Deep transfer learning with gravitational search algorithm for enhanced plant disease classification

Mehdhar S A M Al-Gaashani; Nagwan Abdel Samee; Reem Alkanhel; Ghada Atteia; Hanaa A Abdallah; Asadulla Ashurov; Mohammed Saleh Ali Muthanna

doi:10.1016/j.heliyon.2024.e28967

Deep transfer learning with gravitational search algorithm for enhanced plant disease classification

Heliyon. 2024 Apr 2;10(7):e28967. doi: 10.1016/j.heliyon.2024.e28967. eCollection 2024 Apr 15.

Authors

Mehdhar S A M Al-Gaashani¹, Nagwan Abdel Samee², Reem Alkanhel², Ghada Atteia², Hanaa A Abdallah², Asadulla Ashurov³, Mohammed Saleh Ali Muthanna⁴

Affiliations

¹ School of Resources and Environment, University of Electronic Science and Technology of China, 4 1st Ring Rd East 2 Section, Chenghua District, Chengdu, 610056, Sichuan, China.
² Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
³ School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.
⁴ Institute of Computer Technologies and Information Security, Southern Federal University, 344006, Taganrog, Russia.

Abstract

Plant diseases annually cause damage and loss of much of the crop, if not its complete destruction, and this constitutes a significant challenge for farm owners, governments, and consumers alike. Therefore, identifying and classifying diseases at an early stage is very important in order to sustain local and global food security. In this research, we designed a new method to identify plant diseases by combining transfer learning and Gravitational Search Algorithm (GSA). Two state-of-the-art pretrained models have been adopted for extracting features in this study, which are MobileNetV2 and ResNe50V2. Multilayer feature extraction is applied in this study to ensure representations of plant leaves from different levels of abstraction for precise classification. These features are then concatenated and passed to GSA for optimizing them. Finally, optimized features are passed to Multinomial Logistic Regression (MLR) for final classification. This integration is essential for categorizing 18 different types of infected and healthy leaf samples. The performance of our approach is strengthened by a comparative analysis that incorporates features optimized by the Genetic Algorithm (GA). Additionally, the MLR algorithm is contrasted with K-Nearest Neighbors (KNN). The empirical findings indicate that our model, which has been refined using GSA, achieves very high levels of precision. Specifically, the average precision for MLR is 99.2%, while for KNN it is 98.6%. The resulting results significantly exceed those achieved with GA-optimized features, thereby highlighting the superiority of our suggested strategy. One important result of our study is that we were able to decrease the number of features by more than 50%. This reduction greatly reduces the processing requirements without sacrificing the quality of the diagnosis. This work presents a robust and efficient approach to the early detection of plant diseases. The work demonstrates the utilization of sophisticated computational methods in agriculture, enabling the development of novel data-driven strategies for plant health management, therefore enhancing worldwide food security.

Keywords: Convolutional neural network; Deep learning; Gravitational search algorithm; Plant disease; Transfer learning.