Enhancing food crop classification in agriculture through dipper throat optimization and deep learning with remote sensing

Title

Enhancing food crop classification in agriculture through dipper throat optimization and deep learning with remote sensing

Creator

Antony A.; R G.K.

Description

Remote sensing images (RSIs), a keystone of modern agricultural technology, refer to spectral or visual data captured from drones, satellites, or aircraft without direct physical contact with the Earth's surface. These images provide a wide-ranging view of agricultural landscapes, providing valuable insights into land use, crop health, and environmental conditions. Agricultural food crop classification, a vital application within precision agriculture, includes the detection and classification of different crops cultivated in a certain region. Traditionally reliant on manual techniques, the development of technologies, particularly the incorporation of RSIs, has revolutionized this process. Agricultural food crop classification has become more sophisticated and automated by harnessing the wealth of data received from RS, which facilitates precise management and monitoring of crops on a large scale. Deep learning (DL), a branch of artificial intelligence, plays a more effective role in these synergies. The incorporation of DL into the RSI analysis enables high-precision and efficient detection of various crop types, assisting more informed decision-making in agriculture. This study proposes a new Dipper Throat Optimization Algorithm with Deep Learning based Food Crop Classification (DTOADL-FCC) algorithm using Remote Sensing Imaging for Agricultural Resource Management. The DTOADL-FCC method aims to apply DL algorithms for the classification of different crop types. In the DTOADL-FCC method, fully convolutional network (FCN) based segmentation process is performed. Next, the DTOADL-FCC method exploits the SE-ResNet model for learning intrinsic and complex features. The DTOADL-FCC method makes use of DTOA for the hyperparameter tuning process. Lastly, the classification of crop types takes place using the extreme learning machine (ELM) model. The study utilizes mathematical formulations including activation functions, loss functions, fitness calculations, and iterative update processes. A brief set of simulations showcases that the DTOADL-FCC method achieves remarkable performance over other techniques with much improved results. 2024 The Author(s)

Source

e-Prime - Advances in Electrical Engineering, Electronics and Energy, Vol-9

Date

2024-01-01

Publisher

Elsevier Ltd

Subject

Deep learning; Dipper throat optimization; Food crop classification; Hyper-spectral Image; Hyperparameter tuning; Remote sensing image

Coverage

Antony A., Department of Computer Science & Engineering, CHRIST (Deemed to be University), School of Engineering and Technology Kengeri Campus, Bangalore, 560074, India; R G.K., Department of Computer Science & Engineering, CHRIST (Deemed to be University), School of Engineering and Technology Kengeri Campus, Bangalore, 560074, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 27726711

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.1016/j.prime.2024.100732

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201486291&doi=10.1016%2fj.prime.2024.100732&partnerID=40&md5=4c00ffdb81e715b66a7a3acaf633407e