency in the Eastern Black Sea Using Artificial Neural Network: a Case Study for Trabzon, Turkey

Evaluation of Deep Sea Discharge Systems Efficiency in the Eastern Black Sea Using Artificial Neural Network: a Case Study for Trabzon, Turkey

Comparison of the performance of different learning algorithms in leaf feature extraction and recognition using convolution neural network

Medicinal and Aromatic Plants Identification Using Machine Learning Methods

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