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Trait measurement is critical for the plant breeding and agriculturalproduction pipeline. Typically, a suite of plant traits is measured usinglaborious manual measurements and then used to train and/or validate higherthroughput trait estimation techniques. Here, we introduce a relatively simpleconvolutional neural network (CNN) model that accepts multiple sensor inputsand predicts multiple continuous trait outputs - i.e. a multi-input,multi-output CNN (MIMO-CNN). Further, we introduce deformable convolutionallayers into this network architecture (MIMO-DCNN) to enable the model toadaptively adjust its receptive field, model complex variable geometrictransformations in the data, and fine-tune the continuous trait outputs. Weexamine how the MIMO-CNN and MIMO-DCNN models perform on a multi-input (i.e.RGB and depth images), multi-trait output lettuce dataset from the 2021Autonomous Greenhouse Challenge. Ablation studies were conducted to examine theeffect of using single versus multiple inputs, and single versus multipleoutputs. The MIMO-DCNN model resulted in a normalized mean squared error (NMSE)of 0.068 - a substantial improvement over the top 2021 leaderboard score of0.081. Open-source code is provided.

Simultaneously Predicting Multiple Plant Traits from Multiple Sensors via Deformable CNN Regression