Smart Farming: Using End-To-End Deep Learning to Estimate the Size of Broccoli Heads
A.C. Rijken (TU Delft - Electrical Engineering, Mathematics and Computer Science)
C.C.S. Liem – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Jeroen Wildenbeest – Mentor
X. Zhang – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Broccoli farming requires accurate estimations of the head diameter to determine when crops are ready to be harvested. An existing growth prediction model was trained on data gathered at Verdonk Broccoli. This paper explores an alternative way of gathering the data needed to train the prediction model, namely by using an end-to-end deep learning model to estimate the diameter of a broccoli head based on an image and the camera height.
This new method was first evaluated by comparing the original diameter measurements with the model outputs. The impact of the method was then assessed by examining the accuracy of the prediction model when trained on the data generated using this approach.
The results show that this alternative method can achieve at least the same accuracy as the original approach, but further improvements in accuracy are limited by the dataset used to train the model.