Görüntü Tabanlı Dijital Bitki Fenotiplemesi için Konvolüsyonel Sinir Ağları

Year 2020, Ejosat Special Issue 2020 (HORA), 338 - 342, 15.08.2020

Tolga Ensari Dariel Courage Armah Ahmet Emre Balsever Mustafa Dağtekin

https://doi.org/10.31590/ejosat.780087

Cited By: 1

Abstract

Bitkiler çevrenin en önemli bileşenlerinden biridir. Küresel ısınmanın, sürdürülebilir ürün yetiştirme programlarını zorlaştıran, kuraklık gibi etkileri sebebiyle, milyonlarca insan yetersiz beslenmektedir. Bu çalışmanın amacı bilgisayar görüsü, resim tabanlı makine öğrenmesi teknikleri ve evrişimli sinir ağları kullanarak, Uluslararası Bitki Fenotipleme Ağı tarafından araştırmacılara yardımcı olmak amacıyla, bitki yetiştiricilerinin arzu edeceği mahsul özelliklerine sahip ve daha zorlu çevre koşullarına dayanabilecek, canlı bitkilerin üretimine yardımcı olacak genleri belirlemek için, bir model sunmak ve test etmektir. Ayrıca, Türkiye'de ve çevresinde türünün ilk örneği olan bu çalışma, tarımın bu alanında gelecekteki akademik araştırmalar için bir zemin sağlamayı amaçlamaktadır. Seçilen ölçüt mutantların sınıflandırılma kriteridir (bitki hastalık tespiti). Bu araştırmada seçilen veri kümesi, Türkiye'de bulunabilecek başlıca bitki ürünlerinden mısır ve üzümdür. Üzüm ve mısırı etkileyen üç farklı bitki hastalığı sırasıyla kullanılmıştır ve özel evrişimli sinir ağı için toplam 8 farklı sınıfta, eğitim ve test olmak üzere 1600 açıklamalı görüntüye karşılık gelen sağlıklı üzüm ve mısır veri kümesi görüntüsü eklenmiştir. Sonuçlar, geliştirilen modelin eğitimden sonra test veri kümesinde % 97.03 doğruluk elde ettiğini göstermektedir. Böylece çalışmada, geliştirilen modelin şu anda kullanılan evrişimli sinir ağı modellerinden daha iyi performans gösterdiği ve görüntü algılama alanında daha fazla araştırma için bir temel olarak kullanılabileceği sonucuna varılmıştır.

Keywords

Dijital Bitki Fenotipleme, Konvolüsyon Sinir Ağları (CNN), Bilgisayar Görüsü, Bitki Hastalığı Tespiti

Convolutional Neural Networks for Image-Based Digital Plant Phenotyping

Abstract

Plants are one of the most important components of the environment. Millions of people are undernourished because of global warming whose adverse effects such as drought has made it difficult for sustainable crop breeding programs. This paper is aimed to propose and test computer vision and machine learning image-based methods precisely convolutional neural networks; for a benchmark suggested by the International Plant Phenotyping Network to help researchers, plant breeders choose desirable crop traits, and link them to specific genes that helped in the production of viable plants that could withstand harsher environmental conditions. Also as a first of its kind in Turkey and its environ, this paper is aimed to provide a ground base for future research in this area of agriculture. The benchmark chosen is the classification of mutants’ benchmark (plant disease detection). In this paper, the dataset chosen was two of the main cash crops that can be found in Turkey were used: Maize and Grapes. Three different plant diseases affecting Grape and Maize were used respectively and a class of healthy grape and maize annotated images were added amounting to a total of 8 different classes and 1600 annotated images for both training and testing for the custom convolutional neural network to be proposed. The results show that the custom model achieved 97.03 % accuracy on the test dataset after training. The research thus concluded that, the custom model performed better than most currently used convolutional neural network models and can be used as a basis for further research in the field of image detection.

Keywords

Digital Plant Phenotyping, Convolutional Neural Network (CNN), Computer Vision, Plant Disease Detection

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