Classification of Maize leaf diseases from healthy leaves using Deep Forest

Prerna Sharma

1. Maharaja Agrasen Institute of Technology, Delhi, India
Email: [email protected]
2. Maharaja Agrasen Institute of Technology, Delhi, India
Email: [email protected]
3. Maharaja Agrasen Institute of Technology, Delhi, India
Email: [email protected]
*Corresponding Author: Prerna Sharma, Email: [email protected]

Apart from being relied upon for feeding the entire world, the agricultural sector is also responsible for a third of the global Gross-Domestic-Product (GDP). Additionally, a majority of developing nations depend on their agricultural produce as it provides employment opportunities for a significant fraction of the poor. This calls for methods to ensure the accurate and efficient diagnosis of plant disease, to minimize any adverse effects on the produce. This paper proposes the recognition and classification of maize plant leaf diseases by application of the Deep Forest algorithm. The Automated novel approach and accurate classification using the Deep Forest technique are a significant step-up from the existing manual classification and other techniques with less accuracy. The proposed approach has outperformed Deep Neural models and other traditional machine learning algorithms in terms of accuracy. It justifies its low dependency on extensive Hyper-parameter tuning and the size of the dataset as against other Deep Learning Models based on neural networks.

Deep Forest, Maize leaf, Disease classification, Agriculture, Image classification, gcForest

Jatin Arora, Utkarsh Agrawal, and Prerna Sharma (2020). Classification of Maize leaf diseases from healthy leaves using Deep Forest. Journal of Artificial Intelligence and Systems, 2, 14–26. https://doi.org/10.33969/AIS.2020.21002.

[1] Mohanty, Sharada & Hughes, David & Salathe, Marcel. (2016). Using Deep Learning for Image-Based Plant Disease Detection. Frontiers in Plant Science. 7. 10.3389/fpls.2016.01419.
[2] S. D. Khirade and A. B. Patil, ”Plant Disease Detection Using Image Processing,” 2015 International Conference on Computing Communication Control and Automation, Pune, 2015, pp. 768-771. doi: 10.1109/ICCUBEA.2015.153
[3] Singh, Vijai & Misra, A.K. (2016). Detection of Plant Leaf Diseases Using Image Segmentation and Soft Computing Techniques. Information Processing in Agriculture. 4. 10.1016/j.inpa.2016.10.005.
[4] Golhani, Kamlesh & Balasundram, Siva & Vadamalai, Ganesan & Pradhan, Biswajeet.(2018). A Review of Neural Networks in Plant Disease Detection using Hyperspectral Data. Information Processing in Agriculture. 10.1016/j.inpa.2018.05.002.
[5] Polke, Anish & Joshi, Kavita & Gouda, Pramod. (2019). Leaf Disease Detection Based on Machine Learning. 10.1007/978-3-030-00665-5 172.
[6] Nettleton, David & Katsantonis, Dimitrios & Kalaitzidis, Argyrios & Sarafijanovic-Djukic, Natasa & Puigdollers, Pau & Confalonieri, Roberto. (2019). Predicting rice blast disease: machine learning versus process-based models. BMC Bioinformatics. 20. 514. 10.1186/s12859-019-3065-1.
[7] Zhou, Zhi-Hua & Feng, Ji. (2019). Deep forest. National Science Review. 6. 74-86. 10.1093/nsr/nwy108.
[8] Zhou, Tianchi & Sun, Xiaobing & Xia, Xin & Bin, Li & Chen, Xiang. (2019). Improving Defect Prediction with Deep Forest. Information and Software Technology. 114. 10.1016/j.infsof.2019.07.003.
[9] Zhao, Lingling & Wang, Junjie & Nabil, Mahieddine & Zhang, Jun. (2018). Deep Forest-based Prediction of Protein Subcellular Localization. Current Gene Therapy. 18. 10.2174/1566523218666180913110949.
[10] Cao, Xianghai & Wen, Li & Ge, Yiming & Zhao, Jing & Jiao, Licheng.(2019). Rotation-Based Deep Forest for Hyperspectral Imagery Classification. IEEE Geoscience and Remote Sensing Letters. PP. 1-5. 10.1109/LGRS.2019.2892117.
[11] Zhang, Ya-Lin & Li, Xiaolong & Qi, Yuan & Zhou, Zhi-Hua & Zhou, Jun & Zheng, Wenhao & Feng, Ji & Li, Longfei & Liu, Ziqi & Li, Ming & Zhang, Zhiqiang & Chen, Chaochao. (2019). Distributed Deep Forest and its Application to Automatic Detection of Cash-Out Fraud. ACM Transactions on Intelligent Systems and Technology. 10. 1- 19. 10.1145/3342241.
[12] Shao, Lizhen & Zhang, Donghui & Du, Haipeng & Fu, Dongmei. (2019). Deep forest in ADHD data classification. IEEE Access. PP. 1-1. 10.1109/ACCESS.2019.2941515.
[13] Utkin, Lev & Ryabinin, Mikhail. (2017). A Siamese Deep Forest. Knowledge-Based Systems. 10.1016/j.knosys.2017.10.006.
[14] Deepak Gupta, Prerna Sharma, Krishna Choudhary, Kshitij Gupta, Rahul Chawla, Ashish Khanna, Victor Hugo C. de Albuquerque, “Artificial plant optimization algorithm to detect infected leaves using machine learning”, Expert Systems (Wiley) K
[15] hamparia, Aditya & Saini, Gurinder & Gupta, Deepak & Khanna, Ashish & Tiwari, Shrasti & Albuquerque, V.H.C.. (2019). Seasonal Crops Disease Prediction and Classification Using Deep Convolutional Encoder Network. Circuits, Systems, and Signal Processing. 10.1007/s00034-019-01041-0.
[16] Gupta D., Rodrigues J.J.P.C., Sundaram S., Khanna A., Korotaev V., Albuquerque V. H.C., Usability Feature Extraction Using Modified Crow Search Algorithm: A Novel Approach, Neural Computing and Applications, pp. 1-11, 2018.
[17] Jain R., Gupta D., Khanna A., Usability Feature Optimization using MWOA, In proceedings of International Conference on Innovative Computing and Communications (ICICC), Lecture Notes in Networks and Systems, Springer, Singapore, vol. 56, pp. 453-462, 2018.
[18] Gupta D., Khanna A., SK L., Shankar K., Furtado V., Rodrigues J.J.P.C., Efficient Artificial Fish Swarm Based Clustering Approach on Mobility Aware Energy-Efficient For MANET, Transactions on Emerging Telecommunications Technologies, 2018.
[19] Gupta D., Ahlawat A., Sagar K. (2017). “Usability Prediction and Ranking of SDLC models using Fuzzy Hierarchical Usability Model”, Open Engineering (Central European Journal of Engineering), ESCI, SCOPUS. Volume 7, No. 1.
[20] Gupta D., Khanna A. (2017). “Software Usability Datasets”, International Journal of Pure and Applied Mathematics, SCOPUS. Volume 117, No. 15, 1001-1014.
[21] Gupta D., Sagar K. (2010) “Remote file synchronization single-round algorithm”, International Journal of Computer Applications, 4(1), 32-36.
[22] Patnaik A., Gupta D. (2010). “Unique identification system”, International Journal of Computer Applications, 7(5).
[23] Deepak Gupta, Jatin Arora, Utkarsh Agrawal, Ashish Khanna, Victor Hugo C. de Albuquerque, Optimized Binary Bat algorithm for classification of white blood cells, Measurement, Volume 143, 2019, Pages 180-190, ISSN 0263-2241, DOI: 10.1016/j.measurement.2019.01.002.
[24] Jatin Arora, Utkarsh Agrawal, Prayag Tiwari, Deepak Gupta, Ashish Khanna, Ensemble Feature Selection Method based on recently developed Nature-inspired algorithms, Proceedings of ICICC 2019, DOI: 10.1007/978-981-15-1286-5_39.
[25] K. Shankar, S.K. Lakshmanaprabu, Deepak Gupta, Andino Maseleno, Victor Hugo C. de Albuquerque, “Optimal features-based multi kernel SVM approach for thyroid disease classification”, Journal of Supercomputing (Springer), July 2018, https://doi.org/10.1007/s11227-018-2469-4.