Machine Learning Based Approaches for the Detection and Classification of Crop Leaf Diseases
Keywords:
Machine learning, convolutional neural network (CNN), transfer learning, support vector machine (SVM), random forestAbstract
Crop leaf diseases pose a significant threat to agricultural productivity worldwide, leading to substantial crop losses and economic consequences. Timely and precise identification and categorization of these diseases are vital for effective disease management and safeguarding crops. Over the past years, machine learning methods have gained prominence due to their potential to automate disease diagnosis and classification procedures. This review paper presents an overview of the various machine learning based techniques employed for the diagnosis and classification of crop leaf diseases. We discuss the fundamental concepts and methodologies of machine learning algorithms used in this context, along with their strengths and limitations. Furthermore, we analyze and compare the performance of different machine learning techniques reported in the literature and highlight the key factors influencing their effectiveness. Lastly, we pinpoint the present challenges and future research avenues to drive progress in this domain.
References
Barbedo JG. Impact of dataset size and variety on the effectiveness of deep learning and transfer learning for plant disease classification. Computers Electron Agric. 2018; 153: 46–53.
Arsenovic M, Karanovic M, Sladojevic S, Anderla A, Stefanovic D. Solving current limitations of deep learning based approaches for plant disease detection. Symmetry. 2019; 11 (7): 939.
Holzinger A, Kieseberg P, Weippl E, Tjoa AM. Current advances, trends and challenges of machine learning and knowledge extraction: from machine learning to explainable AI. In: Machine Learning and Knowledge Extraction: Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Hamburg, Germany, August 27–30, 2018. Cham, Switzerland: Springer; 2018. pp. 1–8.
Too EC, Yujian L, Njuki S, Yingchun L. A comparative study of fine-tuning deep learning models for plant disease identification. Computers Electron Agric. 2019; 161: 272–279.
Shah N, Jain S. Detection of disease in cotton leaf using artificial neural network. In: 2019 Amity International Conference on Artificial Intelligence (AICAI), Dubai, United Arab Emirates,February 4–6, 2019. pp. 473–476.
Bhimte NR, Thool VR. Diseases detection of cotton leaf spot using image processing and SVM classifier. In: 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, June 14–15, 2018. pp. 340–344.
Lu Y, Yi S, Zeng N, Liu Y, Zhang Y. Identification of rice diseases using deep convolutional neural networks. Neurocomputing. 2017; 267: 378–384.
Pagariya R, Bartere M. Identification of cotton crop diseases and provide their remedies using K-means clustering. Int J Adv Res Computer Sci. 2014; 5 (8): 58–61.
Warne PP, Ganorkar SR. Detection of diseases on cotton leaves using K-mean clustering method. Int Res J Eng Technol. 2015; 2 (4): 425–431.
Sarangdhar AA, Pawar VR. Machine learning regression technique for cotton leaf disease detection and controlling using IoT. In: 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, April 20–22, 2017. Vol. 2, pp. 449–454.
Arivazhagan S, Ligi SV. Mango leaf diseases identification using convolutional neural network. Int J Pure Appl Math. 2018; 120 (6): 11067–11079.
Guo T, Dong J, Li H, Gao Y. Simple convolutional neural network on image classification. In: 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), Beijing, China, March 10–12, 2017. pp. 721–724.
Wallelign S, Polceanu M, Buche C. Soybean plant disease identification using convolutional neural network. In: 31st FLAIRS Conference, Melbourne, FL, USA, May 21–23, 2018. pp. 146–151.
Prajapati BS, Dabhi VK, Prajapati HB. A survey on detection and classification of cotton leaf diseases. In: 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), Chennai, India, March 3–5, 2016. pp. 2499–2506.
Durmuş H, Güneş EO, Kırcı M. Disease detection on the leaves of the tomato plants by using deep learning. In: 2017 6th International Conference on Agro-Geoinformatics, Fairfax, VA, USA, August 7–10, 2017. pp. 1–5.
16Amara J, Bouaziz B, Algergawy A. A deep learning-based approach for banana leaf diseases classification. Datenbanksysteme für Business, Technologie und Web (BTW 2017)-Workshopband; 2017.
Hidayatuloh A, Nursalman M, Nugraha E. Identification of tomato plant diseases by leaf image using squeezenet model. In: 2018 International Conference on Information Technology Systems and Innovation (ICITSI), Bandung, Indonesia, October 22–26, 2018. pp. 199–204.
Patil SP, Zambre RS. Classification of cotton leaf spot disease using support vector machine. Int J Eng Res. 2014; 3 (4): 1511–1514.
Sardogan M, Tuncer A, Ozen Y. Plant leaf disease detection and classification based on CNN with LVQ algorithm. In: 2018 3rd International Conference on Computer Science and Engineering (UBMK), Sarajevo, Bosnia and Herzegovina, September 20–23, 2018. pp. 382–385.
Mohanty SP, Hughes DP, Salathé M. Using deep learning for image-based plant disease detection. Front Plant Sci. 2016; 7: 1419.
Tm P, Pranathi A, SaiAshritha K, Chittaragi NB, Koolagudi SG. Tomato leaf disease detection using convolutional neural networks. In: 2018 Eleventh International Conference on Contemporary Computing (IC3), Noida, India, August 2–4, 2018. pp. 1–5).
Batmavady S, Samundeeswari S. Detection of cotton leaf diseases using image processing. Int J Recent Technol Eng. 2019; 8 (2S4): 169–173.
Varma SJ, Patoliya J. A survey on diseases detection and classification of cotton leaf. Int J Creative Res Thoughts. 2018; 6 (1): 1014–1019.
Sibiya M, Sumbwanyambe M. Deep learning models for the detection of plant leaf diseases: a systematic review. Preprints.org. February 21, 2019. doi: 10.20944/preprints201902.0204.v1.
Nagaraju M, Chawla P. Systematic review of deep learning techniques in plant disease detection. Int J Syst Assurance Eng Manage. 2020; 11: 547–560.
