Jonathan Binas (Mila, Montreal); Leonie Luginbuehl (Department of Plant Sciences, University of Cambridge); Yoshua Bengio (Mila)
The growing population and the changing climate will push modern agriculture to its limits in an increasing number of regions on earth. Establishing next-generation sustainable food supply systems will mean producing more food on less arable land, while keeping the environmental impact to a minimum. Modern machine learning methods have achieved super-human performance on a variety of tasks, simply learning from the outcomes of their actions. We propose a path towards more sustainable agriculture, considering plant development an optimization problem with respect to certain parameters, such as yield and environmental impact, which can be optimized in an automated way. Specifically, we propose to use reinforcement learning to autonomously explore and learn ways of influencing the development of certain types of plants, controlling environmental parameters, such as irrigation or nutrient supply, and receiving sensory feedback, such as camera images, humidity, and moisture measurements. The trained system will thus be able to provide instructions for optimal treatment of a local population of plants, based on non-invasive measurements, such as imaging.