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Google.com Destekli Öğretilebilir Makine Kullanılarak Elma Hastalıklarının Sınıflandırılması

Yıl 2024, , 66 - 71, 31.08.2024

Mehmet Serhat Odabas , Nurettin Şenyer , Semih Osman Saka

https://doi.org/10.55507/gopzfd.1287389

Öz

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.

Anahtar Kelimeler

Bitki Hastalığı Tespiti Makine öğrenimi Öğretilebilir Makine.

Kaynakça

  • 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
  • Akbas B (2019). Plant health's place in sustainable agriculture. Journal of Agriculture Engineering (368): 6–13.
  • 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
  • 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.
  • 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
  • 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.
  • 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
  • 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
  • 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.
  • 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).
  • 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<2072::AID-ART357>3.0.CO;2-3
  • 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
  • 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
  • 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
  • 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
  • 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 
  • 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
  • Google. (2023). Teachable Machine. https://teachablemachine.withgoogle.com
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • Saka SO (2022). Github. https://github.com: https://sosaka0.github.io/Apple-disease/.
  • 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
  • Senel FA (2020). Classification of Apricot Kernels by using Machine Learning Algorithms. Bitlis Eren University Journal of Science 9(2): 807–15.
  • 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
  • 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
  • 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
  • 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

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

Yıl 2024, , 66 - 71, 31.08.2024

Mehmet Serhat Odabas , Nurettin Şenyer , Semih Osman Saka

https://doi.org/10.55507/gopzfd.1287389

Öz

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.

Anahtar Kelimeler

Plant Disease Detection Machine learning Teachable Machine.

Kaynakça

  • 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
  • Akbas B (2019). Plant health's place in sustainable agriculture. Journal of Agriculture Engineering (368): 6–13.
  • 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
  • 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.
  • 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
  • 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.
  • 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
  • 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
  • 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.
  • 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).
  • 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<2072::AID-ART357>3.0.CO;2-3
  • 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
  • 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
  • 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
  • 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
  • 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 
  • 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
  • Google. (2023). Teachable Machine. https://teachablemachine.withgoogle.com
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • Saka SO (2022). Github. https://github.com: https://sosaka0.github.io/Apple-disease/.
  • 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
  • Senel FA (2020). Classification of Apricot Kernels by using Machine Learning Algorithms. Bitlis Eren University Journal of Science 9(2): 807–15.
  • 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
  • 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
  • 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
  • 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
Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ziraat Mühendisliği
Bölüm Araştırma Makaleleri
Yazarlar

Mehmet Serhat Odabas 0000-0002-1863-7566

Nurettin Şenyer 0000-0002-2324-9285

Semih Osman Saka 0000-0002-6241-5485

Yayımlanma Tarihi 31 Ağustos 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Odabas, M. S., Şenyer, N., & Saka, S. O. (2024). Classification of Apple Diseases and Pests using The Google.com Powered Teachable Machine. Journal of Agricultural Faculty of Gaziosmanpaşa University (JAFAG), 41(2), 66-71. https://doi.org/10.55507/gopzfd.1287389