Development of weighted ensemble transfer learning for tomato leaf disease classification solving low resolution problems

ABSTRACT

The manual prediction of Tomato Leaf Diseases (TLD) is more time-consuming, and the implementation cost is high. The various natural characteristic and the low resolution of images make a difficult task for plant disease recognition. The main intention of this paper is to develop a novel tomato leaf disease classification using deep learning techniques, especially for solving the low-resolution issues in the images. The pre-processing is done by the contrast enhancement technique and then the low-resolution problem in pre-processed images is done through Deep Convolutional Neural Networks (DCNN). The segmentation of diseases is carried out through Mask Region-Based Convolutional Neural Networks (Mask R-CNN). The weight of each classifier score is optimized by Controlling Parameter-based Artificial Gorilla Troops Optimization (CP-AGTO). The accuracy and sensitivity of the recommended technique attain 92% and 93%. Thus, the efficacy of the proposed model has been examined with several performance metrics over various recent approaches.

KEYWORDS:

• Tomato leaf disease classification
• image super-resolution
• weighted ensemble transfer learning
• mask region-based convolutional neural networks
• controlling parameter-based artificial gorilla troops optimization

Acknowledgement

I acknowledge “KLEF –2012030001” Full Time fellowship awarded for the successfully completion of the work. Kindly, include the acknowledgment details before disclosure statement part.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Alampally Sreedevi

Alampally Sreedevi, formerly worked as Assistant Professor in Department of Computer science and engineering in Sri Indu College Of Engineering And Technology, Hyderabad, Telangana. She is pursuing full time Ph.D in Image processing using deep learning technology from KL University in department of Computers Science and Engineering. She has 9+ years of experience in teaching. She has published 20+ research papers in various reputed journals. She is a life member of ISTE. She filled a patent in the area of Machine Learning through IPR India. Her areas of Interest: Data Mining and Big Data Analytics, Machine Learning, Image processing and Deep learning.

Chiranjeevi Manike

Dr. Chiranjeevi Manike, Ph.D., is an Associate Professor and Head of Computer Science & Engineering Department at Koneru Lakshmaiah Education Foundation, Deemed to be University, Hyderabad, Telangana-500075. He received B.Tech and M.Tech degrees in Computer Science & Engineering in 2003 and 2008 from Jawaharlal Nehru Technological University at Hyderabad. He obtained Full-Time Ph.D. in Computer Science & Engineering from Indian Institute of Technology (Indian School of Mines), Dhanbad in 2015. He has more than 16 years of experience in teaching. His primary research interests include Data Science and Big Data Analytics. He has published his research work in peer-reviewed journals and conferences. Received best paper award in an international conference. He is certified Data Scientist from Simplilearn and he has done many certifications in the area of Data Science and Big Data Analytics.