1,493 research outputs found
Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection
Selective weeding is one of the key challenges in the field of agriculture
robotics. To accomplish this task, a farm robot should be able to accurately
detect plants and to distinguish them between crop and weeds. Most of the
promising state-of-the-art approaches make use of appearance-based models
trained on large annotated datasets. Unfortunately, creating large agricultural
datasets with pixel-level annotations is an extremely time consuming task,
actually penalizing the usage of data-driven techniques. In this paper, we face
this problem by proposing a novel and effective approach that aims to
dramatically minimize the human intervention needed to train the detection and
classification algorithms. The idea is to procedurally generate large synthetic
training datasets randomizing the key features of the target environment (i.e.,
crop and weed species, type of soil, light conditions). More specifically, by
tuning these model parameters, and exploiting a few real-world textures, it is
possible to render a large amount of realistic views of an artificial
agricultural scenario with no effort. The generated data can be directly used
to train the model or to supplement real-world images. We validate the proposed
methodology by using as testbed a modern deep learning based image segmentation
architecture. We compare the classification results obtained using both real
and synthetic images as training data. The reported results confirm the
effectiveness and the potentiality of our approach.Comment: To appear in IEEE/RSJ IROS 201
Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs
Precision farming robots, which target to reduce the amount of herbicides
that need to be brought out in the fields, must have the ability to identify
crops and weeds in real time to trigger weeding actions. In this paper, we
address the problem of CNN-based semantic segmentation of crop fields
separating sugar beet plants, weeds, and background solely based on RGB data.
We propose a CNN that exploits existing vegetation indexes and provides a
classification in real time. Furthermore, it can be effectively re-trained to
so far unseen fields with a comparably small amount of training data. We
implemented and thoroughly evaluated our system on a real agricultural robot
operating in different fields in Germany and Switzerland. The results show that
our system generalizes well, can operate at around 20Hz, and is suitable for
online operation in the fields.Comment: Accepted for publication at IEEE International Conference on Robotics
and Automation 2018 (ICRA 2018
Simulation of near infrared sensor in unity for plant-weed segmentation classification
Weed spotting through image classification is one of the methods applied in precision agriculture to increase efficiency in crop damage reduction. These classifications are nowadays typically based on deep machine learning with convolutional neural networks (CNN), where a main difficulty is gathering large amounts of labeled data required for the training of these networks. Thus, synthetic dataset sources have been developed including simulations based on graphic engines; however, some data inputs that can improve the performance of CNNs like the near infrared (NIR) have not been considered in these simulations. This paper presents a simulation in the Unity game engine that builds fields of sugar beets with weeds. Images are generated to create datasets that are ready to train CNNs for semantic segmentation. The dataset is tested by comparing classification results from the bonnet CNN network trained with synthetic images and trained with real images, both with RGB and RGBN (RGB+near infrared) as inputs. The preliminary results suggest that the addition of the NIR channel to the simulation for plant-weed segmentation can be effectively exploited. These show a difference of 5.75% for the global mean IoU over 820 classified images by including the NIR data in the unity generated dataset
WeedMap: A large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming
We present a novel weed segmentation and mapping framework that processes
multispectral images obtained from an unmanned aerial vehicle (UAV) using a
deep neural network (DNN). Most studies on crop/weed semantic segmentation only
consider single images for processing and classification. Images taken by UAVs
often cover only a few hundred square meters with either color only or color
and near-infrared (NIR) channels. Computing a single large and accurate
vegetation map (e.g., crop/weed) using a DNN is non-trivial due to difficulties
arising from: (1) limited ground sample distances (GSDs) in high-altitude
datasets, (2) sacrificed resolution resulting from downsampling high-fidelity
images, and (3) multispectral image alignment. To address these issues, we
adopt a stand sliding window approach that operates on only small portions of
multispectral orthomosaic maps (tiles), which are channel-wise aligned and
calibrated radiometrically across the entire map. We define the tile size to be
the same as that of the DNN input to avoid resolution loss. Compared to our
baseline model (i.e., SegNet with 3 channel RGB inputs) yielding an area under
the curve (AUC) of [background=0.607, crop=0.681, weed=0.576], our proposed
model with 9 input channels achieves [0.839, 0.863, 0.782]. Additionally, we
provide an extensive analysis of 20 trained models, both qualitatively and
quantitatively, in order to evaluate the effects of varying input channels and
tunable network hyperparameters. Furthermore, we release a large sugar
beet/weed aerial dataset with expertly guided annotations for further research
in the fields of remote sensing, precision agriculture, and agricultural
robotics.Comment: 25 pages, 14 figures, MDPI Remote Sensin
Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution
[No abstract available
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