Crop Development

Crop development research focuses on improving crop yield prediction and breeding efficiency through advanced data analysis and machine learning. Current efforts utilize deep learning architectures like convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), along with reinforcement learning (RL) for breeding program optimization, and parameter-efficient fine-tuning (PEFT) techniques to adapt large models for specific crop monitoring tasks. These advancements aim to enhance precision agriculture by enabling more accurate yield forecasting, optimized breeding strategies, and improved crop monitoring using remote sensing and ground imagery, ultimately contributing to increased food security and sustainable agricultural practices.