Open Access

ARTICLE

Plant Disease Classification Using Deep Bilinear CNN

D. Srinivasa Rao¹, Ramesh Babu Ch², V. Sravan Kiran¹, N. Rajasekhar^3,*, Kalyanapu Srinivas⁴, P. Shilhora Akshay¹, G. Sai Mohan¹, B. Lalith Bharadwaj¹

1 VNR VJIET, Hyderabad, 500090, India
2 Geetanjali College of Engineering and Technology, Hyderabad, 501301, India
3 Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, 500090, India
4 Kakatiya Institute of Technology and Science, Warangal, 506015, India

* Corresponding Author: N. Rajasekhar. Email:

Intelligent Automation & Soft Computing 2022, 31(1), 161-176. https://doi.org/10.32604/iasc.2022.017706

Received 08 February 2021; Accepted 24 April 2021; Issue published 03 September 2021

Abstract

Plant diseases have become a major threat in farming and provision of food. Various plant diseases have affected the natural growth of the plants and the infected plants are the leading factors for loss of crop production. The manual detection and identification of the plant diseases require a careful and observative examination through expertise. To overcome manual testing procedures an automated identification and detection can be implied which provides faster, scalable and precisive solutions. In this research, the contributions of our work are threefold. Firstly, a bi-linear convolution neural network (Bi-CNNs) for plant leaf disease identification and classification is proposed. Secondly, we fine-tune VGG and pruned ResNets and utilize them as feature extractors and connect them to fully connected dense networks. The hyperparameters are tuned to reach faster convergence and obtain better generalization during stochastic optimization of Bi-CNN(s). Finally, the proposed model is designed to leverage scalability by implying the Bi-CNN model into a real-world application and release it as an open-source. The model is designed on variant testing criteria ranging from 10% to 50%. These models are evaluated on gold-standard classification measures. To study the performance, testing samples were expanded by 5x (i.e., from 10% to 50%) and it is found that the deviation in the accuracy was quite low (0.27%) which resembles the consistent generalization ability. Finally, the larger model obtained an accuracy score of 94.98% for 38 distinct classes.

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