Abstract
Unmanned aerial vehicle (UAV) based remote sensing is gaining momentum worldwide in a variety of agricultural and environmental monitoring and modelling applications. At the same time, the increasing availability of yield monitoring devices in harvesters enables input-target mapping of in-season RGB and crop yield data in a resolution otherwise unattainable by openly availabe satellite sensor systems. Using time series UAV RGB and weather data collected from nine crop fields in Pori, Finland, we evaluated the feasibility of spatio-temporal deep learning architectures in crop yield time series modelling and prediction with RGB time series data. Using Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) networks as spatial and temporal base architectures, we developed and trained CNN-LSTM, convolutional LSTM and 3D-CNN architectures with full 15 week image frame sequences from the whole growing season of 2018. The best performing architecture, the 3D-CNN, was then evaluated with several shorter frame sequence configurations from the beginning of the season. With 3D-CNN, we were able to achieve 218.9 kg/ha mean absolute error (MAE) and 5.51% mean absolute percentage error (MAPE) performance with full length sequences. The best shorter length sequence performance with the same model was 292.8 kg/ha MAE and 7.17% MAPE with four weekly frames from the beginning of the season.
Highlights
The abundance of modern sensor and communication technology already present in production facilities and similar highly connected environments has seeped into the realm of agriculture
Train and compare several spatio-temporal models to determine the most suitable model for intra-field yield modelling from a selection of models already utilized in the context of spatio-temporal modelling with remote sensing data
The model performing worst was somewhat surprisingly the ConvLSTM, showing performance inferior even to the pretrained Convolutional Neural Networks (CNN) trained with just point-in-time data
Summary
The abundance of modern sensor and communication technology already present in production facilities and similar highly connected environments has seeped into the realm of agriculture. Nationally and locally available data generating remote sensing systems are in place, providing relevant data for optimizing several agricultural outputs. On the global and national scale, satellite systems (Sentinel and Landsat missions, for example) provide temporally relevant spatial data about visible land surfaces. Modern data-based modeling techniques benefit from increased resolution of spatial data, as they are able to better learn the relevant features in performing a given task, e.g., intra-field yield prediction. Feeding this data to automated processing and decision making pipelines is a vital part of Smart Farming enabling Decision Support Systems [1]
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