Deep Learning Based Classification of Apple Leaf Diseases Using AlexNet

Yıl 2023, Cilt: IDAP-2023 : International Artificial Intelligence and Data Processing Symposium Sayı: IDAP-2023, 67 - 74, 18.10.2023

Felix Olanrewaju Babalola , Nekabari Isabella Kpai , Önsen Toygar

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

Öz

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.

Anahtar Kelimeler

Apple leaf diseases, detection, deep learning

Destekleyen Kurum

Not available.

Proje Numarası

Not available.

Teşekkür

Not available.

Elma Yaprağı Hastalıklarının AlexNet Kullanılarak Derin Öğrenme Tabanlı Sınıflandırılması

Öz

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.

Anahtar Kelimeler

Elma yaprağı hastalıkları, tespit, derin öğrenme

Proje Numarası

Not available.

Kaynakça

• Alqethami S, Almtanni B, Alzhrani W, Alghamdi M. (2022). Disease detection in apple leaves using image processing techniques. Engineering, Technology & Applied Science Research, 12(2), 8335–8341. https://doi.org/10.48084/etasr.4721
• Babalola FO, Bitirim Y, Toygar Ö. (2020). Palm vein recognition through fusion of texture-based and CNN-based methods. Signal, Image and Video Processing, 15(3), 459–466. https://doi.org/10.1007/s11760-020-01765-6
• Chakraborty S, Paul S, Rahat-uz-Zaman Md. (2021). Prediction of Apple leaf diseases using multiclass support vector machine. 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST). https://doi.org/10.1109/icrest51555.2021.9331132
• Dutot M, Nelson LM, Tyson R.C. (2013). Predicting the spread of postharvest disease in stored fruit, with application to apples. Postharvest Biology and Technology, 85, 45–56.
• Es-saady Y, El Massi I, El Yassa M, Mammass D, Benazoun A. (2016). Automatic recognition of plant leaves diseases based on serial combination of two SVM classifiers. 2016 International Conference on Electrical and Information Technologies (ICEIT). https://doi.org/10.1109/eitech.2016.7519661
• Fu L, Li S, Sun Y, Mu Y, Hu T, Gong H. (2022). Lightweight-convolutional neural network for Apple Leaf Disease Identification. Frontiers in Plant Science, 13. https://doi.org/10.3389/fpls.2022.831219
• Islam M, Anh Dinh, Wahid K, Bhowmik P. (2017). Detection of potato diseases using image segmentation and multiclass support vector machine. 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE). https://doi.org/10.1109/ccece.2017.7946594
• Kannala J, Rahtu E. (2012). BSIF: Binarized statistical image features, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), Tsukuba, Japan, pp. 1363-1366.
• Krizhevsky A, Sutskever I, Hinton GE. (2017). ImageNet classification with deep convolutional Neural Networks. Communications of the ACM, 60(6), 84–90. https://doi.org/10.1145/3065386
• Liu B, Zhang Y, He D, Li Y. (2017). Identification of Apple leaf diseases based on deep convolutional neural networks. Symmetry, 10(1), 11. https://doi.org/10.3390/sym10010011
• Meyyappan S, Chandramouleeswaran S. (2018). Plant Infection Detection Using Image Processing. International Journal of Modern Engineering Research (IJMER). 8. 2249-6645.
• Qin F, Liu D, Sun B, Ruan L, Ma Z, Wang H. (2016). Identification of alfalfa leaf diseases using image recognition technology. PLOS ONE, 11(12).
• Rothe PR., Kshirsagar RV. (2015). Cotton leaf disease identification using pattern recognition techniques. 2015 International Conference on Pervasive Computing (ICPC). https://doi.org/10.1109/pervasive.2015.7086983
• Sannakki SS., Rajpurohit VS, Nargund VB, Kulkarni P. (2013). Diagnosis and classification of grape leaf diseases using neural networks. 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT). https://doi.org/10.1109/icccnt.2013.6726616
• Wang G, Sun Y, Wang J. (2017). Automatic image-based plant disease severity estimation using Deep Learning. Computational Intelligence and Neuroscience, 2017, 1–8. https://doi.org/10.1155/2017/2917536
• Yan Q, Yang B, Wang W, Wang B, Chen P, Zhang J. (2020). Apple leaf diseases recognition based on an improved convolutional neural network. Sensors, 20(12), 3535.
• Yu H. Cheng X. Chen C. Heidari A A. Liu J. Cai Z. & Chen H. (2022). Apple leaf disease recognition method with improved residual network. Multimedia Tools and Applications, 81(6), 7759–7782. https://doi.org/10.1007/s11042-022-11915-2