Felix Olanrewaju BABALOLA, Nekabari Isabella KPAİ, Önsen TOYGAR

Elma Yaprağı Hastalıklarının AlexNet Kullanılarak Derin Öğrenme Tabanlı Sınıflandırılması

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

Deep Learning Based Classification of Apple Leaf Diseases Using AlexNet

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,

PDF

___

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