A Knowledge-Enhanced Object Detection for Sustainable Agriculture

Abstract

The integration of autonomous aerial vehicles (AAVs) in agriculture has advanced precision farming by enhancing the ability to monitor and optimize agricultural plots. Object detection—critical for identifying crops, pests, and diseases–presents challenges due to data availability and varying environmental conditions. To address these challenges, we propose a Deep Learning framework tailored to agricultural contexts, utilizing domain-specific knowledge from AAV imagery. Our framework uses a knowledge base of visual features and loss values from multiple deep-learning models during the training phase to choose the most effective model for the testing phase. This approach improves model adaptability and accuracy across diverse agricultural scenarios. Evaluated on a comprehensive dataset of AAV-captured images covering various crop types and conditions, our model shows superior performance compared to state-of-the-art techniques. This demonstrates the value of integrating domain knowledge into deep learning for enhancing object detection, ultimately advancing agricultural efficiency, supporting sustainable resource management, and reducing environmental impact.

Keywords

• Agriculture
• knowledge guided deep learning
• object detection
• remote sensing
• sustainability