Research Article
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Potato Plant Leaf Disease Detection Using Deep Learning Method

Year 2024, Volume: 30 Issue: 1, 153 - 165, 09.01.2024

Cemal İhsan Sofuoğlu Derya Bırant

https://doi.org/10.15832/ankutbd.1276722

Abstract

In agriculture, plant disease detection and cures for those diseases are crucial for high crop production and yield sustainably. Improvements in the automated disease detection and analysis areas may provide important benefits for early action that would allow intervention at earlier stages for cure and preventing spread of the disease. As a result, damages on crop yield could be minimized. This study proposes a new deep-learning model that correctly classifies plant leaf diseases for the agriculture and food sectors. It focuses on the detection of plant diseases for potato leaves from images by designing a new convolutional neural network (CNN) architecture. The CNN methodology applies filters to input images, extracts key features, reduces dimensions while preserving important characteristics in images, and finally, performs classification. The experimental results conducted on a real-world dataset showed that a significant improvement (8.6%) in accuracy was achieved on average by the proposed model (98.28%) compared to the state-of-the-art models (89.67%) in the literature. The weighted averages of recall, precision, and f-score metrics were obtained around 0.978, meaning that the method was highly successful in disease diagnosis.

Keywords

Agriculture Disease diagnosis PlantVillage Smart farming Image classification Deep learning Convolutional neural networks

References

  • Ahmad W, Shah S M A & Irtaza A (2020). Plants disease phenotyping using quinary patterns as texture descriptor. KSII Transactions on Internet and Information Systems 14(8): 3312-3327. doi.org/10.3837/tiis.2020.08.009
  • Ahmed I & Yadav P K (2023). A systematic analysis of machine learning and deep learning based approaches for identifying and diagnosing plant diseases. Sustainable Operations and Computers 4: 96-104. doi.org/10.1016/j.susoc.2023.03.001
  • Aparajita A, Sharma R, Singh A, Dutta M K, Riha K & Kriz P (2017). Image processing based automated identification of late blight disease from leaf images of potato crops. In: Proceedings of the 40th International Conference on Telecommunications and Signal Processing (TSP), 05-07 July, Barcelona, Spain, pp. 758-762. doi.org/10.1109/tsp.2017.8076090
  • Atik I (2022). Classification of plant leaf diseases using deep learning methods. Kahramanmaras Sutcu Imam University Journal of Engineering Sciences 25(2): 126-137. (In Turkish) doi.org/10.17780/ksujes.1096541
  • Aurangzeb K, Akmal F, Khan M A, Sharif M & Javed M Y (2020). Advanced machine learning algorithm based system for crops leaf diseases recognition. In: Proceedings of the 6th Conference on Data Science and Machine Learning Applications (CDMA), 4-5 March, Riyadh, Saudi Arabia, pp. 146-151. doi.org/10.1109/cdma47397.2020.00031
  • Bayram F & Yıldız M (2023). Classification of some barley cultivars with deep convolutional neural networks. Journal of Agricultural Sciences (Tarim Bilimleri Dergisi) 29(1): 262-271. doi.org/10.15832/ankutbd.815230
  • Bhagat M & Kumar D (2023). Efficient feature selection using BoWs and SURF method for leaf disease identification. Multimedia Tools and Applications 82: 28187–28211. doi.org/10.1007/s11042-023-14625-5
  • Chaitanya P K & Yasudha K (2020). Image based plant disease detection using convolution neural networks algorithm. International Journal of Innovative Science and Research Technology 5(5): 331-334
  • Ciran A & Özbay E (2022). Classification of maize leaf diseases by fusion of pre-trained CNN architectures. European Journal of Science and Technology 44: 74-83. (In Turkish) doi.org/10.31590/ejosat.1216356
  • Çınar İ & Koklu M (2022). Identification of rice varieties using machine learning algorithms. Journal of Agricultural Sciences (Tarim Bilimleri Dergisi) 28(2): 307-325. doi.org/10.15832/ankutbd.862482
  • Dikici B, Bekçioğulları M F, Açıkgöz H & Korkmaz D (2022). Performance investigation of pre-trained convolutional neural networks in olive leaf disease classification. Konya Journal of Engineering Sciences 10(3): 535-547. (In Turkish) doi.org/10.36306/konjes.1078358
  • Ertem S & Özbay E (2022). Disease detection in tomato leaf images by deep feature combination approach in classification problem. European Journal of Science and Technology 44: 84-92. (In Turkish) doi.org/10.31590/ejosat.1216380
  • Ferentinos K P (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture 145: 311-318. doi.org/10.1016/j.compag.2018.01.009
  • Gerdan Koc D, Koc C & Vatandas M (2022). Diagnosis of tomato plant diseases using pre-trained architectures and a proposed convolutional neural network model. Journal of Agricultural Sciences (Tarim Bilimleri Dergisi) 29(2): 627-638. doi.org/10.15832/ankutbd.957265
  • Ghosh A & Roy P (2021). AI Based automated model for plant disease detection, a deep learning approach. Communications in Computer and Information Science 1406: 199-213. doi.org/10.1007/978-3-030-75529-4_16
  • Guo Y, Fang Z, Zhang S & Dong H (2021). Classification of potato early blight with unbalanced data based on GhostNet. In: Proceedings of the 3rd International Academic Exchange Conference on Science and Technology Innovation (IAECST), 10-12 December, Guangzhou, China, pp. 559-563. doi.org/10.1109/iaecst54258.2021.9695532
  • He Y, Gao Q & Ma Z (2022). A crop leaf disease image recognition method based on bilinear residual networks. Mathematical Problems in Engineering, 2022: 1-15. doi.org/10.1155/2022/2948506 Hughes D P & Salathe M (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. ArXiv, arXiv:1511.08060. https://arxiv.org/pdf/1511.08060
  • Islam M, Dinh A, Wahid K A & Bhowmik P K (2017). Detection of potato diseases using image segmentation and multiclass support vector machine. In: Proceedings of the Canadian Conference on Electrical and Computer Engineering, 30 April-3 May, Windsor, ON, Canada, pp. 1-4. doi.org/10.1109/ccece.2017.7946594
  • Ismail W, Khan M A, Shah S A, Javed M Y, Rehman A & Saba T (2020). An adaptive image processing model of plant disease diagnosis and quantification based on color and texture histogram. In: Proceedings of the 2nd International Conference on Computer and Information Sciences (ICCIS), 13-15 October, Sakaka, Saudi Arabia, pp. 1-6. doi.org/10.1109/iccis49240.2020.9257650
  • Iqbal M A & Talukder K H (2020). Detection of potato disease using image segmentation and machine learning. In: Proceedings of the International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), 4-6 August, Chennai, India, pp. 43-47. doi.org/10.1109/wispnet48689.2020.9198563
  • Jasim M A & Al-Tuwaijari J M (2020). Plant leaf diseases detection and classification using image processing and deep learning techniques. In: Proceedings of the International Conference on Computer Science and Software Engineering (CSASE), 16-18 April, Duhok, Iraq, pp. 259-265. doi.org/10.1109/csase48920.2020.9142097
  • Jeyalakshmi S & Radha R (2020). An effective approach to feature extraction for classification of plant diseases using machine learning. Indian Journal of Science and Technology 13(32): 3295-3314. doi.org/10.17485/ijst/v13i32.827
  • Kaur N & Devendran Dr V (2021). Plant leaf disease detection using ensemble classification and feature extraction. Turkish Journal of Computer and Mathematics Education 12(11): 2339-2352.
  • Kumar A & Patel V K (2023). Classification and identification of disease in potato leaf using hierarchical based deep learning convolutional neural network. Multimedia Tools and Applications 82: 31101–31127. doi.org/10.1007/s11042-023-14663-z
  • Kurmi Y & Gangwar S (2022). A leaf image localization based algorithm for different crops disease classification. Information Processing in Agriculture 9(3): 456-474. doi.org/10.1016/j.inpa.2021.03.001 Mathew A, Antony A, Mahadeshwar Y, Khan T & Kulkarni A (2022). Plant disease detection using GLCM feature extractor and voting classification approach. Materials Today: Proceedings 58(1): 407-415. doi.org/10.1016/j.matpr.2022.02.350
  • Mahum R, Munir H, Mughal Z, Awais M, Khan F S, Saqlain M, Mahamad S & Tlili I (2023). A novel framework for potato leaf disease detection using an efficient deep learning model. Human and Ecological Risk Assessment 29(2): 303-326. doi.org/10.1080/10807039.2022.2064814
  • Moharekar D T T, Pol D U R, Ombase R & Moharekar T J (2022). Detection and classification of plant leaf diseases using convolution neural networks and streamlit. International Research Journal of Modernization in Engineering Technology and Science 4(7): 4305-4309.
  • Monowar M M, Hamid A, Kateb F, Ohi A Q & Mridha M F (2022). Self-supervised clustering for leaf disease identification. Agriculture 12(6): 1-14. doi.org/10.3390/agriculture12060814
  • Mukherjee A (2020). Analysis of diseased leaf images using digital image processing techniques and SVM classifier and disease severity measurements using fuzzy logic. International Journal of Scientific & Engineering Research 11(9): 1905–1912. doi.org/10.14299/ijser.2020.08.12
  • Nanehkaran Y A, Zhang D, Chen J, Tian Y & Al-Nabhan N (2023). Recognition of plant leaf diseases based on computer vision. Journal of Ambient Intelligence and Humanized Computing, in press. doi.org/10.1007/s12652-020-02505-x
  • Oppenheim D & Shani G (2017). Potato disease classification using convolution neural networks. Advances in Animal Biosciences 8(2): 244-249. doi.org/10.1017/s2040470017001376
  • Pardede H F, Suryawati E, Sustika R & Zilvan V (2018). Unsupervised convolutional autoencoder-based feature learning for automatic detection of plant diseases. In: Proceedings of the International Conference on Computer, Control, Informatics and Its Applications (IC3INA), 1-2 November, Tangerang, Indonesia, pp. 158-162. doi.org/10.1109/ic3ina.2018.8629518
  • Patil P, Yaligar N & Meena S (2017). Comparision of performance of classifiers - SVM, RF and ANN in potato blight disease detection using leaf images. In: Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 14-16 December, Coimbatore, India, pp. 1-5. doi.org/10.1109/iccic.2017.8524301
  • Prajna U (2021). Detection and classification of grain crops and legumes disease: a survey. Sparklinglight Transactions on Artificial Intelligence and Quantum Computing 1(1): 41-55. doi.org/10.55011/staiqc.2021.1105
  • Rozaqi A J, Arief M R & Sunyoto A (2021). Implementation of transfer learning in the convolutional neural network algorithm for identification of potato leaf disease. Procedia of Engineering and Life Science 1(1): 1-9. doi.org/10.21070/pels.v1i1.820
  • Sabzi S, Abbaspour-gilandeh Y, Abbaspour-gılandeh Y, Javadıkıa H, Javadikia H, Havaskhan H & Havaskhan H (2015). Automatic grading of emperor apples based on image processing and ANFIS. Journal of Agricultural Sciences 21(3): 326-336. doi.org/10.1501/tarimbil_0000001335
  • Sabzi S, Abbaspour Gılandeh Y & Javadıkıa H (2018). Developing a machine vision system to detect weeds from potato plant. Journal of Agricultural Sciences 24(1): 105-118. doi.org/10.15832/ankutbd.446402
  • Saeed F, Khan M A, Sharif M, Mittal M, Goyal L M & Roy S (2021). Deep neural network features fusion and selection based on PLS regression with an application for crops diseases classification. Applied Soft Computing 103: 1-15. doi.org/10.1016/j.asoc.2021.107164
  • Salih T A, Ali A J & Ahmed M N (2020). Deep learning convolution neural network to detect and classify tomato plant leaf diseases. Open Access Library Journal 7(5): 1-12. doi.org/10.4236/oalib.1106296
  • Sanjeev K, Gupta N K, Jeberson W J & Paswan S (2021). Early prediction of potato leaf diseases using ANN classifier. Oriental Journal of Computer Science and Technology 13(2): 129-134. doi.org/10.13005/ojcst13.0203.11
  • Sarker M R K R, Borsha N A, Sefatullah M, Khan A R, Jannat S & Ali H (2022). A deep transfer learning-based approach to detect potato leaf disease at an earlier stage. In: Proceedings of the Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 21-22 April, Bhilai, India, pp. 1-5. doi.org/10.1109/icaect54875.2022.9807963
  • Saygılı A (2023). The efficiency of transfer learning and data augmentation in lemon leaf image classification. European Journal of Engineering and Applied Sciences 6(1): 32-40. doi.org/10.55581/ejeas.1321042
  • Sharma S, Anand V & Singh S (2021). Classification of diseased potato leaves using machine learning. In: Proceedings of the 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), 18-19 June, Bhopal, India, pp. 554-559. doi.org/10.1109/csnt51715.2021.9509702
  • Shwetha K S & Sneha S P (2022). Machine learning techniques for potato leaf disease. International Research Journal of Modernization in Engineering Technology and Science 4(7): 434-441.
  • Singh A & Kaur H (2021). Potato plant leaves disease detection and classification using machine learning methodologies. IOP Conference Series: Materials Science and Engineering 1022(1): 1-9. doi.org/10.1088/1757-899x/1022/1/012121
  • Singh J & Kaur H (2019). Plant disease detection based on region-based segmentation and KNN classifier. Lecture Notes in Computational Vision and Biomechanics 30: 1667-1675. doi.org/10.1007/978-3-030-00665-5_154 Sladojevic S, Arsenovic M, Anderla A, Culibrk D & Stefanovic D (2016). Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience 2016: 1-11. doi.org/10.1155/2016/3289801
  • Swetha V & Jayaram R (2019). A novel method for plant leaf malady recognition using machine learning classifiers. In: Proceedings of the 3rd International Conference on Electronics, Communication and Aerospace Technology (ICECA), 12-14 June, Coimbatore, India, pp. 1360-1365. doi.org/10.1109/iceca.2019.8822094
  • Tiwari D, Ashish M, Gangwar N, Sharma A, Patel S & Bhardwaj S (2020). Potato leaf diseases detection using deep learning. In: Proceedings of the 4th International Conference on Intelligent Computing and Control Systems (ICICCS), 13-15 May, Madurai, India, pp. 461-466. doi.org/10.1109/iciccs48265.2020.9121067
  • Türkoğlu M, Hanbay K, Saraç Sivrikaya I. & Hanbay D (2020). Classification of apricot diseases by using deep convolution neural network. Bitlis Eren University Journal of Science 9(1): 334-345. (In Turkish) doi.org/10.17798/bitlisfen.562101
  • Wagle S A & Harikrishnan R (2021). Comparison of plant leaf classification using modified AlexNet and support vector machine. Traitement Du Signal 38(1): 79-87. doi.org/10.18280/ts.380108

Year 2024, Volume: 30 Issue: 1, 153 - 165, 09.01.2024

Cemal İhsan Sofuoğlu Derya Bırant

https://doi.org/10.15832/ankutbd.1276722

Abstract

References

  • Ahmad W, Shah S M A & Irtaza A (2020). Plants disease phenotyping using quinary patterns as texture descriptor. KSII Transactions on Internet and Information Systems 14(8): 3312-3327. doi.org/10.3837/tiis.2020.08.009
  • Ahmed I & Yadav P K (2023). A systematic analysis of machine learning and deep learning based approaches for identifying and diagnosing plant diseases. Sustainable Operations and Computers 4: 96-104. doi.org/10.1016/j.susoc.2023.03.001
  • Aparajita A, Sharma R, Singh A, Dutta M K, Riha K & Kriz P (2017). Image processing based automated identification of late blight disease from leaf images of potato crops. In: Proceedings of the 40th International Conference on Telecommunications and Signal Processing (TSP), 05-07 July, Barcelona, Spain, pp. 758-762. doi.org/10.1109/tsp.2017.8076090
  • Atik I (2022). Classification of plant leaf diseases using deep learning methods. Kahramanmaras Sutcu Imam University Journal of Engineering Sciences 25(2): 126-137. (In Turkish) doi.org/10.17780/ksujes.1096541
  • Aurangzeb K, Akmal F, Khan M A, Sharif M & Javed M Y (2020). Advanced machine learning algorithm based system for crops leaf diseases recognition. In: Proceedings of the 6th Conference on Data Science and Machine Learning Applications (CDMA), 4-5 March, Riyadh, Saudi Arabia, pp. 146-151. doi.org/10.1109/cdma47397.2020.00031
  • Bayram F & Yıldız M (2023). Classification of some barley cultivars with deep convolutional neural networks. Journal of Agricultural Sciences (Tarim Bilimleri Dergisi) 29(1): 262-271. doi.org/10.15832/ankutbd.815230
  • Bhagat M & Kumar D (2023). Efficient feature selection using BoWs and SURF method for leaf disease identification. Multimedia Tools and Applications 82: 28187–28211. doi.org/10.1007/s11042-023-14625-5
  • Chaitanya P K & Yasudha K (2020). Image based plant disease detection using convolution neural networks algorithm. International Journal of Innovative Science and Research Technology 5(5): 331-334
  • Ciran A & Özbay E (2022). Classification of maize leaf diseases by fusion of pre-trained CNN architectures. European Journal of Science and Technology 44: 74-83. (In Turkish) doi.org/10.31590/ejosat.1216356
  • Çınar İ & Koklu M (2022). Identification of rice varieties using machine learning algorithms. Journal of Agricultural Sciences (Tarim Bilimleri Dergisi) 28(2): 307-325. doi.org/10.15832/ankutbd.862482
  • Dikici B, Bekçioğulları M F, Açıkgöz H & Korkmaz D (2022). Performance investigation of pre-trained convolutional neural networks in olive leaf disease classification. Konya Journal of Engineering Sciences 10(3): 535-547. (In Turkish) doi.org/10.36306/konjes.1078358
  • Ertem S & Özbay E (2022). Disease detection in tomato leaf images by deep feature combination approach in classification problem. European Journal of Science and Technology 44: 84-92. (In Turkish) doi.org/10.31590/ejosat.1216380
  • Ferentinos K P (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture 145: 311-318. doi.org/10.1016/j.compag.2018.01.009
  • Gerdan Koc D, Koc C & Vatandas M (2022). Diagnosis of tomato plant diseases using pre-trained architectures and a proposed convolutional neural network model. Journal of Agricultural Sciences (Tarim Bilimleri Dergisi) 29(2): 627-638. doi.org/10.15832/ankutbd.957265
  • Ghosh A & Roy P (2021). AI Based automated model for plant disease detection, a deep learning approach. Communications in Computer and Information Science 1406: 199-213. doi.org/10.1007/978-3-030-75529-4_16
  • Guo Y, Fang Z, Zhang S & Dong H (2021). Classification of potato early blight with unbalanced data based on GhostNet. In: Proceedings of the 3rd International Academic Exchange Conference on Science and Technology Innovation (IAECST), 10-12 December, Guangzhou, China, pp. 559-563. doi.org/10.1109/iaecst54258.2021.9695532
  • He Y, Gao Q & Ma Z (2022). A crop leaf disease image recognition method based on bilinear residual networks. Mathematical Problems in Engineering, 2022: 1-15. doi.org/10.1155/2022/2948506 Hughes D P & Salathe M (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. ArXiv, arXiv:1511.08060. https://arxiv.org/pdf/1511.08060
  • Islam M, Dinh A, Wahid K A & Bhowmik P K (2017). Detection of potato diseases using image segmentation and multiclass support vector machine. In: Proceedings of the Canadian Conference on Electrical and Computer Engineering, 30 April-3 May, Windsor, ON, Canada, pp. 1-4. doi.org/10.1109/ccece.2017.7946594
  • Ismail W, Khan M A, Shah S A, Javed M Y, Rehman A & Saba T (2020). An adaptive image processing model of plant disease diagnosis and quantification based on color and texture histogram. In: Proceedings of the 2nd International Conference on Computer and Information Sciences (ICCIS), 13-15 October, Sakaka, Saudi Arabia, pp. 1-6. doi.org/10.1109/iccis49240.2020.9257650
  • Iqbal M A & Talukder K H (2020). Detection of potato disease using image segmentation and machine learning. In: Proceedings of the International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), 4-6 August, Chennai, India, pp. 43-47. doi.org/10.1109/wispnet48689.2020.9198563
  • Jasim M A & Al-Tuwaijari J M (2020). Plant leaf diseases detection and classification using image processing and deep learning techniques. In: Proceedings of the International Conference on Computer Science and Software Engineering (CSASE), 16-18 April, Duhok, Iraq, pp. 259-265. doi.org/10.1109/csase48920.2020.9142097
  • Jeyalakshmi S & Radha R (2020). An effective approach to feature extraction for classification of plant diseases using machine learning. Indian Journal of Science and Technology 13(32): 3295-3314. doi.org/10.17485/ijst/v13i32.827
  • Kaur N & Devendran Dr V (2021). Plant leaf disease detection using ensemble classification and feature extraction. Turkish Journal of Computer and Mathematics Education 12(11): 2339-2352.
  • Kumar A & Patel V K (2023). Classification and identification of disease in potato leaf using hierarchical based deep learning convolutional neural network. Multimedia Tools and Applications 82: 31101–31127. doi.org/10.1007/s11042-023-14663-z
  • Kurmi Y & Gangwar S (2022). A leaf image localization based algorithm for different crops disease classification. Information Processing in Agriculture 9(3): 456-474. doi.org/10.1016/j.inpa.2021.03.001 Mathew A, Antony A, Mahadeshwar Y, Khan T & Kulkarni A (2022). Plant disease detection using GLCM feature extractor and voting classification approach. Materials Today: Proceedings 58(1): 407-415. doi.org/10.1016/j.matpr.2022.02.350
  • Mahum R, Munir H, Mughal Z, Awais M, Khan F S, Saqlain M, Mahamad S & Tlili I (2023). A novel framework for potato leaf disease detection using an efficient deep learning model. Human and Ecological Risk Assessment 29(2): 303-326. doi.org/10.1080/10807039.2022.2064814
  • Moharekar D T T, Pol D U R, Ombase R & Moharekar T J (2022). Detection and classification of plant leaf diseases using convolution neural networks and streamlit. International Research Journal of Modernization in Engineering Technology and Science 4(7): 4305-4309.
  • Monowar M M, Hamid A, Kateb F, Ohi A Q & Mridha M F (2022). Self-supervised clustering for leaf disease identification. Agriculture 12(6): 1-14. doi.org/10.3390/agriculture12060814
  • Mukherjee A (2020). Analysis of diseased leaf images using digital image processing techniques and SVM classifier and disease severity measurements using fuzzy logic. International Journal of Scientific & Engineering Research 11(9): 1905–1912. doi.org/10.14299/ijser.2020.08.12
  • Nanehkaran Y A, Zhang D, Chen J, Tian Y & Al-Nabhan N (2023). Recognition of plant leaf diseases based on computer vision. Journal of Ambient Intelligence and Humanized Computing, in press. doi.org/10.1007/s12652-020-02505-x
  • Oppenheim D & Shani G (2017). Potato disease classification using convolution neural networks. Advances in Animal Biosciences 8(2): 244-249. doi.org/10.1017/s2040470017001376
  • Pardede H F, Suryawati E, Sustika R & Zilvan V (2018). Unsupervised convolutional autoencoder-based feature learning for automatic detection of plant diseases. In: Proceedings of the International Conference on Computer, Control, Informatics and Its Applications (IC3INA), 1-2 November, Tangerang, Indonesia, pp. 158-162. doi.org/10.1109/ic3ina.2018.8629518
  • Patil P, Yaligar N & Meena S (2017). Comparision of performance of classifiers - SVM, RF and ANN in potato blight disease detection using leaf images. In: Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 14-16 December, Coimbatore, India, pp. 1-5. doi.org/10.1109/iccic.2017.8524301
  • Prajna U (2021). Detection and classification of grain crops and legumes disease: a survey. Sparklinglight Transactions on Artificial Intelligence and Quantum Computing 1(1): 41-55. doi.org/10.55011/staiqc.2021.1105
  • Rozaqi A J, Arief M R & Sunyoto A (2021). Implementation of transfer learning in the convolutional neural network algorithm for identification of potato leaf disease. Procedia of Engineering and Life Science 1(1): 1-9. doi.org/10.21070/pels.v1i1.820
  • Sabzi S, Abbaspour-gilandeh Y, Abbaspour-gılandeh Y, Javadıkıa H, Javadikia H, Havaskhan H & Havaskhan H (2015). Automatic grading of emperor apples based on image processing and ANFIS. Journal of Agricultural Sciences 21(3): 326-336. doi.org/10.1501/tarimbil_0000001335
  • Sabzi S, Abbaspour Gılandeh Y & Javadıkıa H (2018). Developing a machine vision system to detect weeds from potato plant. Journal of Agricultural Sciences 24(1): 105-118. doi.org/10.15832/ankutbd.446402
  • Saeed F, Khan M A, Sharif M, Mittal M, Goyal L M & Roy S (2021). Deep neural network features fusion and selection based on PLS regression with an application for crops diseases classification. Applied Soft Computing 103: 1-15. doi.org/10.1016/j.asoc.2021.107164
  • Salih T A, Ali A J & Ahmed M N (2020). Deep learning convolution neural network to detect and classify tomato plant leaf diseases. Open Access Library Journal 7(5): 1-12. doi.org/10.4236/oalib.1106296
  • Sanjeev K, Gupta N K, Jeberson W J & Paswan S (2021). Early prediction of potato leaf diseases using ANN classifier. Oriental Journal of Computer Science and Technology 13(2): 129-134. doi.org/10.13005/ojcst13.0203.11
  • Sarker M R K R, Borsha N A, Sefatullah M, Khan A R, Jannat S & Ali H (2022). A deep transfer learning-based approach to detect potato leaf disease at an earlier stage. In: Proceedings of the Second International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 21-22 April, Bhilai, India, pp. 1-5. doi.org/10.1109/icaect54875.2022.9807963
  • Saygılı A (2023). The efficiency of transfer learning and data augmentation in lemon leaf image classification. European Journal of Engineering and Applied Sciences 6(1): 32-40. doi.org/10.55581/ejeas.1321042
  • Sharma S, Anand V & Singh S (2021). Classification of diseased potato leaves using machine learning. In: Proceedings of the 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), 18-19 June, Bhopal, India, pp. 554-559. doi.org/10.1109/csnt51715.2021.9509702
  • Shwetha K S & Sneha S P (2022). Machine learning techniques for potato leaf disease. International Research Journal of Modernization in Engineering Technology and Science 4(7): 434-441.
  • Singh A & Kaur H (2021). Potato plant leaves disease detection and classification using machine learning methodologies. IOP Conference Series: Materials Science and Engineering 1022(1): 1-9. doi.org/10.1088/1757-899x/1022/1/012121
  • Singh J & Kaur H (2019). Plant disease detection based on region-based segmentation and KNN classifier. Lecture Notes in Computational Vision and Biomechanics 30: 1667-1675. doi.org/10.1007/978-3-030-00665-5_154 Sladojevic S, Arsenovic M, Anderla A, Culibrk D & Stefanovic D (2016). Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience 2016: 1-11. doi.org/10.1155/2016/3289801
  • Swetha V & Jayaram R (2019). A novel method for plant leaf malady recognition using machine learning classifiers. In: Proceedings of the 3rd International Conference on Electronics, Communication and Aerospace Technology (ICECA), 12-14 June, Coimbatore, India, pp. 1360-1365. doi.org/10.1109/iceca.2019.8822094
  • Tiwari D, Ashish M, Gangwar N, Sharma A, Patel S & Bhardwaj S (2020). Potato leaf diseases detection using deep learning. In: Proceedings of the 4th International Conference on Intelligent Computing and Control Systems (ICICCS), 13-15 May, Madurai, India, pp. 461-466. doi.org/10.1109/iciccs48265.2020.9121067
  • Türkoğlu M, Hanbay K, Saraç Sivrikaya I. & Hanbay D (2020). Classification of apricot diseases by using deep convolution neural network. Bitlis Eren University Journal of Science 9(1): 334-345. (In Turkish) doi.org/10.17798/bitlisfen.562101
  • Wagle S A & Harikrishnan R (2021). Comparison of plant leaf classification using modified AlexNet and support vector machine. Traitement Du Signal 38(1): 79-87. doi.org/10.18280/ts.380108
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Details

Primary Language English
Subjects Engineering
Journal Section Makaleler
Authors

Cemal İhsan Sofuoğlu 0009-0009-5280-5445

Derya Bırant 0000-0003-3138-0432

Publication Date January 9, 2024
Submission Date April 4, 2023
Acceptance Date September 25, 2023
Published in Issue Year 2024 Volume: 30 Issue: 1

Cite

APA Sofuoğlu, C. İ., & Bırant, D. (2024). Potato Plant Leaf Disease Detection Using Deep Learning Method. Journal of Agricultural Sciences, 30(1), 153-165. https://doi.org/10.15832/ankutbd.1276722
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Journal of Agricultural Sciences is published open access journal. All articles are published under the terms of the Creative Commons Attribution License (CC BY).