Deep Learning Based Classification of Apple Leaf Diseases Using AlexNet

Year 2023, , 67 - 74, 18.10.2023

Felix Olanrewaju Babalola , Nekabari Isabella Kpai , Önsen Toygar

https://doi.org/10.53070/bbd.1349566

Abstract

The diagnosis of a disease on the plants is a critical step in avoiding a significant loss of harvest and agricultural product amount. The indications can be found on parts of plants such as fruits, leaves, lesions, and stems. The leaf demonstrates the symptoms by changing, and therefore revealing the spots on it. This disease identification is accomplished through manual inspection for pathogen detection, which might take extra time and cost. Hence, automatic detection of plant diseases can be vital in the agricultural economy. This study proposes the use of a simple deep learning model, AlexNet, for detecting anomalies in apple leaves in order to predict the presence or absence of a disease in a tree correctly. The Convolutional Neural Network model is implemented using the Plant Village dataset, augmented to 12,624 images for proper training. The proposed apple leaf disease categorization system achieves an overall accuracy of 99.56 percent. For comparison of results, a different method, namely Binarized Statistical Image Features (BSIF), is also implemented. Furthermore, the results are juxtaposed against studies using similar state-of-the art approaches.

Keywords

Apple leaf diseases, detection, deep learning

Supporting Institution

Not available.

Project Number

Not available.

Thanks

Not available.

Elma Yaprağı Hastalıklarının AlexNet Kullanılarak Derin Öğrenme Tabanlı Sınıflandırılması

Abstract

Bitkilerde hastalık teşhisi, önemli miktarda hasat ve tarımsal ürün kaybının önlenmesinde kritik bir adımdır. Endikasyonlar bitkinin gövde, yaprak, lezyon ve meyve gibi kısımlarında bulunabilir. Belirtiler, yaprağın değişmesi ve üzerindeki beneklerin ortaya çıkmasıyla belli olur. Bu hastalık tanımlaması, ekstra zaman ve maliyet gerektirebilecek patojen tespiti için manuel inceleme yoluyla gerçekleştirilir. Dolayısıyla, bitki hastalıklarının otomatik tespiti tarım ekonomisinde hayati olabilir. Bu çalışma, elma yapraklarındaki anormallikleri tespit etmek ve bir ağaçta bir hastalığın varlığını veya yokluğunu doğru bir şekilde tahmin etmek üzere basit bir derin öğrenme modeli olan AlexNet'in kullanılmasını önermektedir. Derin Evrişimli Sinir Ağı modeli, uygun eğitim için 12,624 görüntüye yükseltilmiş PlantVillage veri kümesi kullanılarak uygulanmıştır. Hastalıklı elma yapraklarının görüntülerini sınıflandırmak için önerilen yöntem, %99.56'lık bir genel doğruluk elde etmiştir. Sonuçların karşılaştırılması için, farklı bir yöntem olan İkili İstatistiksel Görüntü Öznitelikleri (BSIF) uygulanmıştır. Ayrıca sonuçlar, literatürdeki benzer son teknoloji yaklaşımları kullanan çalışmalarla karşılaştırılmıştır.

Keywords

Elma yaprağı hastalıkları, tespit, derin öğrenme

Project Number

Not available.

References

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