Metaheuristic Optimization for Improving Weed Detection in Wheat Images Captured by Drones

El-Sayed M. El-Kenawy (), Nima Khodadadi, Seyedali Mirjalili (), Tatiana Makarovskikh, Mostafa Abotaleb, Faten Khalid Karim, Hend K. Alkahtani (), Abdelaziz A. Abdelhamid, Marwa M. Eid, Takahiko Horiuchi, Abdelhameed Ibrahim and Doaa Sami Khafaga
Additional contact information
El-Sayed M. El-Kenawy: Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
Nima Khodadadi: Department of Civil and Environmental Engineering, Florida International University, Miami, FL, USA
Seyedali Mirjalili: Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, Brisbane QLD 4006, Australia
Tatiana Makarovskikh: Department of System Programming, South Ural State University, Chelyabinsk, Russia
Mostafa Abotaleb: Department of System Programming, South Ural State University, Chelyabinsk, Russia
Faten Khalid Karim: Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Hend K. Alkahtani: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Abdelaziz A. Abdelhamid: Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
Marwa M. Eid: Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, Egypt
Takahiko Horiuchi: Graduate School of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
Abdelhameed Ibrahim: Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
Doaa Sami Khafaga: Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

Mathematics, 2022, vol. 10, issue 23, 1-30

Abstract: Background and aim: Machine learning methods are examined by many researchers to identify weeds in crop images captured by drones. However, metaheuristic optimization is rarely used in optimizing the machine learning models used in weed classification. Therefore, this research targets developing a new optimization algorithm that can be used to optimize machine learning models and ensemble models to boost the classification accuracy of weed images. Methodology: This work proposes a new approach for classifying weed and wheat images captured by a sprayer drone. The proposed approach is based on a voting classifier that consists of three base models, namely, neural networks (NNs), support vector machines (SVMs), and K-nearest neighbors (KNN). This voting classifier is optimized using a new optimization algorithm composed of a hybrid of sine cosine and grey wolf optimizers. The features used in training the voting classifier are extracted based on AlexNet through transfer learning. The significant features are selected from the extracted features using a new feature selection algorithm. Results: The accuracy, precision, recall, false positive rate, and kappa coefficient were employed to assess the performance of the proposed voting classifier. In addition, a statistical analysis is performed using the one-way analysis of variance (ANOVA), and Wilcoxon signed-rank tests to measure the stability and significance of the proposed approach. On the other hand, a sensitivity analysis is performed to study the behavior of the parameters of the proposed approach in achieving the recorded results. Experimental results confirmed the effectiveness and superiority of the proposed approach when compared to the other competing optimization methods. The achieved detection accuracy using the proposed optimized voting classifier is 97.70%, F-score is 98.60%, specificity is 95.20%, and sensitivity is 98.40%. Conclusion: The proposed approach is confirmed to achieve better classification accuracy and outperforms other competing approaches.

Keywords: smart farming; metaheuristic optimization; weed detection; machine learning; sine cosine algorithm; grey wolf optimization algorithms (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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