16,574 research outputs found
Maize-Nutrient-Manager: A mobile phone application for field-specific, balanced nutrient management advisory
To increase productivity and profitability, while limiting nutrient losses and related GHG-emissions, African smallholders need more tailored fertilizer advice. Yet, such advice critically hinges upon – largely lacking – field-level management data, as management is key to efficient fertilizer use. The Maize- Nutrient-Manager (MNM) mobile phone application enables collection of such data at scale, and directly converts this data into actionable advice for the farmer. Focusing on field-level management data, MNM can identify those management practices that are currently imperative for enhancing smallholder farmers’ efficient use of fertilizers in their locality, thereby increasing productivity while reducing greenhouse gas (GHG) emissions. This document describes the background, design principles and development process of then MNM mobile phone application, as well as its pilot use in advisory practice in the Mbozi and Momba districts of Songwe region, Tanzania
Simple identification tools in FishBase
Simple identification tools for fish species were included in the FishBase information system from its inception. Early tools made use of the relational model and characters like fin ray meristics. Soon pictures and drawings were added as a further help, similar to a field guide. Later came the computerization of existing dichotomous keys, again in combination with pictures and other information, and the ability to restrict possible species by country, area, or taxonomic group. Today, www.FishBase.org offers four different ways to identify species. This paper describes these tools with their advantages and disadvantages, and suggests various options for further
development. It explores the possibility of a holistic and integrated computeraided strategy
The Flora Incognita app - interactive plant species identification
Being able to identify plant species is an important factor for understanding biodiversity and its change due to natural and anthropogenic drivers. We discuss the freely available Flora Incognita app for Android, iOS and Harmony OS devices that allows users to interactively identify plant species and capture their observations. Specifically developed deep learning algorithms, trained on an extensive repository of plant observations, classify plant images with yet unprecedented accuracy. By using this technology in a context-adaptive and interactive identification process, users are now able to reliably identify plants regardless of their botanical knowledge level. Users benefit from an intuitive interface and supplementary educational materials. The captured observations in combination with their metadata provide a rich resource for researching, monitoring and understanding plant diversity. Mobile applications such as Flora Incognita stimulate the successful interplay of citizen science, conservation and education
Understanding citizen science and environmental monitoring: final report on behalf of UK Environmental Observation Framework
Citizen science can broadly be defined as the involvement of volunteers in science. Over the past decade there has
been a rapid increase in the number of citizen science initiatives. The breadth of environmental-based citizen
science is immense. Citizen scientists have surveyed for and monitored a broad range of taxa, and also contributed
data on weather and habitats reflecting an increase in engagement with a diverse range of observational science.
Citizen science has taken many varied approaches from citizen-led (co-created) projects with local community
groups to, more commonly, scientist-led mass participation initiatives that are open to all sectors of society. Citizen
science provides an indispensable means of combining environmental research with environmental education and
wildlife recording.
Here we provide a synthesis of extant citizen science projects using a novel cross-cutting approach to objectively
assess understanding of citizen science and environmental monitoring including: 1. Brief overview of knowledge on the motivations of volunteers.
2. Semi-systematic review of environmental citizen science projects in order to understand the variety of
extant citizen science projects.
3. Collation of detailed case studies on a selection of projects to complement the semi-systematic review.
4. Structured interviews with users of citizen science and environmental monitoring data focussing on policy, in
order to more fully understand how citizen science can fit into policy needs.
5. Review of technology in citizen science and an exploration of future opportunities
Crowd-sourced plant occurrence data provide a reliable description of macroecological gradients
Deep learning algorithms classify plant species with high accuracy, and smartphone applications leverage this technology to enable users to identify plant species in the field. The question we address here is whether such crowd-sourced data contain substantial macroecological information. In particular, we aim to understand if we can detect known environmental gradients shaping plant co-occurrences. In this study we analysed 1 million data points collected through the use of the mobile app Flora Incognita between 2018 and 2019 in Germany and compared them with Florkart, containing plant occurrence data collected by more than 5000 floristic experts over a 70-year period. The direct comparison of the two data sets reveals that the crowd-sourced data particularly undersample areas of low population density. However, using nonlinear dimensionality reduction we were able to uncover macroecological patterns in both data sets that correspond well to each other. Mean annual temperature, temperature seasonality and wind dynamics as well as soil water content and soil texture represent the most important gradients shaping species composition in both data collections. Our analysis describes one way of how automated species identification could soon enable near real-time monitoring of macroecological patterns and their changes, but also discusses biases that must be carefully considered before crowd-sourced biodiversity data can effectively guide conservation measures
Generic Paddy Plant Disease Detector (GP2D2): An Application of the Deep-CNN Model
Rice is the primary food for almost half of the world’s population, especially for the people of Asian countries. There is a demand to improve the quality and increase the quantity of rice production to meet the food requirements of the increasing population. Bulk cultivation and quality production of crops need appropriate technology assistance over manual traditional methods. In this work, six popular Deep-CNN architectures, namely AlexNet, VGG-19, VGG-16, InceptionV3, MobileNet, and ResNet-50, are exploited to identify the diseases in paddy plants since they outperform most of the image classification applications. These CNN models are trained and tested with Plant Village dataset for classifying the paddy plant images into one of the four classes namely, Healthy, Brown Spot, Hispa, or Leaf Blast, based on the disease condition. The performance of the chosen architectures is compared with different hyper parameter settings. AlexNet outperformed other convolutional neural networks (CNNs) in this multiclass classification task, achieving an accuracy of 89.4% at the expense of a substantial number of network parameters, indicating the large model size of AlexNet. For developing mobile applications, the ResNet-50 architecture was adopted over other CNNs, since it has a comparatively smaller number of network parameters and a comparable accuracy of 86.1%. A fine-tuned ResNet-50 architecture supported mobile app, “Generic Paddy Plant Disease Detector (GP2D2)” has been developed for the identification of most commonly occurring diseases in paddy plants. This tool will be more helpful for the new generation of farmers in bulk cultivation and increasing the productivity of paddy. This work will give insight into the performance of CNN architectures in rice plant disease detection task and can be extended to other plants too
Next-Generation Field Guides
To conserve species, we must first identify them. Field researchers, land managers, educators, and citizen scientists need up-to-date and accessible tools to identify organisms, organize data, and share observations. Emerging technologies complement traditional, book-form field guides by providing users with a wealth of multimedia data. We review technical innovations of next-generation field guides, including Web-based and stand-alone applications, interactive multiple-access keys, visual-recognition software adapted to identify organisms, species checklists that can be customized to particular sites, online communities in which people share species observations, and the use of crowdsourced data to refine machine-based identification algorithms. Next-generation field guides are user friendly; permit quality control and the revision of data; are scalable to accommodate burgeoning data; protect content and privacy while allowing broad public access; and are adaptable to ever-changing platforms and browsers. These tools have great potential to engage new audiences while fostering rigorous science and an appreciation for nature.Organismic and Evolutionary Biolog
Artificial intelligence-powered expert system model for identifying fall armyworm infestation in maize (Zea mays L.)
Maize (Zea mays L) is one of the most saleable cereal crops grown worldwide and a dominant staple food in many developing countries. The severe outbreak of fall armyworm in maize causes massive yield loss. Modern technologies, including smartphones, can assist in detecting recognising the fall armyworm infestation in maize. The objective of this study was to develop an automated Artificial Intelligence Powered Expert System (AIPES) for identifying fall armyworm infestation in maize. In addition, it put forward a deep learning-based model that is trained on photographs of healthy and fall armyworm infested leaves, cobs and tassels from a dataset and furnished an application that will be detecting maize fall armyworm infestation using Convolutional Neural Network (CNN) architecture and Mobile Net V 2 framework model. The study developed an Artificial Intelligence (AI) based maize fall armyworm infestation detection system using a DCNN (Deep Convolutional Neural Network) to support maize cultivating farmers. The model executed the objective by accurately identifying the fall armyworm infested maize plant and also classified them vis-c-vis the healthier crop. The deep learning models were trained to detect and recognise fall armyworm infection using more than 11000 images of fall armyworm infested leaves, cobs, and tassels. The created application (AIPES for identifying fall armyworm infestation in maize) using CNN detected and recognised the fall armyworm infestation in maize with a 100 per cent training accuracy rate and 87 per cent validation accuracy. So, the detection of maize fall armyworm and the treatment of fall armyworm-infested maize could lead to a higher maize crop yield.     Â
Development of a portable system for detection of leaf area in plants
Recently, the determination of leaf area and growth rates in plants through portable devices has become popular due to the advantages offered by artificial vision systems to identify and classify objects using images. The purpose of the research was to develop a portable system for measuring leaf area through image recognition. The methodology includes 3D designs, acrylic device building, determining the area in pixels by the technique of radial basis neural networks (RBFN) directly in the RGB color space and pixel conversion to cm2. The results obtained by analysis of variance for each species showed that the p-value was greater than 0.86 for each class of plants. Moreover, the system obtained coefficients of determination higher to 0.90 for Orange leaves, coefficients of determination higher to 0.95 for Patevaca leaves and coefficients of determination higher to 0.99 for Chirimoya leaves from a set of 30 leaves belonging to 3 species of plants with different sizes and areas compared to manual analysis. This system is a useful tool for the objective determination of leaf area in plants and becomes a nationwide alternative compared to existing expensive systems. Furthermore, the system is reprogrammable, flexible, not destructive and low cost
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