Deep Learning and Hyperspectral Imaging

Gathering data on crop status by using deep-learning analyses of hyperspectral imaging to improve spray operations.

This Flagship Innovation Experiment’s (FIE) goal is to reduce spraying applications on crops by combining Artificial Intelligence (AI) with hyperspectral imaging, thus creating a supportive decision model to channel the collected data into task maps. In order to apply the right amount at the right time extensive information on the crop status is needed. These data are collected by hyperspectral cameras, capturing the reflection of light on leaves to provide information about pigment concentration, cell structure or infections. Hence, this FIE relies on deep learning technology and analytics to construct and validate cutting edge algorithms, producing new data and thus better classify crop images.

Generative Autoencoder Networks (GANs) are essential for agricultural systems since the window for data acquisition is typically short and the need for quantity is prevalent with data-hungry deep architectures. The deep learning algorithms will be implemented on supercomputers to allow fast training of decision algorithms, while inference will be done on embedded Graphics Processing Unit (GPU) devices. The results provide the necessary input to build task maps which are ultimately used to carry out precise spraying operations.