Abstract
A rapid expansion in the world’s population needs an enormous supply of food grains to satisfy agricultural needs. Unfortunately, crop diseases adversely impact food production and disrupt the supply chain. To circumvent the limitations of continuous human monitoring, machine learning techniques can automatically diagnose leaf diseases in their early stages based on image data. In this paper, an Interpretable Leaf Disease Detector (I-LDD) framework for image-based leaf disease detection using Extreme Learning Machine (ELM) is proposed. The choice of ELM for this work has been motivated by its quicker convergence, good generalization capability, and shorter learning time compared to the standard gradient-based learning algorithms. Experiments have been carried out using a publicly available PlantVillage dataset comprising healthy and diseased leaf images for 32 categories. In the first phase, the leaf images are preprocessed and segmented using the k-means clustering algorithm. In the second phase, textural and frequency-based features are extracted from the segmented images. In the third phase, several machine learning classifiers are trained using tenfold cross-validation. It is observed that I-LDD achieves an accuracy of 0.9322 ± 0.0088 at a 95% confidence level, outperforming the state-of-the-art methods. Moreover, a statistical significance test on the classification performance metrics also revealed the superiority of I-LDD over the state-of-the-art classifiers. Further, Local Interpretable Model-agnostic Explanations (LIME) is used to obtain the top 10 superpixels that contributed most to the class predicted by I-LDD.
Similar content being viewed by others
Data availability
All data generated or analyzed during this study are included in this article.
References
Ahmad S (1994) A usable real-time 3D hand tracker. In: Proceedings of 1994 28th Asilomar conference on signals, systems and computers, vol 2. IEEE, pp 1257–1261
Alagumariappan P, Dewan NJ, Muthukrishnan GN, Raju BKB, Bilal RAA, Sankaran V (2020) Intelligent plant disease identification system using Machine Learning. Eng Proc 2(1):49
Alguliyev R, Imamverdiyev Y, Sukhostat L, Bayramov R (2021) Plant disease detection based on a deep model. Soft Comput 25(21):13229–13242
Alsmirat MA, Al-Alem F, Al-Ayyoub M, Jararweh Y, Gupta B (2019) Impact of digital fingerprint image quality on the fingerprint recognition accuracy. Multimed Tools Appl 78:3649–3688
Aqel D, Al-Zubi S, Mughaid A, Jararweh Y (2021) Extreme learning machine for plant diseases classification: a sustainable approach for smart agriculture. Clust Comput 25(3):1–14
Atal DK, Singh M (2020) Arrhythmia classification with ECG signals based on the optimization-enabled deep convolutional neural network. Comput Methods Programs Biomed 196:105607
Bhatia A, Chug A, Prakash Singh A (2020) Application of extreme learning machine in plant disease prediction for highly imbalanced dataset. J Stat Manag Syst 23(6):1059–1068
Bock CH, Barbedo JG, Del Ponte EM, Bohnenkamp D, Mahlein A-K (2020) From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy. Phytopathol Res 2(1):1–30
Chug A, Bhatia A, Singh AP, Singh D (2022) A novel framework for image-based plant disease detection using hybrid deep learning approach. Soft Comput 4:1–26
Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30
Diana Andrushia A, Mary Neebha T, Trephena Patricia A, Umadevi S, Anand N, Varshney A (2023) Image-based disease classification in grape leaves using convolutional capsule network. Soft Comput 27(3):1457–1470
FAO (2020) 2020 is International Year of Plant Health, howpublished. https://www.unep.org/news-and-stories/story/2020-international-year-plant-health. Accessed 05 Jun 2022
Geneva (2021) International Day of Plant Health—Geneva Environment Network. https://www.genevaenvironmentnetwork.org/resources/updates/international-day-of-plant-health/. Accessed 08 Jan 2022
Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 6:610–621
Hatuwal BK, Shakya A, Joshi B (2020) Plant leaf disease recognition using random forest, KNN, SVM and CNN. Polibits 62:13–19
Hu M-K (1962) Visual pattern recognition by moment invariants. IRE Trans Inf Theory 8(2):179–187
Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: 2004 IEEE international joint conference on neural networks (IEEE Cat. No. 04CH37541), vol 2. IEEE, pp 985–990
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501
Hughes D, Salathé M, et al (2015) An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060
Kaur S, Pandey S, Goel S (2018) Semi-automatic leaf disease detection and classification system for soybean culture. IET Image Proc 12(6):1038–1048
Khakimov A, Salakhutdinov I, Omolikov A, Utaganov S (2022) Traditional and current-prospective methods of agricultural plant diseases detection: a review. IOP Confer Ser: Earth Environ Sci 951(1):012002
Kim JK, Park HW (1999) Statistical textural features for detection of microcalcifications in digitized mammograms. IEEE Trans Med Imaging 18(3):231–238
Klassen W, Vreysen M (2021) Area-wide integrated pest management and the sterile insect technique. In: Sterile insect technique. CRC Press, pp 75–112
Kortli Y, Jridi M, Al Falou A, Atri M (2020) Face recognition systems: a survey. Sensors 20(2):342
Krishnan VG, Deepa J, Rao PV, Divya V, Kaviarasan S (2022) An automated segmentation and classification model for banana leaf disease detection. J Appl Biol Biotechnol 10(01):213–220
Kurmi Y, Gangwar S, Chaurasia V, Goel A (2022) Leaf images classification for the crops diseases detection. Multimed Tools Appl 81(6):8155–8178
Lacombe T, Favreliere H, Pillet M (2020) Modal features for image texture classification. Pattern Recogn Lett 135:249–255
Libo Z, Tian H, Chunyun G, Elhoseny M (2019) Real-time detection of cole diseases and insect pests in wireless sensor networks. J Intell Fuzzy Syst 37(3):3513–3524
Lima AA, Mridha MF, Das SC, Kabir MM, Islam MR, Watanobe Y (2022) A comprehensive survey on the detection, classification, and challenges of neurological disorders. Biology 11(3):469
Lucas JA (2020) Plant pathology and plant pathogens. Wiley
MacQueen J (1967) Classification and analysis of multivariate observations. In: 5th Berkeley Symp. Math. Statist. Probability, pp 281–297
Manida M (2022) The future of food and agriculture trends and challenges. Agric Food E-Newslett 1(2):180
Merot A, Fermaud M, Gosme M, Smits N (2020) Effect of conversion to organic farming on pest and disease control in French vineyards. Agronomy 10(7):1047
Moumni M, Allagui MB, Mancini V, Murolo S, Tarchoun N, Romanazzi G (2020) Morphological and molecular identification of seedborne fungi in squash (Cucurbita maxima, Cucurbita moschata). Plant Dis 104(5):1335–1350
NASEM (2019) Science breakthroughs to advance food and agricultural research by 2030. The National Academies Press
Oh S-H, Park S-W, Kim B-J (2002) DWT (discrete wavelet transform) based watermark system. In: 2002 Digest of technical papers. International conference on consumer electronics (IEEE Cat. No. 02CH37300). IEEE, pp 192–193
Ojala T, Pietikäinen M, Mäenpää T (2001) A generalized local binary pattern operator for multiresolution gray scale and rotation invariant texture classification. In: International conference on advances in pattern recognition. Springer, pp 399–408
Pallathadka H, Ravipati P, Sajja GS, Phasinam K, Kassanuk T, Sanchez DT, Prabhu P (2022) Application of machine learning techniques in rice leaf disease detection. Mater Today: Proc 51:2277–2280
Panchal P, Raman VC, Mantri S (2019) Plant diseases detection and classification using machine learning models. In: 2019 4th international conference on computational systems and information technology for sustainable solution (CSITSS), vol 4. IEEE, pp 1–6
Qi S, Ning X, Yang G, Zhang L, Long P, Cai W, Li W (2021) Review of multi-view 3D object recognition methods based on deep learning. Displays 69:102053
Rajpal S, Agarwal M, Rajpal A, Lakhyani N, Saggar A, Kumar N (2022) COV-ELM classifier: an extreme learning machine based identification of COVID-19 using Chest X-Ray Images. Intell Dec Technol 16(1):193–203
Ribeiro MT, Singh S, Guestrin C (2016) “ Why should I trust you?” Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1135–1144
Ristaino JB, Anderson PK, Bebber DP, Brauman KA, Cunniffe NJ, Fedoroff NV, Finegold C, Garrett KA, Gilligan CA, Jones CM, Martin MD, MacDonald GK, Neenan P, Records A, Schmale DG, Tateosian L, Wei Q (2021) The persistent threat of emerging plant disease pandemics to global food security. Proc Natl Acad Sci, Eng, Med Others 118(23):e2022239118
Roy K, Chaudhuri SS, Frnda J, Bandopadhyay S, Ray IJ, Banerjee S, Nedoma J (2023) Detection of tomato leaf diseases for agro-based industries using novel PCA DeepNet. IEEE Access 11:14983–15001
Saleem MH, Potgieter J, Arif KM (2019) Plant disease detection and classification by deep learning. Plants 8(11):468
Stiglic G, Kocbek P, Fijacko N, Zitnik M, Verbert K, Cilar L (2020) Interpretability of machine learning-based prediction models in healthcare. Wiley Interdiscipl Rev: Data Min Knowl Discov 10(5):e1379
Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vis 7(1):11–32
Tang Z, Zheng Y, Gu K, Liao K, Wang W, Yu M (2018) Full-reference image quality assessment by combining features in spatial and frequency domains. IEEE Trans Broadcast 65(1):138–151
Udutalapally V, Mohanty SP, Pallagani V, Khandelwal V (2020) scrop: a novel device for sustainable automatic disease prediction, crop selection, and irrigation in internet-of-agro-things for smart agriculture. IEEE Sens J 21(16):17525–17538
Varuna Shree N, Kumar T (2018) Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain informatics 5(1):23–30
Vellido A (2020) The importance of interpretability and visualization in machine learning for applications in medicine and health care. Neural Comput Appl 32(24):18069–18083
Weszka JS, Dyer CR, Rosenfeld A (1976) A comparative study of texture measures for terrain classification. IEEE Trans Syst, Man, Cybern SMC-6(4):269–285
Xian TS, Ngadiran R (2021) Plant diseases classification using machine learning. J Phys: Confer Ser 1962(1):012024
Yanikoglu B, Kholmatov A (2009) Online signature verification using Fourier descriptors. EURASIP J Adv Signal Process 2009:1–13
Zargari A, Du Y, Heidari M, Thai TC, Gunderson CC, Moore K, Mannel RS, Liu H, Zheng B, Qiu Y (2018) Prediction of chemotherapy response in ovarian cancer patients using a new clustered quantitative image marker. Phys Med Biol 63(15):155020
Zhang N, Yang G, Pan Y, Yang X, Chen L, Zhao C (2020) A review of advanced technologies and development for hyperspectral-based plant disease detection in the past three decades. Remote Sens 12(19):3188
Zhou X, Li Z, Xie H, Feng T, Lu Y, Wang C, Chen R (2020) Leukocyte image segmentation based on adaptive histogram thresholding and contour detection. Curr Bioinform 15(3):187–195
Acknowledgements
Dr. Ankit Rajpal (PI) and Dr. Manoj Agarwal (Co-PI) would like to thank the Institution of Eminence (IoE), University of Delhi, India, for providing equipment support under Faculty Research Programme (Ref. No./IoE/2021/12/FRP).
Funding
This research did not receive any grant from any of the funding agencies.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest. This article does not contain any studies with human participants or animals performed by any of the authors.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Mishra, R., Kavita, Rajpal, A. et al. I-LDD: an interpretable leaf disease detector. Soft Comput 28, 2517–2533 (2024). https://doi.org/10.1007/s00500-023-08512-2
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-023-08512-2