.png){kind=icon}
.png){kind=icon}
Return
Resource-Efficient Neural Network for Crop Damage Classification in Precision Agriculture
.png){kind=icon}
PRE
AI
M
S
A
M
Y
K
V
DOI：10.1109/tii.2026.3686363.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Timely and accurate crop damage classification (CDC) is vital for informed decision-making in the industry of precision agriculture. Traditional manual methods are slow and unreliable, whereas recent deep learning models, although accurate, are often too computationally intensive for resource-constrained environments. In this study, we present LNetCDC, a lightweight attention-based convolutional neural network tailored for CDC. The architecture integrates an EchoBlock for efficient feature extraction, combined with residual pathways enhanced by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“Channelwise Refine”</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“Dual Gate Attention”</i> modules to emphasize critical spatial and channelwise features. Also, dilated convolutions are incorporated into deeper layers to capture multiscale contextual patterns. We evaluated our LNetCDC on a benchmark crop damage dataset, where it outperformed existing state-of-the-art (SOTA) models in terms of both accuracy and efficiency. Notably, it achieves around 2.3% gain in accuracy with only 0.86 million parameters compared with 1.13 million in the prior SOTA model for CDC. These results demonstrate the effectiveness and suitability of LNetCDC for real-time deployment on industrial edge devices.
Keywords:
Crop damage classification (CDC)
precision agriculture
resource-efficient learning
smart agriculture
Journal
IF:
9.9
Papers:
8.3K
Citations:
6.0W
Organization
Cited Papers