An Agricultural Precision Sprayer Deposit Identification System

Data-driven Artificial Intelligence systems are playing an increasingly significant role in the advancement of precision agriculture. Currently, precision sprayers lack fully automated methods to evaluate the effectiveness of their operation, e.g. whether spray has landed on target weeds. In this paper, using an agricultural spot spraying system images were collected from an RGB camera to locate spray deposits on weeds or lettuces. We present an interpretable deep learning pipeline to identify spray deposits on lettuces and weeds without using existing methods such as tracers or water-sensitive papers. We implement a novel stratification and sampling methodology to improve results from a baseline. Using a binary classification head after transfer learning networks, spray deposits are identified with over 90% Area Under the Receiver Operating Characteristic (AUROC). This work offers a data-driven approach for an automated evaluation methodology for the effectiveness of precision sprayers

Colour Gradients and the Colour-Magnitude Relation: Different Properties of Brightest Cluster Galaxies and E/S0 Galaxies in the Sloan Digital Sky Survey

We examine the colour-magnitude relation of approximately 5000 Brightest Cluster Galaxies (BCGs) in the Sloan Digital Sky Survey, and compare with non-BCG E/S0 galaxies. The colour-magnitude and colour-sigma (velocity dispersion) relations are flatter in slope (by a factor of about 2) for BCGs than for non-BCG E/S0s, and the BCGs also tend to be redder by 0.01 magnitudes in g-r. We investigate radial colour gradients in both samples, using the ratio of the de Vaucouleurs radii in the g and r bands. We find BCGs have significantly flatter (by 23%) mean colour gradients than other high luminosity E/S0s. In early-type galaxies, the colour gradients are strongest at intermediate luminosities of Mr=-22. Colour gradients in E/S0s increase with radius (up to 10kpc) and are negatively correlated with 10sigma + Mr (velocity dispersion relative to luminosity) and with mass density. The gradients also tend to decrease with increasing stellar age. These trends are weak or not seen in BCGs, in which the mean colour gradient is low whatever the other properties. We discuss possible explanations, which involve a greater amount of dry merging in the formation history of the BCGs.Comment: 16 pages, 24 figures, revised, accepted for publication in MNRAS

A re-appraisal of the reliability of the 20 m multi-stage shuttle run test

This is the author's PDF version of an article published in European journal of applied physiology in 2007. The original publication is available at www.springerlink.co

The history of star formation and mass assembly in early-type galaxies

We define a volume limited sample of over 14,000 early-type galaxies (ETGs) selected from data release six of the Sloan Digital Sky Survey. The density of environment of each galaxy is robustly measured. By comparing narrow band spectral line indices with recent models of simple stellar populations (SSPs) we investigate trends in the star formation history as a function of galaxy mass (velocity dispersion), density of environment and galactic radius. We find that age, metallicity and alpha-enhancement all increase with galaxy mass and that field ETGs are younger than their cluster counterparts by ~2 Gyr. We find negative radial metallicity gradients for all masses and environments, and positive radial age gradients for ETGs with velocity dispersion over 180 km/s. Our results are qualitatively consistent with a relatively simple picture for ETG evolution in which the low-mass halos accreted by a proto-ETG contained not only gas but also a stellar population. This fossil population is preferentially found at large radii in massive ETGs because the stellar accretions were dissipationless. We estimate that the typical, massive ETG should have been assembled at z < 3.5. The process is similar in the cluster and the field but occurred earlier in dense environments.Comment: 5 pages, 2 figures. Submitted to MNRA

A brief introduction to recent developments in population-based structural health monitoring

This is the final version. Available from the publisher via the DOI in this record.One of the main problems in data-based Structural Health Monitoring (SHM), is the scarcity of measured data corresponding to damage states in the structures of interest. One approach to solving this problem is to develop methods of transferring health inferences and information between structures in an identified population—Population-based SHM (PBSHM). In the case of homogenous populations (sets of nominally-identical structures, like in a wind farm), the idea of the form has been proposed which encodes information about the ideal or typical structure together with information about variations across the population. In the case of sets of disparate structures—heterogeneous populations—transfer learning appears to be a powerful tool for sharing inferences, and is also applicable in the homogenous case. In order to assess the likelihood of transference being meaningful, it has proved useful to develop an abstract representation framework for spaces of structures, so that similarities between structures can formally be assessed; this framework exploits tools from graph theory. The current paper discusses all of these very recent developments and provides illustrative examplesEngineering and Physical Sciences Research Council (EPSRC

Evaluating the Use of Interpretable Quantized Convolutional Neural Networks for Resource-Constrained Deployment

The deployment of Neural Networks on resource-constrained devices for object classification and detection has led to the adoption of network compression methods, such as Quantization. However, the interpretation and comparison of Quantized Neural Networks with their Non-Quantized counterparts remains inadequately explored. To bridge this gap, we propose a novel Quantization Aware eXplainable Artificial Intelligence (XAI) pipeline to effectively compare Quantized and Non-Quantized Convolutional Neural Networks (CNNs). Our pipeline leverages Class Activation Maps (CAMs) to identify differences in activation patterns between Quantized and Non-Quantized. Through the application of Root Mean Squared Error, a subset from the top 5% scoring Quantized and Non-Quantized CAMs is generated, highlighting regions of dissimilarity for further analysis. We conduct a comprehensive comparison of activations from both Quantized and Non-Quantized CNNs, using Entropy, Standard Deviation, Sparsity metric s, and activation histograms. The ImageNet dataset is utilized for network evaluation, with CAM effectiveness assessed through Deletion, Insertion, and Weakly Supervised Object Localization (WSOL). Our findings demonstrate that Quantized CNNs exhibit higher performance in WSOL and show promising potential for real-time deployment on resource-constrained devices

A new Northeast Asian Lynceus (Crustacea: Branchiopoda: Laevicaudata) with uniquely modified thoracopods and an evaluation of DNA barcoding for clam shrimp species identification

A new species of smooth clam shrimp (Branchiopoda: Laevicaudata) from Mongolia and China is described here based on both morphological and genetic differences. The new species, Lynceus grossipedia n. sp., has unique features, including asymmetrically modified male thoracopods (left side thoracopods III-VI), male claspers 'movable finger' (=endopod) with delicate setation, and broad, bicarinate male and female rostrum. Lynceus grossipedia n. sp. is compared with the genera Paralimnetis and Lynceiopsis and a recently described Lynceus from China, also showing modified male thoracopods. Lynceus mandsuricus is declared nomen inquirendum. DNA barcoding is commonly used for speeding up species identification and systematics, but it has not been applied on smooth clam shrimp taxonomy yet. In order to explore the usefulness of DNA barcoding for smooth clam shrimps, we generated new cytochrome c oxidase (COX1) data for 10 Lynceus species. Previous Laevicaudata sequences in Genbank were scarce (~50) and biased, with 62% (n=31) being assigned to a single taxon (i.e., Lynceus macleayanus) and 28% (n=14) not assigned to species. Based on the addition of new barcoding data and the comparison with Genbank data for other clam shrimps, we could conclude that there is a similar distance threshold (=barcoding gap) between species of all three suborders (Spinicaudata, Cyclestherida, and Laevicaudata)