A Metaheuristic Harris Hawks Optimization Algorithm for Weed Detection Using Drone Images

Author:

P.P. Fathimathul Rajeena1ORCID,Ismail Walaa N.23ORCID,Ali Mona A. S.14ORCID

Affiliation:

1. Computer Science Department, College of Computer Science and Information Technology, King Faisal University, Al Ahsa 31982, Saudi Arabia

2. Department of Management Information Systems, College of Business Administration, Al Yamamah University, Riyadh 11512, Saudi Arabia

3. Faculty of Computers and Information, Minia University, Minia 61519, Minia, Egypt

4. Computer Science Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, AlQlubia, Egypt

Abstract

There are several major threats to crop production. As herbicide use has become overly reliant on weed control, herbicide-resistant weeds have evolved and pose an increasing threat to the environment, food safety, and human health. Convolutional neural networks (CNNs) have demonstrated exceptional results in the analysis of images for the identification of weeds from crop images that are captured by drones. Manually designing such neural architectures is, however, an error-prone and time-consuming process. Natural-inspired optimization algorithms have been widely used to design and optimize neural networks, since they can perform a blackbox optimization process without explicitly formulating mathematical formulations or providing gradient information to develop appropriate representations and search paradigms for solutions. Harris Hawk Optimization algorithms (HHO) have been developed in recent years to identify optimal or near-optimal solutions to difficult problems automatically, thus overcoming the limitations of human judgment. A new automated architecture based on DenseNet-121 and DenseNet-201 models is presented in this study, which is called “DenseHHO”. A novel CNN architecture design is devised to classify weed images captured by sprayer drones using the Harris Hawk Optimization algorithm (HHO) by selecting the most appropriate parameters. Based on the results of this study, the proposed method is capable of detecting weeds in unstructured field environments with an average accuracy of 98.44% using DenseNet-121 and 97.91% using DenseNet-201, the highest accuracy among optimization-based weed-detection strategies.

Funder

Ministry of Education in Saudi Arabia

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference41 articles.

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