Abstract
Apples are one of the most popular agricultural products. Despite being one of the most widely grown commodities, apple demand is on the rise. As a result, this crop, which was formerly only grown in temperate climates, is now being grown in tropical climates. Pest and disease infestations are a major issue that affects apple output each year. In this paper, an approach has been made which combines machine learning and image processing concepts to identify diseases from infected apple leaves. This method effectively differentiates between diseased and non-diseased apple leaves. Pre-processing of the image is done using grab cut segmentation which is the primary stage in the disease identification process. The infected type from the original leaf image is recognized by 96% using the segmentation of the diseased portion, and multiclass SVM detects the infected type from 500 images using the feature extraction.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kellerhals, M., Tschopp, D., & Roth, M. Challenges in apple breeding (pp. 12–18).
Behera, S. K., Rath, A. K., & Sethy, P. K. (2021). Maturity status classification of papaya fruits based on machine learning and transfer learning approach. Information Processing in Agriculture, 8(2), 244–250.
Syazwani, R., & Nurazwin, W., et al. (2021). Automated image identification, detection and fruit counting of top-view pineapple crown using machine learning. Alexandria Engineering Journal.
Khan, N., et al. (2021). Oil palm and machine learning: Reviewing one decade of ideas, innovations, applications, and gaps. Agriculture, 11(9), 832.
Patil, P. U., et al. (2021). Grading and sorting technique of dragon fruits using machine learning algorithms. Journal of Agriculture and Food Research, 4, 100118.
Munera, S., et al. (2021). Discrimination of common defects in loquat fruit cv. ‘Algerie’ using hyperspectral imaging and machine learning techniques. Postharvest Biology and Technology, 171, 111356.
Tripathi, M. K., & Maktedar, D. D. (2021). Detection of various categories of fruits and vegetables through various descriptors using machine learning techniques. International Journal of Computational Intelligence Studies, 10(1), 36–73.
Koyama, K., et al. (2021). Predicting sensory evaluation of spinach freshness using machine learning model and digital images. PLoS ONE, 16(3), e0248769.
Brighty, S., Sahaya, P., Shri Harini, G., & Vishal, N. (2021). Detection of adulteration in fruits using machine learning. In 2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE.
Rodrigues, B., et al. (2021). Ripe-unripe: Machine learning based ripeness classification. In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE.
Naz, F., Irshad, G., & Abbasi, N. A. (2018). Surveillance and characterization of Botryosphaeria obtusa causing frogeye leaf spot of Apple in District Quetta Abstract (Vol. 16, pp. 111–115).
Strickland, D., Carroll, J., & Cox, K. (2020). Cedar apple rust.
Riffle, J. W., & Peterson, G. W. (1986). Diseases of trees in the great plains (General Technical Reports—U.S. Department of Agriculture, Forest Service, no. RM-129). https://doi.org/10.5962/bhl.title.99571
Rigor, D. B., Oryan, C., Ochasan, J. M., Boncato, T., Pedroche, N., & Amoy, M. (1997). Evaluation of temperate zone fruits in the highlands of Nothern Luzon, Philippines. Acta Hortic, 441, 59–66. https://doi.org/10.17660/ActaHortic.1997.441.5
Concepcion, R. S., Loresco, P. J. M., Bedruz, R. A. R., Dadios, E. P., Lauguico, S. C., & Sybingco, E. (2020). Trophic state assessment using hybrid classification tree-artificial neural network. International Journal of Advances in Intelligent Informatics, 6(1), 46–59. https://doi.org/10.26555/ijain.v6i1.408
Concepcion, R., Lauguico, S., Alejandrino, J., Dadios, E. P., & Sybingco, E. (2018). Lettuce canopy area measurement using static supervised neural networks based on numerical image textural feature analysis of Haralick and gray level co-occurrence matrixs. Journal of Agricultural Science, 156(1), 1. https://doi.org/10.1017/S0021859618000163
Javel, I. M., Bandala, A. A., Salvador, R. C., Bedruz, R. A. R., Dadios, E. P., & Vicerra, R. R. P. (2019). Coconut fruit maturity classification using fuzzy logic. In 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM 2018). https://doi.org/10.1109/HNICEM.2018.8666231
De Luna, R. G., Dadios, E. P., Bandala, A. A., & Vicerra, R. R. P. (2019). Tomato fruit image dataset for deep transfer learning-based defect detection. In Proceedings of the IEEE 2019 9th International Conference on Robotics, Automation and Mechatronics (RAM) (pp. 356–361). https://doi.org/10.1109/CIS-RAM47153.2019.9095778
De Luna, R. G., Dadios, E. P., & Bandala, A. A. (2019). Automated image capturing system for deep learning-based tomato plant leaf disease detection and recognition. In IEEE Region 10 Annual International Conference, Proceedings/TENCON (Vol. 2018, pp. 1414–1419). https://doi.org/10.1109/TENCON.2018.8650088
Chien, C.-L., Tseng, D.-C., et al.: Color image enhancement with exact HSI color model. International Journal of Innovative Computing, Information and Control, 7(12), 6691–6710.
Yu, C., Dian-ren, C., Yang, L., & Lei, C. (2010). Otsu’s thresholding method based on gray level-gradient two-dimensional histogram. In 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010), vol. 3. IEEE, 2010, pp. 282–285.
Ehsanirad, A., & Sharath Kumar, Y. H. (2010). Leaf recognition for plant classification using GLCM and PCA methods. Oriental Journal of Computer Science and Technology, 3(1), 31–36.
Zweig, M. H., & Campbell, G. (1993). Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine. Clinical Chemistry, 39(4), 561–577.
Hu, Y., Ping, X., Xu, M., Shan, W., & He, Y. (2016). Detection of late blight disease on potato leaves using hyperspectral imaging technique. Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu, 36(2), 515–519.
Prakash, R. M., Saraswathy, G., Ramalakshmi, G., Mangaleswari, K., & Kaviya, T. (2017). Detection of leaf diseases and classification using digital image processing. In 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (pp. 1–4). IEEE.
Asfarian, A., Herdiyeni, Y., Rauf, A., & Mutaqin, K. H. (2013). Paddy diseases identification with texture analysis using fractal descriptors based on Fourier spectrum. In 2013 International Conference on Computer, Control, Informatics and Its Applications (IC3INA) (pp.77–81). IEEE.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bhavya, K.R., Pravinth Raja, S., Sunil Kumar, B., Karthik, S.A., Chavadaki, S. (2023). An Efficient Machine Learning Approach for Apple Leaf Disease Detection. In: Rao, B.N.K., Balasubramanian, R., Wang, SJ., Nayak, R. (eds) Intelligent Computing and Applications. Smart Innovation, Systems and Technologies, vol 315. Springer, Singapore. https://doi.org/10.1007/978-981-19-4162-7_39
Download citation
DOI: https://doi.org/10.1007/978-981-19-4162-7_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-4161-0
Online ISBN: 978-981-19-4162-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)