13,964 research outputs found
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
Designing a fruit identification algorithm in orchard conditions to develop robots using video processing and majority voting based on hybrid artificial neural network
The first step in identifying fruits on trees is to develop garden robots for different purposes
such as fruit harvesting and spatial specific spraying. Due to the natural conditions of the fruit
orchards and the unevenness of the various objects throughout it, usage of the controlled conditions
is very difficult. As a result, these operations should be performed in natural conditions, both
in light and in the background. Due to the dependency of other garden robot operations on the
fruit identification stage, this step must be performed precisely. Therefore, the purpose of this
paper was to design an identification algorithm in orchard conditions using a combination of video
processing and majority voting based on different hybrid artificial neural networks. The different
steps of designing this algorithm were: (1) Recording video of different plum orchards at different
light intensities; (2) converting the videos produced into its frames; (3) extracting different color
properties from pixels; (4) selecting effective properties from color extraction properties using
hybrid artificial neural network-harmony search (ANN-HS); and (5) classification using majority
voting based on three classifiers of artificial neural network-bees algorithm (ANN-BA), artificial
neural network-biogeography-based optimization (ANN-BBO), and artificial neural network-firefly
algorithm (ANN-FA). Most effective features selected by the hybrid ANN-HS consisted of the third
channel in hue saturation lightness (HSL) color space, the second channel in lightness chroma hue
(LCH) color space, the first channel in L*a*b* color space, and the first channel in hue saturation
intensity (HSI). The results showed that the accuracy of the majority voting method in the best execution
and in 500 executions was 98.01% and 97.20%, respectively. Based on different performance evaluation
criteria of the classifiers, it was found that the majority voting method had a higher performance.European Union (EU) under Erasmus+ project entitled
“Fostering Internationalization in Agricultural Engineering in Iran and Russia” [FARmER] with grant
number 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JPinfo:eu-repo/semantics/publishedVersio
Supervised Classification: Quite a Brief Overview
The original problem of supervised classification considers the task of
automatically assigning objects to their respective classes on the basis of
numerical measurements derived from these objects. Classifiers are the tools
that implement the actual functional mapping from these measurements---also
called features or inputs---to the so-called class label---or output. The
fields of pattern recognition and machine learning study ways of constructing
such classifiers. The main idea behind supervised methods is that of learning
from examples: given a number of example input-output relations, to what extent
can the general mapping be learned that takes any new and unseen feature vector
to its correct class? This chapter provides a basic introduction to the
underlying ideas of how to come to a supervised classification problem. In
addition, it provides an overview of some specific classification techniques,
delves into the issues of object representation and classifier evaluation, and
(very) briefly covers some variations on the basic supervised classification
task that may also be of interest to the practitioner
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