A5002975268- Remote Sensing in Agriculture
- Smart Agriculture and AI
- Climate change impacts on agriculture
- Global Maternal and Child Health
- Remote-Sensing Image Classification
- Hydrological Forecasting Using AI
- Healthcare Systems and Reforms
- Remote Sensing and LiDAR Applications
- Leaf Properties and Growth Measurement
- Energy Load and Power Forecasting
- Hydrology and Drought Analysis
- Child Nutrition and Water Access
University of Technology Sydney
2023-2025
Accurate, reliable and transparent crop yield prediction is crucial for informed decision-making by governments, farmers, businesses regarding food security as well agricultural business management. Deep learning (DL) methods, particularly Long Short-Term Memory networks, have emerged one of the most widely used architectures in studies, providing promising results. Although other sequential DL methods like 1D Convolutional Neural Networks (1D-CNN) Bidirectional long short-term memory...
The timely and reliable prediction of crop yields on a larger scale is crucial for ensuring stable food supply security. In the last few years, many studies have demonstrated that deep learning can offer solutions yield prediction. However, key challenge in applying deep-learning models to their reliance extensive training data, which are often lacking parts world. To address this challenge, study introduces TrAdaBoost.R2, along with fine-tuning domain-adversarial neural network...