Machine vision detection of pests, diseases, and weeds: A review

Most of mankind’s living and workspace have been or going to be blended with smart technologies like the Internet of Things. The industrial domain has embraced automation technology, but agriculture automation is still in its infancy since the espousal has high investment costs and little commercialization of innovative technologies due to reliability issues. Machine vision is a potential technique for surveillance of crop health which can pinpoint the geolocation of crop stress in the field. Early statistics on crop health can hasten prevention strategies such as pesticide, fungicide applications to reduce the pollution impact on water, soil, and air ecosystems. This paper condenses the proposed machine vision relate research literature in agriculture to date to explore various pests, diseases, and weeds detection mechanisms

Direct N-body Kernels for Multicore Platforms

Abstract—We present an inter-architectural comparison of single- and double-precision direct n-body implementations on modern multicore platforms, including those based on the Intel Nehalem and AMD Barcelona systems, the Sony-Toshiba-IBM PowerXCell/8i processor, and NVIDIA Tesla C870 and C1060 GPU systems. We compare our implementations across platforms on a variety of proxy measures, including performance, coding complexity, and energy efficiency. I

On the efficacy of handcrafted and deep features for seed image classification

Computer vision techniques have become important in agriculture and plant sciences due to their wide variety of applications. In particular, the analysis of seeds can provide meaningful information on their evolution, the history of agriculture, the domestication of plants, and knowledge of diets in ancient times. This work aims to propose an exhaustive comparison of several different types of features in the context of multiclass seed classification, leveraging two public plant seeds data sets to classify their families or species. In detail, we studied possible optimisations of five traditional machine learning classifiers trained with seven different categories of handcrafted features. We also fine-tuned several well-known convolutional neural networks (CNNs) and the recently proposed SeedNet to determine whether and to what extent using their deep features may be advantageous over handcrafted features. The experimental results demonstrated that CNN features are appropriate to the task and representative of the multiclass scenario. In particular, SeedNet achieved a mean F-measure of 96%, at least. Nevertheless, several cases showed satisfactory performance from the handcrafted features to be considered a valid alternative. In detail, we found that the Ensemble strategy combined with all the handcrafted features can achieve 90.93% of mean F-measure, at least, with a considerably lower amount of times. We consider the obtained results an excellent preliminary step towards realising an automatic seeds recognition and classification framework

Archaeology at Ayers Town: An Early Federal Period Community in the Catawba Nation

Research Report No. 37, Research Laboratories of Archaeology, University of North Carolina at Chapel Hill. Reports in this series discuss the findings of archaeological excavations and research projects undertaken by the RLA between 1984 and present

Developing a bioinformatics framework for proteogenomics

In the last 15 years, since the human genome was first sequenced, genome sequencing and annotation have continued to improve. However, genome annotation has not kept up with the accelerating rate of genome sequencing and as a result there is now a large backlog of genomic data waiting to be interpreted both quickly and accurately. Through advances in proteomics a new field has emerged to help improve genome annotation, termed proteogenomics, which uses peptide mass spectrometry data, enabling the discovery of novel protein coding genes, as well as the refinement and validation of known and putative protein-coding genes. The annotation of genomes relies heavily on ab initio gene prediction programs and/or mapping of a range of RNA transcripts. Although this method provides insights into the gene content of genomes it is unable to distinguish protein-coding genes from putative non-coding RNA genes. This problem is further confounded by the fact that only 5% of the public protein sequence repository at UniProt/SwissProt has been curated and derived from actual protein evidence. This thesis contends that it is critically important to incorporate proteomics data into genome annotation pipelines to provide experimental protein-coding evidence. Although there have been major improvements in proteogenomics over the last decade there are still numerous challenges to overcome. These key challenges include the loss of sensitivity when using inflated search spaces of putative sequences, how best to interpret novel identifications and how best to control for false discoveries. This thesis addresses the existing gap between the use of genomic and proteomic sources for accurate genome annotation by applying a proteogenomics approach with a customised methodology. This new approach was applied within four case studies: a prokaryote bacterium; a monocotyledonous wheat plant; a dicotyledonous grape plant; and human. The key contributions of this thesis are: a new methodology for proteogenomics analysis; 145 suggested gene refinements in Bradyrhizobium diazoefficiens (nitrogen-fixing bacteria); 55 new gene predictions (57 protein isoforms) in Vitis vinifera (grape); 49 new gene predictions (52 protein isoforms) in Homo sapiens (human); and 67 new gene predictions (70 protein isoforms) in Triticum aestivum (bread wheat). Lastly, a number of possible improvements for the studies conducted in this thesis and proteogenomics as a whole have been identified and discussed

Yielding to the image: how phenotyping reproductive growth can assist crop improvement and production

Reproductive organs are the main reason we grow and harvest most plant species as crops, yet they receive less attention from phenotyping due to their complexity and inaccessibility for analysis. This review highlights recent progress towards the quantitative high-throughput phenotyping of reproductive development, focusing on three impactful areas that are pivotal for plant breeding and crop production. First, we look at phenotyping phenology, summarizing the indirect and direct approaches that are available. This is essential for analysis of genotype by environment, and to enable effective management interpretation and agronomy and physiological interventions. Second, we look at pollen development and production, in addition to anther characteristics, these are critical points of vulnerability for yield loss when stress occurs before and during flowering, and are of particular interest for hybrid technology development. Third, we elaborate on phenotyping yield components, indirectly or directly during the season, with a numerical or growth related approach and post-harvest processing. Finally, we summarise the opportunities and challenges ahead for phenotyping reproductive growth and their feasibility and impact, with emphasis on plant breeding applications and targeted yield increases

Using a GIS technology to plan an agroforestry sustainable system in Sardinia

This study was conducted with the aim to quantify the spread of livestock agroforestry in a Mediterranean ecosystem (island of Sardinia, Italy) and evaluate its sustainability in terms of grazing impact. By using GIS software ArcMap 10.2.2, the map of Sardinia vegetal landscape, obtained by information of Sardinia nature map based on the classification of habitat according to CORINE-Biotopes system, have been overplayed with the map of livestock grazing impact map CAIA developed by INTREGA (spin-off ENEA), to obtain for Meriagos (local agro-silvo-pastoral systems; classified “Dehesa 84.6” according to CORINE-Biotopes system), bushlands and woodlands, the surfaces under grazing and evaluate the extension of overgrazing for each of them