Disease detection in maize leaves using deep learning networks

Year 2021, Volume: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Issue: Special, 208 - 216, 20.10.2021

Mustafa Göksu , Kubilay Muhammed Sünnetci , Ahmet Alkan

https://doi.org/10.53070/bbd.989305

Cited By: 3

Abstract

Nowadays, people need easy access to basic nutrients to live a healthy life. In addition to providing calories that can meet the physiological needs of human beings, maize, which is one of the basic foods, contains valuable minerals and vitamins such as vitamin B6, sodium, magnesium, zinc, potassium, calcium, vitamin A. As a result of the increase in the world population in the world and our country, the need for maize is increasing day by day. Herein, it is important to detect the diseases seen in maize leaves that reduce the efficiency of maize production. Thanks to the developing technologies, producers should be encouraged by using technological opportunities in maize cultivation. In the study, it is aimed to detect maize rust, gray leaf spot, and leaf blight on maize leaves. In addition, two models based on the EfficientNetB5 network and convolutional neural network have been developed to detect diseases found in maize leaves using deep learning methods. To increase the performance metrics of created models, the number of images has been increased by using data augmentation techniques (mirror, rotation, scale). From the results, it is seen that the prediction success rates obtained in the EfficientNetB5 transfer learning model and the developed deep learning model are equal to 92.12% and 89.88%, respectively.

Keywords

Deep learning, image processing, convolutional neural network (CNN), maize leaf diseases, EfficientNetB

Derin öğrenme ağları kullanılarak mısır yapraklarında hastalık tespiti

Abstract

Günümüzde insanların sağlıklı yaşayabilmeleri için temel besinlere kolayca erişebilmeleri gerekmektedir. Temel besinlerden olan mısırda insanoğlunun fizyolojik ihtiyaçlarını karşılayabilecek kalorinin sağlanması yanında mısırda yer alan B6 vitamini, sodyum, magnezyum, çinko, potasyum, kalsiyum, A vitamini gibi değerli mineraller ve vitaminler bulunmaktadır. Dünya’da ve ülkemizde dünya nüfusunun artmasıyla, mısıra olan ihtiyaç gün geçtikçe artmaktadır. Bu noktada, mısır üretiminin verimliliğini düşüren mısır yapraklarında görülen hastalıkların tespiti önemlidir. Gelişen teknolojiler sayesinde mısır yetiştiriciliğinde teknolojik imkânlar kullanılarak üreticilerin teşvik edilmesi gerekmektedir. Bu çalışma sayesinde, mısır yapraklarında görülen mısır pası, gri yaprak lekesi ve yaprak yanığı tespitinin gerçekleştirilmesi amaçlanmıştır. Ayrıca, derin öğrenme yöntemleri kullanılarak mısır yapraklarında görülen hastalıkların tespit edilebilmesi için EfficientNetB5 ağı ve evrişimsel sinir ağları tabanlı iki adet model geliştirilmiştir. Oluşturulan modellerin performans metriklerini arttırabilmek için, görüntülerin sayısı veri çoğaltma teknikleri kullanılarak (aynalama, döndürme, büyültme) arttırılmıştır. Sonuçlardan, EfficientNetB5 transfer öğrenmesi modeli ve geliştirilen derin öğrenme modelinde elde edilen tahmin başarı oranlarının sırasıyla %92.12 ve %89.88’e eşit olduğu görülmektedir.

Keywords

Derin öğrenme, görüntü işleme, evrişimli sinir ağları (ESA), mısır yaprağı hastalıkları, EfficientNetB5

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