Automatic Olive Peacock Spot Disease Recognition System Development by Using Single Shot Detector

S. Uğuz
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引用次数: 4

Abstract

Tarim alaninda gerceklestirilen yapay zekâ temelli calismalar arasinda, derin ogrenmeye dayanan hastalik tespiti uygulamalarinin giderek yayginlastigi gorulmektedir. Bitki turleri arasindaki cesitlilik ve cogu bitki turunun belirli cografyalarda yetismesi bu alanda gerceklestirilen calismalarin sayisinin istenen duzeyde olmadigini gostermektedir. Dunyada sadece belirli bolgelerde yetisen zeytin bitkisine ait halkali leke hastaligi ozellikle Turkiye’de yaygin olarak gorulmektedir. Bu calismada halkali leke hastaligina ait semptomlarin populer derin ogrenme mimarilerinden olan Single Shot Detector ile tespitine donuk bir uygulama gerceklestirilmistir. Kontrollu kosullar altinda olusturulan veri seti, Single Shot Detector mimarisi uzerinde farkli IoU treshold degerleri ile egitilmistir. IoU=0.5 icin %96 duzeyinde Average Precision degeri elde edilmistir. Ayrica, gerek zeytin yetistiricileri gerekse de konu ile ilgili olan kisiler icin calismanin masaustu uygulamasi gelistirilmistir.
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基于单次检测的橄榄孔雀斑疹病自动识别系统的开发
在农业领域开展的人工智能研究中,基于深度学习的病害检测应用越来越广泛。植物物种的多样性和大多数植物物种在某些地区生长的事实表明,在这一领域开展的研究数量还没有达到理想水平。橄榄树的环斑病只在世界上某些地区种植,在土耳其尤其常见。在这项研究中,利用流行的深度学习架构之一 "单次检测器"(Single Shot Detector)开展了一项检测环斑病症状的应用。在受控条件下生成的数据集在不同 IoU 门限值的 Single Shot Detector 架构上进行了训练。当 IoU=0.5 时,平均精确度达到 96%。此外,还为橄榄种植者和对此感兴趣的人开发了该研究的桌面应用程序。
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