Year: 2024
Type: article
Abstract: Smart agriculture refers to the use of modern information technology, Internet of Things technology, and artificial intelligence to achieve accurate management, efficient operation and sustainable development of the entire process of agricultural production. Smart agriculture mainly includes the application of data collection and analysis, intelligent agricultural machinery, precise fertilization, disease and pest detection and agricultural products traceability. Leaf disease detection and classification are considered as challenging yet important tasks in smart agriculture. Deep leaning methods have been proven effective for these image-based recognition tasks. In this study, two advanced deep learning methods, namely, Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) are combined together to achieve a further improvement. Numerical results demonstrate that the proposed method outperforms both CNN and LSTM variants.
Author: Yibing Pan
Institution: Fuzhou University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W4404250039