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KURTULMUŞ, EZGİ

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KURTULMUŞ

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    Publication
    Deep learning for proximal soil sensor development towards smart irrigation
    (Pergamon-elsevier Science Ltd, 2022-03-23) Kurtulmus, Ezgi; ARSLAN, BİLGE; KURTULMUŞ, EZGİ; Arslan, Bilge; KURTULMUŞ, FERHAT; Kurtulmus, Ferhat; Ziraat Fakültesi; Biyosistem Mühendisliği Bölümü; R-8053-2016
    Excessive agricultural water consumption threatens safe access of billions of people to potable water. Smart irrigation systems offer more efficient water use in irrigated agriculture. Determining irrigation requirements of various soil texture classes in production fields requires more sophisticated sensing technologies such as deep learning. This study has proposed a proximal sensing system by means of using a color camera towards smart irrigation based on computer vision and deep learning to identify water requirements of three soil texture classes under different illumination conditions. An imaging station was composed to reduce the workload in obtaining training images required for training deep convolutional neural network models. Five deep learning architectures were employed to identify texture-water classes: AlexNet, GoogleNet, ResNet, VGG16, and SqueezeNet. Those models were experimented with and investigated to determine the best models in terms of detection performance and speed. By using cross-validation rules, approximately 12,214 images were studied individually for the purpose of training and testing. The AlexNet model outperformed the other deep learning models with an F1 score of 0.9973 in identifying twelve soil texture-water classes. GoogleNet and ResNet displayed the fastest detection speeds with an average processing time of 16.92 ms. The findings obtained from this study have indicated that deep learning bears a great potential in determining irrigation requirements of production fields under varying conditions
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    Determination of pipe diameters for pressurized irrigation systems using linear programming and artificial neural networks
    (Ankara Üniversitesi, 2023-01-01) Kurtulmuş, Ezgi; Kurtulmuş, Ferhat; Kuşcu, Hayrettin; Arslan, Bilge; Demir, Ali Osman; KURTULMUŞ, EZGİ; KURTULMUŞ, FERHAT; KUŞÇU, HAYRETTİN; ARSLAN, BİLGE; DEMİR, ALİ OSMAN; Ziraat Fakültesi; Biyosistem Mühendisliği Bölümü; 0000-0001-9600-7685; AAH-4682-2021; AAH-2936-2021; R-8053-2016; JOP-8553-2023; JLX-2232-2023
    Pressurized irrigation systems are widespread among other alternatives in Mediterranean countries. Since the initial investment costs of pressurized irrigation systems are quite high, it is crucial to determine design parameters such as pipe diameter. Most of the current optimization techniques for pipe diameter selection are based on linear, non-linear, and dynamic programming models. The ultimate aim of these techniques is to produce solutions to problems with less cost and computation time. In this study, a novel approach for determining pipe diameter was proposed using Artificial Neural Networks (ANN) as an alternative to existing models. For this purpose, three pressurized irrigation systems were investigated. Different ANN architectures were created and tested using hydrant level parameters of the irrigation systems, such as irrigated area per hydrant, hydrant discharge, pipe length, and hydrant elevation. Different training algorithms, transfer functions, and hidden neuron numbers were tried to determine the best ANN model for each irrigation system. Using multilayer feed-forward ANN architecture, the highest coefficients of determination were found to be 0.97, 0.93, and 0.83 for irrigation systems investigated. It was concluded that pipe diameters could be determined by using artificial neural networks in the planning of pressurized irrigation systems.