Canopy anomaly classification using Hybrid ML, a case study on potatoes

Crop canopy reflectance is often used as a proxy for crop vitality. While it relatively easy to identify low vitality spots through vegetation indices (e.g. NDVI, WDVI, etc.) automating the identification of the causes of the low vitality spots remains an unsolved challenge. In fact factors that can cause a drop (or an increment) in vegetation indices, for example water and nitrogen abiotic stress, and biotic stresses like soil and air-borne diseases, and weeds. The objective of this project is to create a model to detect the presence low vigor (e.g. poor spots on an NDVI map) and identify its cause for potato crops.

We are developing a hybrid model (Scientific ML) composed a recurrent neural network trained on a synthetic data set generated using a potato growth model (Tipstar), coupled to a canopy reflectance model(PROSAIL). The model will consume time series data of multispectral signatures as well as data on crop management (e.g. fertilization, water stress, maturity class), weather and soil to facilitate the identification of the anomaly. The appearance of different stresses at different times in the season will be a major driver of the predicted stress factor, for example low emergence will cause an initial decrease in canopy vigor indicators — like NVDI — that will decrease as the season proceeds and canopy will close. The model will be validated on experimental data described below.