Log In Sign Up

Finding Berries: Segmentation and Counting of Cranberries using Point Supervision and Shape Priors

by   Peri Akiva, et al.

Precision agriculture has become a key factor for increasing crop yields by providing essential information to decision makers. In this work, we present a deep learning method for simultaneous segmentation and counting of cranberries to aid in yield estimation and sun exposure predictions. Notably, supervision is done using low cost center point annotations. The approach, named Triple-S Network, incorporates a three-part loss with shape priors to promote better fitting to objects of known shape typical in agricultural scenes. Our results improve overall segmentation performance by more than 6.74 results by 22.91 network, we have collected the CRanberry Aerial Imagery Dataset (CRAID), the largest dataset of aerial drone imagery from cranberry fields. This dataset will be made publicly available.


page 1

page 3

page 4

page 5

page 8


Scene and Environment Monitoring Using Aerial Imagery and Deep Learning

Unmanned Aerial vehicles (UAV) are a promising technology for smart farm...

MinneApple: A Benchmark Dataset for Apple Detection and Segmentation

In this work, we present a new dataset to advance the state-of-the-art i...

Deep object detection for waterbird monitoring using aerial imagery

Monitoring of colonial waterbird nesting islands is essential to trackin...

A Self-Training Approach for Point-Supervised Object Detection and Counting in Crowds

In this paper, we propose a novel self-training approach which enables a...

An Enhanced Deep Convolutional Encoder-Decoder Network for Road Segmentation on Aerial Imagery

Object classification from images is among the many practical examples w...

Seg Struct: The Interplay Between Part Segmentation and Structure Inference for 3D Shape Parsing

We propose Seg Struct, a supervised learning framework leveraging the ...