10.12.2019 19:00 – YouTube Live Download iCal

Webinar: Deep Learning and SfM: a promising framework for agricultural automation – an example with grapes (and some apples)

Thiago Santos from the Brazilian Agricultural Research Corporation, Embrapa, will present a webinar, "Deep Learning and SfM: a promising framework for agricultural automation – an example with grapes (and some apples)," on Tuesday, December 10, 2019 at 3:00pm/15:00h BRT (UTC -3:00) as part of the IEEE RAS Technical Committee on Agricultural Robotics and Automation's (AgRA) webinar series.

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This webinar will also be broadcast with YouTube Live, which is an option from Zoom (registration not necessary if you are joining via YouTube Live).

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All details, including a time zone converter, technical details, and presenter biography available here:



Title: Deep Learning and SfM: a promising framework for agricultural automation – an example with grapes (and some apples)


Abstract: Differently of industrial applications, agricultural environments lack structured spatial data and well-defined settings.

Such an environment is complex and presents large variations between fields and even intra-field, so intelligent systems are needed for its dynamic interpretation. For example, in fruit growing, not even the number and the placement of fruits is known in advance, which imposes a challenge for any automation attempt. Fortunately, recent computer vision advances are able to automate the recovery of the 3-D structure of crop fields, by using techniques as structure-from-motion (SfM) and SLAM, and detect and classify objects of interest, such as plants, leaves and fruits, by using state-of-the art techniques in machine vision such as Convolutional Neural Networks (CNNs). In this presentation, we will focus on wine grapes, a crop presenting large variability in shape, color, and compactness. We can successfully detect grape clusters, and segment them using state-of-the-art CNNs. In a dataset containing 408 grape clusters from images taken on field, we have reached a F1-score up to 0.91 for instance segmentation, a fine separation of each cluster from other structures in the image that allows a more accurate assessment of fruit size and shape. We will also show that clusters can be identified and tracked along video sequences recording orchards rows, using 3-D information from a SfM system. We also will present a public dataset containing grape clusters properly annotated in 300 images and a novel annotation methodology for segmentation of complex objects in natural images. This computer vision pipeline can be replicated for different crops and production systems, and we will present a preliminary work with apples to illustrate that such a pipeline can be employed on the development of sensing components for several agricultural and environmental applications.