This paper describes the role of the autonomous underwater vehicle (AUV) Sirius on a research cruise to survey drowned reefs along the shelf edge of the Great Barrier Reef in Queensland, Australia. The primary function of the AUV was to provide georeferenced, high-resolution optical validation of seabed interpretations based on acoustic data. We describe the AUV capabilities and its operation in the context of the multiple systems used in the cruise. The data processing pipeline involved in generating simultaneous localization and mapping–based navigation, large-scale, three-dimensional visualizations, and automated classification of survey data is briefly described. We also present preliminary results illustrating the type of data products possible with our system and how they can inform the science driving the cruise. © 2010 Wiley Periodicals, Inc.

Autonomous underwater vehicle–assisted surveying of drowned reefs on the shelf edge of the Great Barrier Reef, Australia

https://pubag.nal.usda.gov/download/46603/PDF

Review: Sensing technologies for precision specialty crop production

Effective crop protection requires early and accurate detection of biotic stress. In recent years, remarkable results have been achieved in the early detection of weeds, plant diseases and insect pests in crops. These achievements are related both to the development of non-invasive, high resolution optical sensors and data analysis methods that are able to cope with the resolution, size and complexity of the signals from these sensors. Several methods of machine learning have been utilized for precision agriculture such as support vector machines and neural networks for classification (supervised learning); k-means and self-organizing maps for clustering (unsupervised learning). These methods are able to calculate both linear and non-linear models, require few statistical assumptions and adapt flexibly to a wide range of data characteristics. Successful applications include the early detection of plant diseases based on spectral features and weed detection based on shape descriptors with supervised or unsupervised learning methods. This review gives a short introduction into machine learning, analyses its potential for precision crop protection and provides an overview of instructive examples from different fields of precision agriculture.

A review of advanced machine learning methods for the detection of biotic stress in precision crop protection

In recent years, weeds have been responsible for most agricultural yield losses. To deal with this threat, farmers resort to spraying the fields uniformly with herbicides. This method not only requires huge quantities of herbicides but impacts the environment and human health. One way to reduce the cost and environmental impact is to allocate the right doses of herbicide to the right place and at the right time (precision agriculture). Nowadays, unmanned aerial vehicles (UAVs) are becoming an interesting acquisition system for weed localization and management due to their ability to obtain images of the entire agricultural field with a very high spatial resolution and at a low cost. However, despite significant advances in UAV acquisition systems, the automatic detection of weeds remains a challenging problem because of their strong similarity to the crops. Recently, a deep learning approach has shown impressive results in different complex classification problems. However, this approach needs a certain amount of training data, and creating large agricultural datasets with pixel-level annotations by an expert is an extremely time-consuming task. In this paper, we propose a novel fully automatic learning method using convolutional neuronal networks (CNNs) with an unsupervised training dataset collection for weed detection from UAV images. The proposed method comprises three main phases. First, we automatically detect the crop rows and use them to identify the inter-row weeds. In the second phase, inter-row weeds are used to constitute the training dataset. Finally, we perform CNNs on this dataset to build a model able to detect the crop and the weeds in the images. The results obtained are comparable to those of traditional supervised training data labeling, with differences in accuracy of 1.5% in the spinach field and 6% in the bean field.

Deep Learning with Unsupervised Data Labeling for Weed Detection in Line Crops in UAV Images

The angular momentum Jinc of a light beam can be changed by passage through a slab of crystal. When the beam is incident along the optic axis of a biaxial crystal, which may also possess optical activity (chirality), the final angular momentum J can have both orbital (Jorb) and spin (Jsp) contributions, which we calculate paraxially exactly for arbitrary biaxiality and chirality and initially uniformly polarized beams with circular symmetry. For the familiar special case of a non-chiral crystal with fully developed conical-refraction rings, J is purely orbital and equal to Jinc/2, reflecting an interesting singularity structure in the beam. Explicit formulas and numerical computations are presented for a Gaussian incident beam. The change in angular momentum results in a torque on the crystal, along the axis of the incident beam. An additional, much larger, torque, about an axis lying in the slab, arises from the offset of the cone of conical refraction relative to the incident beam.

https://iopscience.iop.org/article/10.1088/1464-4258/7/11/011/pdf

Orbital and spin angular momentum in conical diffraction

Weed detection in multi-spectral images of cotton fields

Image filtering in moving window in DCT domain has proven its capability in image edge preserving denosing. For the highest noise suppression capability, such a filtering requires, in principle, optimal selection of the size of the moving window for each particular image. In order to overcome this drawback, a combination of signal wavelet expansion (sub band decomposition) and moving window filtering in DCT domain is suggested and experimentally tested on a number on synthetic and real life images.

A hybrid transform method for image denoising

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Weed detection in multi-spectral images of cotton fields

A hybrid transform method for image denoising