Precision Agriculture

Precision agriculture employs advanced technologies to optimize farming practices, aiming to maximize yields while minimizing resource use and environmental impact. Current research heavily utilizes machine learning, particularly convolutional neural networks (CNNs) and vision transformers, along with other algorithms like XGBoost and SVMs, to analyze data from various sources including satellite imagery, drone surveys, and ground-based sensors for tasks such as weed detection, disease prediction, and soil moisture estimation. This data-driven approach enables more precise and efficient management of resources like fertilizers, pesticides, and irrigation, leading to improved crop production and sustainability. The integration of robotics and autonomous systems further enhances the capabilities of precision agriculture, automating tasks like spraying and data collection.