28,806 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
Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network
In fruit production, critical crop management decisions are guided by bloom intensity, i.e., the number of flowers present in an orchard. Despite its importance, bloom intensity is still typically estimated by means of human visual inspection. Existing automated computer vision systems for flower identification are based on hand-engineered techniques that work only under specific conditions and with limited performance. This letter proposes an automated technique for flower identification that is robust to uncontrolled environments and applicable to different flower species. Our method relies on an end-to-end residual convolutional neural network (CNN) that represents the state-of-the-art in semantic segmentation. To enhance its sensitivity to flowers, we fine-tune this network using a single dataset of apple flower images. Since CNNs tend to produce coarse segmentations, we employ a refinement method to better distinguish between individual flower instances. Without any preprocessing or dataset-specific training, experimental results on images of apple, peach, and pear flowers, acquired under different conditions demonstrate the robustness and broad applicability of our method
Brachiaria species identification using imaging techniques based on fractal descriptors
The use of a rapid and accurate method in diagnosis and classification of
species and/or cultivars of forage has practical relevance, scientific and
trade in various areas of study. Thus, leaf samples of fodder plant species
\textit{Brachiaria} were previously identified, collected and scanned to be
treated by means of artificial vision to make the database and be used in
subsequent classifications. Forage crops used were: \textit{Brachiaria
decumbens} cv. IPEAN; \textit{Brachiaria ruziziensis} Germain \& Evrard;
\textit{Brachiaria Brizantha} (Hochst. ex. A. Rich.) Stapf; \textit{Brachiaria
arrecta} (Hack.) Stent. and \textit{Brachiaria spp}. The images were analyzed
by the fractal descriptors method, where a set of measures are obtained from
the values of the fractal dimension at different scales. Therefore such values
are used as inputs for a state-of-the-art classifier, the Support Vector
Machine, which finally discriminates the images according to the respective
species.Comment: 7 pages, 5 figure
DeepWheat: Estimating Phenotypic Traits from Crop Images with Deep Learning
In this paper, we investigate estimating emergence and biomass traits from
color images and elevation maps of wheat field plots. We employ a
state-of-the-art deconvolutional network for segmentation and convolutional
architectures, with residual and Inception-like layers, to estimate traits via
high dimensional nonlinear regression. Evaluation was performed on two
different species of wheat, grown in field plots for an experimental plant
breeding study. Our framework achieves satisfactory performance with mean and
standard deviation of absolute difference of 1.05 and 1.40 counts for emergence
and 1.45 and 2.05 for biomass estimation. Our results for counting wheat plants
from field images are better than the accuracy reported for the similar, but
arguably less difficult, task of counting leaves from indoor images of rosette
plants. Our results for biomass estimation, even with a very small dataset,
improve upon all previously proposed approaches in the literature.Comment: WACV 2018 (Code repository:
https://github.com/p2irc/deepwheat_WACV-2018
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