Machine Learning-Potato Leaf Disease Detection App (MR-PoLoD)
DOI:
https://doi.org/10.32736/sisfokom.v13i3.2261Keywords:
Accuracy, application, CNN, healthy potatoesAbstract
Potato production in Indonesia has grown very rapidly, making Indonesia the largest potato producer in Southeast Asia. However, there are challenges for farmers in growing potatoes. Such as treating potatoes for various diseases. 2 diseases will occur in potato plants if not treated quickly, namely early blight disease caused by the fungus Alternaria solani and late blight disease caused by the microorganism Phytophthora infestans. The project "Potato Plant Leaf Disease Detector (MR-PoLod)" aims to design an android application that can classify leaves on potato plants into 3 classifications, namely healthy, early, and late blight disease. This application uses the CNN (Convolutional Neural Network) Machine Learning Algorithm because currently, CNN is recognized as the most efficient and effective model in pattern and image recognition tasks. This application uses the Python programming language which is rich in library and framework availability so that it can meet the needs of machine learning and image classification tasks. The total data used for training data, data validation and data testing is 3165 images. With each division of the data process on the training data of 70%, validation of 15% & testing of 15% to test the effectiveness of the model that has been created. The performance of MR-PoLod for each class, obtained a precision value, recall, and f1-score of 0.99. Likewise, the accuracy value achieved by the model is 0.99 or 99%. Thus, the expected application can facilitate farmers in classifying diseases on potato plant leaves.References
F. Suarezsaga, D. Siahaan, and A. Yuniarti, “A Comparison of Deep Learning for Software Features Extraction in Forensic Online News,” in 2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE), Aug. 2023, pp. 56–61. doi: 10.1109/ICCSCE58721.2023.10237097.
Y. An et al., “A hybrid attention-guided ConvNeXt-GRU network for action recognition,” Engineering Applications of Artificial Intelligence, vol. 133, p. 108243, Jul. 2024, doi: 10.1016/j.engappai.2024.108243.
D. Pineda Medina, I. Miranda Cabrera, R. A. de la Cruz, L. Guerra Arzuaga, S. Cuello Portal, and M. Bianchini, “A Mobile App for Detecting Potato Crop Diseases,” Journal of Imaging, vol. 10, no. 2, Art. no. 2, Feb. 2024, doi: 10.3390/jimaging10020047.
S. Sehgal and A. Roy, “A Study of Deep Learning Techniques on Oilseed Crops,” in 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Nov. 2022, pp. 526–532. doi: 10.1109/ICCCIS56430.2022.10037740.
R. Thenmozhi, A. Kavinilavu, G. Malathi, and K. Mohanambal, “Advancing Deep Learning Models through Robust Feature Extraction and Transfer Learning for Enhanced Image Recognition,” in 2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI), Nov. 2023, pp. 876–880. doi: 10.1109/ICCSAI59793.2023.10421579.
A. Devi, “Agriculture System for Potato Leaf Disease Detection Using Deep Learning And GENAI,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 4, Art. no. 4, Jul. 2024.
K. K. Chakraborty, R. Mukherjee, C. Chakroborty, and K. Bora, “Automated recognition of optical image based potato leaf blight diseases using deep learning,” Physiological and Molecular Plant Pathology, vol. 117, p. 101781, Jan. 2022, doi: 10.1016/j.pmpp.2021.101781.
K. Mahadevan, A. Punitha, and J. Suresh, “Automatic recognition of Rice Plant leaf diseases detection using deep neural network with improved threshold neural network,” e-Prime - Advances in Electrical Engineering, Electronics and Energy, vol. 8, p. 100534, Jun. 2024, doi: 10.1016/j.prime.2024.100534.
S. Ghufron and S. Prayogi, “Cooling System in Machine Operation at Gas Engine Power Plant at PT Multidaya Prima Elektrindo,” Journal of Artificial Intelligence and Digital Business (RIGGS), vol. 1, no. 2, Art. no. 2, 2023, doi: 10.31004/riggs.v1i2.21.
K. Tan et al., “Change detection on multi-sensor imagery using mixed interleaved group convolutional network,” Engineering Applications of Artificial Intelligence, vol. 133, p. 108446, Jul. 2024, doi: 10.1016/j.engappai.2024.108446.
J. A. Jose, A. Sharma, M. Sebastian, and R. V. F. Densil, “Classification of Weeds and Crops using Transfer Learning,” in 2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), Jan. 2022, pp. 1–7. doi: 10.1109/ACCAI53970.2022.9752477.
S. Prayogi, “Enhancement of the Silicon Nanocrystals’ Electronic Structure within a Silicon Carbide Matrix,” Indonesian Journal of Chemistry, vol. 24, no. 1, Art. no. 1, Feb. 2024, doi: 10.22146/ijc.79864.
N. Roberts, M. Smith, and J. Qi, “Data engineering for predictive machine learning of stormwater infrastructure conditions,” Engineering Applications of Artificial Intelligence, vol. 133, p. 108195, Jul. 2024, doi: 10.1016/j.engappai.2024.108195.
T. Anandhakrishnan and S. M. Jaisakthi, “Deep Convolutional Neural Networks for image based tomato leaf disease detection,” Sustainable Chemistry and Pharmacy, vol. 30, p. 100793, Dec. 2022, doi: 10.1016/j.scp.2022.100793.
M. Muhammad, B. Ragadita, S. Prayogi, and S. Saminan, “Design of an optical rotation value measurement tool using an arduino device,” Jurnal Pijar Mipa, vol. 18, no. 5, Art. no. 5, Sep. 2023, doi: 10.29303/jpm.v18i5.4811.
R. Kumar, M. Gupta, Prakhar, and R. Kathait, “Deep Learning Based Analysis and Detection of Potato Leaf Disease,” NEU Journal for Artificial Intelligence and Internet of Things, vol. 2, no. 4, Art. no. 4, Nov. 2023, Accessed: Aug. 22, 2024. [Online]. Available: https://dergi.neu.edu.tr/index.php/aiit/article/view/810
S. Prayogi, Y. Cahyono, I. Iqballudin, M. Stchakovsky, and D. Darminto, “The effect of adding an active layer to the structure of a-Si: H solar cells on the efficiency using RF-PECVD,” J Mater Sci: Mater Electron, vol. 32, no. 6, pp. 7609–7618, Mar. 2021, doi: 10.1007/s10854-021-05477-6.
B. Vadivel, R. Thangaraj, P. Pandiyan, T. Aravind, K. Harish, and E. Sivaraman, “Deep Learning Models for Potato Leaf Disease Identification: A Comparative Analysis,” in 2022 International Conference on Green Energy, Computing and Sustainable Technology (GECOST), Oct. 2022, pp. 58–62. doi: 10.1109/GECOST55694.2022.10010611.
F. Saeed, M. A. Khan, M. Sharif, M. Mittal, L. M. Goyal, and S. Roy, “Deep neural network features fusion and selection based on PLS regression with an application for crops diseases classification,” Applied Soft Computing, vol. 103, p. 107164, May 2021, doi: 10.1016/j.asoc.2021.107164.
F. Silviana and S. Prayogi, “An Easy-to-Use Magnetic Dynamometer for Teaching Newton’s Third Law,” Jurnal Pendidikan Fisika dan Teknologi, vol. 9, no. 1, Art. no. 1, Jun. 2023, doi: 10.29303/jpft.v9i1.4810.
F. Silviana and S. Prayogi, “Utilization of Smartphones in Experiments of Measurement of Electron-Mass Charge Ratio,” International Journal of Engineering and Science Applications, vol. 10, no. 1, Art. no. 1, May 2023.
D. Bienkowski, M. J. Aitkenhead, A. K. Lees, C. Gallagher, and R. Neilson, “Detection and differentiation between potato (Solanum tuberosum) diseases using calibration models trained with non-imaging spectrometry data,” Computers and Electronics in Agriculture, vol. 167, p. 105056, Dec. 2019, doi: 10.1016/j.compag.2019.105056.
N. B. Moin, N. Islam, S. Sultana, L. A. Chhoa, S. M. Ruhul Kabir Howlader, and S. H. Ripon, “Disease Detection of Bangladeshi Crops Using Image Processing and Deep Learning - A Comparative Analysis,” in 2022 2nd International Conference on Intelligent Technologies (CONIT), Jun. 2022, pp. 1–8. doi: 10.1109/CONIT55038.2022.9847715.
Z. Jin and Y. Wu, “Early Crops Diseases Identification Using Deep Transfer Learning,” in 2023 2nd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC), Aug. 2023, pp. 383–386. doi: 10.1109/RAIIC59453.2023.10280831.
A. Bajpai, N. K. Tiwari, A. K. Tripathi, V. Tripathi, and D. Katiyar, “Early leaf diseases prediction in Paddy crop using Deep learning model,” in 2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS), Apr. 2023, pp. 1–6. doi: 10.1109/PCEMS58491.2023.10136038.
M. Li, G. Zhou, A. Chen, L. Li, and Y. Hu, “Identification of tomato leaf diseases based on LMBRNet,” Engineering Applications of Artificial Intelligence, vol. 123, p. 106195, Aug. 2023, doi: 10.1016/j.engappai.2023.106195.
R. Van De Vijver et al., “In-field detection of Alternaria solani in potato crops using hyperspectral imaging,” Computers and Electronics in Agriculture, vol. 168, p. 105106, Jan. 2020, doi: 10.1016/j.compag.2019.105106.
G. Kang, J. Wang, X. Yue, G. Zeng, and Z. Feng, “Transfer Learning Based Crop Disease Identification Using State-of-the-art Deep Learning Framework,” in 2021 IEEE International Performance, Computing, and Communications Conference (IPCCC), Oct. 2021, pp. 1–6. doi: 10.1109/IPCCC51483.2021.9679406.
R. Soltani, E. Benmohamed, and H. Ltifi, “Newman-Watts-Strogatz topology in deep echo state networks for speech emotion recognition,” Engineering Applications of Artificial Intelligence, vol. 133, p. 108293, Jul. 2024, doi: 10.1016/j.engappai.2024.108293.
A. M. Elshewey, S. M. Tawfeek, A. A. Alhussan, M. Radwan, and A. H. Abed, “Optimized Deep Learning for Potato Blight Detection Using the Waterwheel Plant Algorithm and Sine Cosine Algorithm,” Potato Res., May 2024, doi: 10.1007/s11540-024-09735-y.
S. Prayogi and M. I. Marzuki, “The Effect of Addition of SnO2 Doping on The Electronic Structure of TiO2 Thin Film as Photo-Anode in DSSC Applications,” Journal of Emerging Supply Chain, Clean Energy, and Process Engineering, vol. 1, no. 1, Art. no. 1, Sep. 2022, doi: 10.57102/jescee.v1i1.3.
H. Catal Reis and V. Turk, “Potato leaf disease detection with a novel deep learning model based on depthwise separable convolution and transformer networks,” Engineering Applications of Artificial Intelligence, vol. 133, p. 108307, Jul. 2024, doi: 10.1016/j.engappai.2024.108307.
T. S. Jaya and D. Sahlinal, “Potato Leaf Disease Detection with Convolutional Neural Network Method,” International Conference on Agriculture and Applied Science, pp. 50–58, 2022, doi: 10.25181/icoaas.v3i3.2867.
A. Singh and H. Kaur, “Potato Plant Leaves Disease Detection and Classification using Machine Learning Methodologies,” IOP Conf. Ser.: Mater. Sci. Eng., vol. 1022, no. 1, p. 012121, Jan. 2021, doi: 10.1088/1757-899X/1022/1/012121.
G. Gan, H. Ye, M. Dong, W. Ye, and Y. Wang, “Three-partition coevolutionary differential evolution algorithm for mixed-variable optimization problems,” Engineering Applications of Artificial Intelligence, vol. 133, p. 108263, Jul. 2024, doi: 10.1016/j.engappai.2024.108263.
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