A Win-Win in Precision Ag
Deep learning techniques for precision agriculture enable the optimization of management practices, including water and chemical applications, benefiting both the farmer and the environment. We’ll explore two use cases. First, we collect high-resolution aerial imagery and use a deep learning based density-estimation approach to count and localize flowering pineapple plants across a field, enabling precision application of chemicals and reducing waste. Second, we use longitudinal aerial imagery of corn and soy fields to detect and predict nutrient deficiency stress. By leveraging a U-Net and Convolutional LSTM, we are able to detect and predict stress up to three weeks earlier.
Jennifer Hobbs is the Director of Machine Learning at IntelinAir, an ag-tech startup using computer vision and machine learning to deliver intelligence and insights to the agriculture industry. Her team is responsible for the development and delivery of models which identify relevant patterns in the field and alerts users to them. She completed her PhD in Physics and Astronomy at Northwestern University. Throughout her career she has been involved in all phases of the machine learning lifecycle, transforming raw data into compelling technology products through data modeling and architecture, pipeline design and management, machine learning, and visualization.