SavedAdd your journal

Journals / Gaziosmanpaşa Üniversitesi Ziraat Fakültesi Dergisi / 2024 / Cilt: 41 Sayı: 2

Classification of Apple Diseases and Pests using The Google.com Powered Teachable Machine

Google.com Destekli Öğretilebilir Makine Kullanılarak Elma Hastalıklarının Sınıflandırılması

Mehmet Serhat Odabas(ONDOKUZ MAYIS ÜNİVERSİTESİ)0000-0002-1863-7566
Nurettin Şenyer(SAMSUN ÜNİVERSİTESİ)0000-0002-2324-9285
Semih Osman Saka(SAMSUN UNIVERSITY)0000-0002-6241-5485
Pages
66–71
Publication Date
2024-08-31
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this model, results of over 95% were obtained in diseases.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Machine learning and deep learning methods are used in the classification of plant diseases.
📊
Key Findings & Data:It takes a long time to extract features in machine learning.
💡
Conclusion & Impact:In this model, results of over 95% were obtained in diseases.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Bitki Hastalığı Tespiti · Makine öğrenimi · Öğretilebilir Makine.

Abstract

Machine learning and deep learning methods are used in the classification of plant diseases. It takes a long time to extract features in machine learning. In deep learning, computers are required to process big data depending on the size of the data set. With Google Teachable Machine, faster results can be obtained without the need for feature extraction or very powerful computers. For this purpose, a model was created with four apple diseases using the data set related to apple diseases. In this model, results of over 95% were obtained in diseases.

Özet

Bitki hastalıklarının sınıflandırılmasında makine öğrenimi ve derin öğrenme yöntemleri kullanılmaktadır. Makine öğreniminde özellikleri çıkarmak uzun zaman alıyor. Derin öğrenmede, veri kümesinin boyutuna bağlı olarak bilgisayarların büyük verileri işlemesi gerekir. Google öğretilebilir makina ile özellik çıkarımına veya çok güçlü bilgisayarlara ihtiyaç duymadan daha hızlı sonuçlar alınabilir. Bu amaçla elma hastalıkları ile ilgili veri seti kullanılarak dört elma hastalığı ile model oluşturulmuştur. Bu modelde hastalıklarda %95'in üzerinde sonuçlar elde edilmiştir.

🔖

Cite This Article

Odabas, M. S., Şenyer, N., Saka, S. O., *, M. S. O. (2024). Classification of Apple Diseases and Pests using The Google.com Powered Teachable Machine. Gaziosmanpaşa Üniversitesi Ziraat Fakültesi Dergisi, Cilt: 41 Sayı: 2, 66–71. https://doi.org/10.55507/gopzfd.1287389

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (35)

  1. Agustian D, Pertama P, Crisnapati PN, & Novayanti PD (2021). Implementation of Machine Learning Using Google’s Teachable Machine Based on Android. In 2021 3rd International Conference on Cybernetics and Intelligent System (ICORIS), IEEE, 1–7. 10.1109/ICORIS52787.2021.9649528
  2. Akbas B (2019). Plant health's place in sustainable agriculture. Journal of Agriculture Engineering (368): 6–13.
  3. Aksoy B, Halis HD, & Salman OKM (2020). Detection of Diseases in Apple Plant with Artificial Intelligence Methods and Comparison of the Performance of Artificial Intelligence Methods. International Journal of Engineering and Innovative Research 2(3): 194–210. https://doi.org/10.47933/ijeir.772514
  4. Amidi AA (2022). Deep Learning tricks and tips handbook. https://stanford.edu/~shervine/l/tr/teaching/cs-230/cheatsheet-deep-learning-tips-and-tricks#running-nn 2022.
  5. Aslan M (2021). Detection of Peach Diseases with Deep Learning. European Journal of Science and Technology (23): 540–46. https://doi.org/10.31590/ejosat.883787
  6. Aqil M. Tabri1 F, Andayani NN, Panikkai S, Suwardi ER, Bunyamin Z, Azrai M, & Ratuleet T (2021). Integration of Smartphone Technology for Maize Recognition. IOP Conference Series: Earth and Environmental Science 911(1): 012037.
  7. Bansal P, Kumar R, & Kumar S. (2021). Disease detection in apple leaves using deep convolutional neural network. Agriculture 11(7): 617. https://doi.org/10.3390/agriculture11070617
  8. Bashimov G (2016). Comparative Advantage of Turkey in Apple Exports. Journal of Adnan Menderes University Agricultural Faculty 13(2): 9 – 15. https://doi.org/10.25308/aduziraat.293391
  9. Boyaci S. & Çağlar S. (2009). A Study on The Production of Branched Apple Tree Under Nursery Condition in Turkey. The Journal of Agricultural Sciences 2(1): 107–111.
  10. Bracino AA, Concepcion RS, Bedruz RAR, Dadios EP, Vicerra RRP. (2020). Development of a hybrid machine learning model for apple (Malus domestica) health detection and disease classification. In 2020 IEEE 12th international conference on humanoid, nanotechnology, information technology, communication and control, environment, and management (HNICEM) (pp. 1-6).
  11. Burgkart R, Glaser C, Hyhlik‐Dürr A, Englmeier KH, Reiser M, & Eckstein F. (2001). Magnetic resonance imaging–based assessment of cartilage loss in severe osteoarthritis: accuracy, precision, and diagnostic value. Arthritis & Rheumatism: Official Journal of the American College of Rheumatology 44(9): 2072-2077. https://doi.org/10.1002/1529-0131(200109)44:9 3.0.CO;2-3
  12. Caliskan O, Kurt D, Temizel KE, & Odabas MS (2017). Effect of Salt Stress and Irrigation Water on Growth and Development of Sweet Basil (Ocimum basilicum L.). Open Agriculture 2(1): 589-594. https://doi.org/10.1515/opag-2017-0062
  13. Chammem N, Issaqui M, De Almedia AID, & Delgado AM (2018). Food Crises and Food Safety Incidents in European Union, United States, and Maghreb Area: Current Risk Communication Strategies and New Approaches. Journal of AOAC International 101(4): 923-938. 10.5740/jaoacint.17-0446
  14. Chao X, Sun G, Zhao H, Li M, & He D. (2020). Identification of apple tree leaf diseases based on deep learning models. Symmetry 12(7): 1065. https://doi.org/10.3390/sym12071065
  15. Dammer KH, Intreß J, Schirrmann M, & Garz A (2019). Growth Behavior of Ragweed (Ambrosia artemisiifolia L.) on Agricultural Land in Brandenburg (Germany) Conclusions for Image Analysis in Camera Based Monitoring Strategies. Gesunde Pflanzen 71: 227–235. https://doi.org/10.1007/s10343-019-00488-0
  16. Doolotkeldieva T, & Bobusheva S (2017). Scab Disease Caused by Venturia inaequalis on Apple Trees in Kyrgyzstan and Biological Agents to Control This Disease. Advances in Microbiology 7: 450-466. 10.4236/aim.2017.76035
  17. Fréchette B, Cormier D, Chouinard G, Vanoosthuyse F, & Lucas É (2008). Apple aphid, Aphis spp. (Hemiptera: Aphididae), and predator populations in an apple orchard at the non-bearing stage: The impact of ground cover and cultivar. European Journal of Entomology 105: 521–529. 10.14411/eje.2008.069
  18. Google. (2023). Teachable Machine. https://teachablemachine.withgoogle.com
  19. Gupta YM, & Homchan S (2021). Insect Detection Using a Machine Learning Model. Nusantara Bioscience 13(1): 68–72. https://doi.org/10.13057/nusbiosci/n130110
  20. Jasim YA (2021). High-Performance Deep Learning to Detection and Tracking Tomato Plant Leaf Predict Disease and Expert Systems. Advances in Distributed Computing and Artificial Intelligence Journal 10(2): 97–122. https://doi.org/10.14201/ADCAIJ202110297122
  21. Kacar G (2019). Bioecologies of Pests, Natural Enemies in apple orchards of Seben (Bolu). International Journal of Agriculture and Wildlife Science 5(2): 286 – 291. 10.24180/ijaws.605651
  22. Kala KU, Nandhini M, Thangadarshini M, Chakkravarthi MK, & Verma M. (2023). Leveraging Deep Learning for Effective Pest Management in Plantain Tree Cultivation. In International Conference on Soft Computing and Signal Processing (pp. 425-434). Singapore: Springer Nature Singapore. 10.1007/978-981-99-8628-6_36
  23. Khan AI, Quadri SMK, & Banday S. (2021). Deep learning for apple diseases: classification and identification. Int. Journal of Computational Intelligence Studies 10(1):1-12 https://doi.org/10.1016/j.compag.2022.107093
  24. Lordan J, Alegre S, Gatius F, Sarasúa MJ, & Alins G (2015). Woolly apple aphid Eriosoma lanigerum Hausmann ecology and its relationship with climatic variables and natural enemies in Mediterranean areas. Bulletin of Entomological Research 105(1): 60-69. 10.1017/S0007485314000753
  25. Mesías-Ruiz GA, Pérez-Ortiz M, Dorado J, De Castro AI, & Peña JM. (2023). Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review. Frontiers in Plant Science 14: 1143326. 10.3389/fpls.2023.1143326
  26. Odabas MS, Radusiene J, Karpaviciene B, & Camas N (2015). Prediction model of the effect of light intensity on phenolic contents in Hypericum triquetrifolium turra. Bulgarian Chemical Communications 47(2):467-471.
  27. Odabas MS, Kayhan G, Ergun E, & Senyer N (2016). Using Artificial Neural Network and Multiple Linear Regression for Predicting the Chlorophyll Concentration Index of Saint John's Wort Leaves. Communications in Soil Science and Plant Analysis 47(2): 237-245. http://dx.doi.org/10.1080/00103624.2015.1104342
  28. Odabas MS, Senyer N, Kayhan G, & Ergun E (2017). Estimation of Chlorophyll Concentration Index at Leaves using Artificial Neural Networks. Journal of Circuits Systems and Computers 26(2): 1750026. 10.1142/S0218126617500268
  29. Saka SO (2022). Github. https://github.com: https://sosaka0.github.io/Apple-disease/.
  30. Storey G, Meng Q, & Li B. (2022). Leaf disease segmentation and detection in apple orchards for precise smart spraying in sustainable agriculture. Sustainability 14(3):1458. https://doi.org/10.3390/su14031458
  31. Senel FA (2020). Classification of Apricot Kernels by using Machine Learning Algorithms. Bitlis Eren University Journal of Science 9(2): 807–15.
  32. Spitaler U, Pfeifer A, Deltedesco E, Hauptkorn S, & Oettl S (2022). Detection of Monilinia spp. by a multiplex real‑time PCR assay and first report of Monilinia fructicola in South Tyrol (northern Italy). Journal of Plant Diseases and Protection 129:1013–1020.https://doi.org/10.1007/s41348-022-00614-7
  33. Thakur PS, Khanna P, Sheorey T, & Ojha A. (2022). Trends in vision-based machine learning techniques for plant disease identification: A systematic review. Expert Systems with Applications 208: 118117. https://doi.org/10.1016/j.eswa.2022.118117
  34. Turkoglu M, Yanikoglu B, & Hanbay D (2021). PlantDiseaseNet: Convolutional Neural Network Ensemble for Plant Disease and Pest Detection. Signal, Image and Video Processing 16:301-309. https://doi.org/10.1007/s11760-021-01909-2
  35. Turkoglu M, Hanbay K, Sivrikaya IS, & Hanbay D (2020). Classification of Apricot Diseases by Using Deep Convolution Neural Network. Bitlis Eren University Journal of Science 9(1): 334–45. https://doi.org/10.17798/bitlisfen.562101