Tarım alanında gerçekleştirilen yapay zekâ temelli çalışmalar arasında, derin öğrenmeye dayanan hastalık tespiti uygulamalarının giderek yaygınlaştığı görülmektedir. Bitki türleri arasındaki çeşitlilik ve çoğu bitki türünün belirli coğrafyalarda yetişmesi bu alanda gerçekleştirilen çalışmaların sayısının istenen düzeyde olmadığını göstermektedir. Dünyada sadece belirli bölgelerde yetişen zeytin bitkisine ait halkalı leke hastalığı özellikle Türkiye’de yaygın olarak görülmektedir. Bu çalışmada halkalı leke hastalığına ait semptomların popüler derin öğrenme mimarilerinden olan Single Shot Detector ile tespitine dönük bir uygulama gerçekleştirilmiştir. Kontrollü koşullar altında oluşturulan veri seti, Single Shot Detector mimarisi üzerinde farklı IoU treshold değerleri ile eğitilmiştir. IoU=0.5 için %96 düzeyinde Average Precision değeri elde edilmiştir. Ayrıca, gerek zeytin yetiştiricileri gerekse de konu ile ilgili olan kişiler için çalışmanın masaüstü uygulaması geliştirilmiştir.
Among the artificial intelligence based studies conducted in the field of agriculture, disease recognition methods founded on deep learning are observed to become widespread. Due to the diversity and regional specificity of many plant species, studies performed in this field are not at the desired level. Olive peacock spot disease of the olive plant which grows only in certain regions in the world is a widely encountered disease particularly in Turkey. The aim of this research is to develop an olive peacock spot disease detection system using a Single Shot Detector (SSD) which is one the popular deep learning architectures to support olive farmers. This study presents a data set consisting of 1460 olive leaves samples for the detection of olive peacock spot disease. All of the images of the olive leaves which produced under controlled conditions were collected from Aegean region of Turkey during spring and summer. The data set was trained with different intersection over union (IoU) threshold values using SSD architecture. A 96 % average precision (AP) value was obtained with IoU=0.5. As IOU value goes up from 0.5, erroneously classified olive peacock spot disease symptoms growed larger as well. The AP curve becomes flat when between 0.1 and 0.5, and it decreases when greater than 0.5. This analysis showed that the IoU significantly influenced the performance of SSD based model in detection of olive peacock spot disease. In addition to, trainings were performed by employing Pytorch library and a GUI was developed for the SSD based application using PyQt5 which is one of Pyhton's libraries. Results showed that the SSD was a robust tool for recognizing the olive peacock spot disease.
Primary Language | English |
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Subjects | Artificial Intelligence |
Journal Section | Articles |
Authors | |
Publication Date | December 30, 2020 |
Submission Date | June 19, 2020 |
Acceptance Date | September 9, 2020 |
Published in Issue | Year 2020Volume: 3 Issue: 3 |
The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License