This article presents GrowliFlower, a georeferenced, image-based UAV time
series dataset of two monitored cauliflower fields of size 0.39 and 0.60 ha
acquired in 2020 and 2021. The dataset contains RGB and multispectral
orthophotos from which about 14,000 individual plant coordinates are derived
and provided. The coordinates enable the dataset users the extraction of
complete and incomplete time series of image patches showing individual plants.
The dataset contains collected phenotypic traits of 740 plants, including the
developmental stage as well as plant and cauliflower size. As the harvestable
product is completely covered by leaves, plant IDs and coordinates are provided
to extract image pairs of plants pre and post defoliation, to facilitate
estimations of cauliflower head size. Moreover, the dataset contains
pixel-accurate leaf and plant instance segmentations, as well as stem
annotations to address tasks like classification, detection, segmentation,
instance segmentation, and similar computer vision tasks. The dataset aims to
foster the development and evaluation of machine learning approaches. It
specifically focuses on the analysis of growth and development of cauliflower
and the derivation of phenotypic traits to foster the development of automation
in agriculture. Two baseline results of instance segmentation at plant and leaf
level based on the labeled instance segmentation data are presented. The entire
data set is publicly available.

本文介绍了一个地理参考、基于图像的 UAV 时间序列数据集，该数据集包含两个不同尺寸的花椰菜田的 RGB 和多光谱正射图像，740 棵植物的表型特征，以及像素准确的叶片和植物实例分割，旨在促进农业自动化的发展和评估机器学习方法。

GrowliFlower：一组用于菜花生长分析的图像时序数据集

GrowliFlower: An image time series dataset for GROWth analysis of cauLIFLOWER

For a globally recognized planting breeding organization, manually-recorded
field observation data is crucial for plant breeding decision making. However,
certain phenotypic traits such as plant color, height, kernel counts, etc. can
only be collected during a specific time-window of a crop's growth cycle. Due
to labor-intensive requirements, only a small subset of possible field
observations are recorded each season. To help mitigate this data collection
bottleneck in wheat breeding, we propose a novel deep learning framework to
accurately and efficiently count wheat heads to aid in the gathering of
real-time data for decision making. We call our model WheatNet and show that
our approach is robust and accurate for a wide range of environmental
conditions of the wheat field. WheatNet uses a truncated MobileNetV2 as a
lightweight backbone feature extractor which merges feature maps with different
scales to counter image scale variations. Then, extracted multi-scale features
go to two parallel sub-networks for simultaneous density-based counting and
localization tasks. Our proposed method achieves an MAE and RMSE of 3.85 and
5.19 in our wheat head counting task, respectively, while having significantly
fewer parameters when compared to other state-of-the-art methods. Our
experiments and comparisons with other state-of-the-art methods demonstrate the
superiority and effectiveness of our proposed method.

本文介绍了一种基于深度学习的新型框架 WheatNet，能够快速高效地计算麦田中小麦穗的数量和位置，并在植物育种决策过程中提供实时数据收集的帮助，解决手动记录植物观测数据带来的劳动密集性问题。

WheatNet: 一种用于高通量基于图像的小麦穗检测与计数的轻量级卷积神经网络

WheatNet: A Lightweight Convolutional Neural Network for High-throughput  Image-based Wheat Head Detection and Counting

The extraction of phenotypic traits is often very time and labour intensive.
Especially the investigation in viticulture is restricted to an on-site
analysis due to the perennial nature of grapevine. Traditionally skilled
experts examine small samples and extrapolate the results to a whole plot.
Thereby different grapevine varieties and training systems, e.g. vertical shoot
positioning (VSP) and semi minimal pruned hedges (SMPH) pose different
challenges. In this paper we present an objective framework based on automatic
image analysis which works on two different training systems. The images are
collected semi automatic by a camera system which is installed in a modified
grape harvester. The system produces overlapping images from the sides of the
plants. Our framework uses a convolutional neural network to detect single
berries in images by performing a semantic segmentation. Each berry is then
counted with a connected component algorithm. We compare our results with the
Mask-RCNN, a state-of-the-art network for instance segmentation and with a
regression approach for counting. The experiments presented in this paper show
that we are able to detect green berries in images despite of different
training systems. We achieve an accuracy for the berry detection of 94.0% in
the VSP and 85.6% in the SMPH.

文章介绍了一种基于自动图像分析的客观框架，使用卷积神经网络检测图像中的浆果并进行计数，可以在葡萄园不同种植系统中检测到绿色浆果，VSP 的检测准确率为 94.0％，SMPH 为 85.6％。