Plant Classification Method Using Histogram and Machine Learning for Smart Agriculture Applications

Öz

Due to its high potential and value, the Internet of things (IoT) has been used in various areas such as information security, industry 4.0, and smart agriculture. IoT is used in agriculture through the use of sensors, unmanned aerial vehicles (UAV), satellite technologies, robots, image processing, and artificial intelligence technologies. These smart agricultural practices increase production and quality and lead to savings in irrigation, thereby reducing environmental pollution during production. This study proposes an ultra-lightweight automated plant species classification method for smart agriculture applications. A UAV is used to acquire a new image dataset. An ultra-lightweight classification method is then used to classify the acquired plant species images. Our proposed ultra-lightweight computer vision model presents a histogram-based simple feature extraction function. The presented feature extractor uses histogram extraction and median filter in conjunction. The generated features are fed to two shallow classifiers, which are the support vector machine (SVM), and k nearest neighbor (KNN). The utilized SVM and KNN classifiers have attained 96.45% and 94.11% accuracies consecutively. The results demonstrate that this model is very capable of plant image classification and is ready for use in a physical agriculture environment.

Anahtar Kelimeler

• Plant classification
• Smart agriculture
• Histogram
• Feature extraction
• Machine learning

Akıllı Tarım Uygulamaları için Histogram ve Makine Öğrenimi Kullanan Bitki Sınıflandırma Yöntemi

Öz

Nesnelerin interneti (IoT) insanlık için çok değerli bir teknolojidir, dolayısıyla IoT bilgi güvenliği, endüstri 4.0, akıllı tarım gibi çeşitli alanlarda kullanılmaya başlanmıştır. Akıllı tarım uygulamaları sensörler, insansız hava araçları (İHA), uydu teknolojileri, robotlar, görüntü işleme ve yapay zekâ teknolojileri kullanılarak geliştirilmektedir. Akıllı tarım uygulamaları ile sulama alanında tasarruf sağlanmakta ve üretim sırasında çevre kirliliği azaltılmaktadır. Aynı zamanda üretimi ve kaliteyi arttırır. Bu çalışmada, akıllı tarım uygulamaları için ultra hafif otomatik bitki türleri sınıflandırma yöntemi geliştirilmiştir. Bir İHA kullanılarak yeni bir görüntü veri seti elde edilmiştir. Elde edilen bitki türleri görüntüsünü sınıflandırmak için ultra hafif bir sınıflandırma yöntemi önerilmiştir. Önerilen ultra hafif bilgisayarlı görü modelimizde, histogram tabanlı basit bir özellik çıkarma işlevi sunulmuştur. Sunulan öznitelik çıkarıcı, histogram çıkarımı ve medyan filtresi birlikte kullanılmıştır. Oluşturulan öznitelikler, destek vektör makinesi (SVM) ve k en yakın komşu (KNN) olan iki sığ sınıflandırıcıya beslenir. Kullanılan SVM ve KNN sınıflandırıcıları arka arkaya %96,45 ve %94,11 doğruluk elde etmiştir. Sonuçlar, bu modelin bitki görüntü sınıflandırması için oldukça başarılı olduğunu ve fiziksel tarım ortamında kullanıma hazır olduğunu göstermektedir.

Anahtar Kelimeler

• Bitki sınıflandırması
• Akıllı tarım
• Histogram
• Özellik çıkarma
• Makine öğrenmesi

Kaynakça

1. Adak, M. F. (2020). Identification of Plant Species by Deep Learning and Providing as A Mobile Application. Sakarya University Journal of Computer and Information Sciences, 3(3), 231-237. https://doi.org/10.35377/saucis.03.03.773465 google scholar
2. Atila, Ü., Uçar, M., Akyol, K., & Uçar, E. (2021). Plant leaf disease classification using EfficientNet deep learning model. Ecological Informatics, 61(October 2020), 101182. https://doi.org/10.1016/j.ecoinf.2020.101182 google scholar
3. Babayigit, B., & Büyükpatpat, B. (2019). Nesnelerin İnterneti Tabanlı Sulama ve Uzaktan İzleme Sisteminin Tasarımı ve Gerçekleştirimi. 13-19. google scholar
4. Dyrmann, M., Karstoft, H., & Midtiby, H. S. (2016). Plant species classification using deep convolutional neural network. Biosystems Engineering, 151(2005), 72-80. https://doi.org/10.1016/j.biosystemseng.2016.08.024 google scholar
5. El, I., Es-saady, Y., El, M., Mammass, D., & Benazoun, A. (2017). Automatic Recognition of Vegetable Crops Diseases based on Neural Network Classifier. International Journal of Computer Applications, 158(4), 48-51. https://doi.org/10.5120/ijca2017912796 google scholar
6. Goyal, N., Kumar, N., & Gupta, K. (2021). Lower-dimensional intrinsic structural representation of leaf images and plant recognition. Signal, Image and Video Processing. https://doi.org/10.1007/s11760-021-01983-6 google scholar
7. Hameed, K., Chai, D., & Rassau, A. (2018). A comprehensive review of fruit and vegetable classification techniques. Image and Vision Computing, 80, 24-44. https://doi.org/10.1016/j.imavis.2018.09.016 google scholar
8. Keivani, M., Mazloum, J., Sedaghatfar, E., & Tavakoli, M. B. (2020). Automated analysis of leaf shape, texture, and color features for plant classification. TraitementDu Signal, 37(1), 17-28. https://doi.org/10.18280/ts.370103 google scholar

9. Murtaza, F., Saba, U., Haroon Yousaf, M., & Viriri, S. (2020). Plant species identification using discriminant bag of words (DBoW). VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 5, 499-505. https://doi.org/10.5220/0009161004990505 google scholar
10. Pawara, P., Okafor, E., Schomaker, L., & Wiering, M. (2017). Data augmentation for plant classification. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10617 LNCS(September), 615-626. https://doi. org/10.1007/978-3-319-70353-4_52 google scholar
11. Pooja, V., Das, R., & Kanchana, V. (2018). Identification of plant leaf diseases using image processing techniques. Proceedings - 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development, TIAR 2017, 2018-Janua(February), 130-133. https://doi.org/10.1109/TIAR.2017.8273700 google scholar
12. Saleem, G., Akhtar, M., Ahmed, N., & Qureshi, W. S. (2019). Automated analysis of visual leaf shape features for plant classification. Computers and Electronics in Agriculture, 157(January), 270-280. https://doi.org/10.1016/j.compag.2018.12.038 google scholar
13. Selvam, L., & Kavitha, P. (2020). Classification of ladies finger plant leaf using deep learning. Journal of Ambient Intelligence and Humanized Computing, (0123456789). https://doi.org/10.1007/s12652-020-02671-y google scholar
14. Wang, J., Yang, J., Yu, L., Dong, H., & Wang, Y. (2021). DBA_SSD : A Novel End-to-End Object Detection Using Deep Attention Module for 1 Helping Smart Device with Vegetable and Fruit LeafPlant Disease Detection 2. google scholar
15. Xie, C., Yang, C., & He, Y. (2017). Hyperspectral imaging for classification of healthy and gray mold diseased tomato leaves with different infection severities. Computers and Electronics in Agriculture, 135, 154-162. https://doi.org/10.1016/j.compag.2016.12.015 google scholar
16. Yalcin, H., & Razavi, S. (2016). Plant classification using convolutional neural networks. 2016 5th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2016. https://doi.org/10.1109/Agro-Geoinformatics.2016.7577698 google scholar
17. Yaman, O., Ertam, F., Tuncer, T., & Firat Kilincer, I. (2020). Automated UHF RFID-based book positioning and monitoring method in smart libraries. IET Smart Cities, 2(4), 173-180. https://doi.org/10.1049/iet-smc.2020.0033 google scholar
18. Yaman, O., & Tuncer, T. (2021). Ensemble NASNet Deep Feature Generator Based Underwater Acoustic Classification Model. Veri Bilimi, 4(2), 33-39. google scholar
19. Zhu, X., Zhu, M., & Ren, H. (2018). Method of plant leaf recognition based on improved deep convolutional neural network. Cognitive Systems Research, 52, 223-233. https://doi.org/10.1016/j.cogsys.2018.06.008 google scholar