The awardee was announced on 05 May during the 21th EMVA Business Conference in Seville/Spain, where he also had the opportunity to present his work as part of the regular conference program. Pieter Blok is a researcher Deep Learning and Computer Vision at Wageningen University and Research in the Netherlands. Pieter holds a Bachelor and Master of Sciences in Agricultural Engineering from Wageningen University. In December 2022, he obtained his PhD degree at Wageningen University with the thesis entitled “Perception models for selective harvesting robots in fruit and vegetable production”. This PhD thesis was awarded the distinction cum laude. From July 1st, 2023, Pieter will continue his scientific career as an assistant professor at the Laboratory of Field Phenomics at the University of Tokyo in Japan. In Tokyo, Pieter will focus on machine learning and image processing technologies for plant phenotyping.

Awarded Work: Advanced deep learning for harvest robotics

Manual harvesting of fruits and vegetables is a labor-intensive task that suffers from the current shortage of labor in agriculture. To prevent this labor shortage from leading to a reduced supply of fruits and vegetables, robotic alternatives are currently being sought. For a robot to successfully harvest a crop, the fruits and vegetables must be detected using computer vision methods. Unfortunately, to date, most computer vision methods are unable to perform generically when deployed in different fields. This is unfavorable for the commercial success of these robots.

The goal of Pieter Blok’s PhD thesis was to research and develop new machine vision methods that can help a harvesting robot to deal with the mentioned variations and uncertainties. The focus of the PhD thesis was on three tasks that must be performed by every harvesting robot: crop detection, crop size estimation, and crop quality determination.

In the context of crop detection, Blok’s thesis focused on improving the generalization performance of convolutional neural networks (CNNs) to deal with variations within the same crop. Usually, there are many variations within a crop, which can cause the trained CNN to not be able to generalize sufficiently. Therefore, Blok’s research focused on applying different types of data augmentation to optimize the training of a CNN. With geometric data augmentation (rotation, cropping, and scaling of the image), it was demonstrated that the CNN could better generalize to multiple crop varieties.

The next EMVA Business Conference will take place in Gdansk

Traditionally, at the end of the conference it was announced in which city the next, then 22nd EMVA Business Conference will take place. The machine vision industry will meet June 13 – 15, 2024 in Gdansk/Poland.