299 research outputs found

    Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review

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    Disease diagnosis is one of the major tasks for increasing food production in agriculture. Although precision agriculture (PA) takes less time and provides a more precise application of agricultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and orthorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and architectures of machine learning models are used to classify and detect plant diseases. These models help in image segmentation and feature extractions to interpret results. Researchers also use the values of vegetative indices, such as Normalized Difference Vegetative Index (NDVI), Crop Water Stress Index (CWSI), etc., acquired from different multispectral and hyperspectral sensors to fit into the statistical models to deliver results. There are still various drifts in the automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth, resolution, background noise of the image, etc. The future of crop health monitoring using UAVs should include a gimble consisting of multiple sensors, large datasets for training and validation, the development of site-specific irradiance systems, and so on. This review briefly highlights the advantages of automatic detection of plant diseases to the growers

    Use of Aerial Imagery and Novel Experimental Design to Determine the Distribution of Foliar Diseases on Soybean and Improve Efficiency of Product Testing

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    Soybeans are grown on approximately 1.3 million ha in Arkansas generating an estimated $1.7 billion annually. Foliar diseases on soybean can result in economic losses. Growers spend significant time and money on disease scouting via crop consultants and often (subsequent) fungicide applications. Fungicide trials are often arranged in small plots designs. In these scenarios, spatial variability of foliar disease is minimized. While it is advantageous to minimize variance outside of treatment differences, another limitation with many small plot trials is ample disease pressure or having only lower severity. Within a commercial production field, soil types and disease severities vary. Logically, by designing trials that take advantage of sub-field variability, efficacy of foliar fungicides could be determined in multiple zones of disease severity and factors that contribute to disease incidence, severity, or product efficacy could be determined. This work sought to understand foliar diseases distributions and how fungicide product evaluation might be improved. Because of the size of these trials, it was hypothesized that aerial imagery might be useful to determine sub-field variability of plant disease or other factors that influence disease. In 2017-18, strip trials were established in nine soybean fields throughout Southeast Arkansas. Fungicides were applied between full bloom and beginning pod. Fungicide strips were georeferenced with points spread approximately equidistant throughout the length of the field. Foliar diseases were identified, and disease levels determined across the test areas. Disease distributions were mostly significantly clustered and product efficacy changed as disease severity changed. Aerial imagery was captured on wheat, barley, and canola trials using a sUAS with visual (RGB) and near infrared sensors. Images of all test crops were captured at three different altitudes, and bloom percentage on canola and ground coverage for barley and wheat trials were assessed. Plot images were human rated and assessed using disease quantification software and plots were rated by field observations. Human rated and software quantifications of images were similar confirming plot assessment by sUAS is possible for some applications and could be useful in larger research trials such as the commercial field strip trials used in this work

    Proceedings of the 45th Annual Meeting of the Southern Soybean Disease Workers (March 7-8, 2018, Pensacola Beach, Florida)

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    Contents List of SSDW Officers Agenda for March 7-8, 2018 Abstracts for contributed/presented papers Retrospective analyses regarding the impact of soybean diseases in the USA .TW Allen, PD Esker, and CA Bradley Diaporthe (Phomopsis) species on soybean: current status in the United States. Febina Mathew, Kristina Petrovic, Lisa Castlebury, Tom Allen, Gary Bergstrom, John Bonkowski, Carl Bradley, James Buck, Emmanuel Byamukama, Martin Chilvers, Anne Dorrance, Nicholas Dufault, Loren Giesler, Nancy Gregory, Heather Kelly, Nathan Kleczewski, Trey Price, Dean Malvick, Sam Markell, Daren Mueller, Damon Smith, Terry Spurlock, Kiersten Wise, and Marina Johnson New insights into the genetic underpinnings of pathogenesis in Cercospora cf. flagellaris. Burt Bluhm, Ahmad Fakhoury, Alex Zaccaron, and Kona Swift Evaluation of spore traps and molecular tools for a fungicide decision model for frogeye leaf spot. HM Kelly and B Lin Site-specific management strategies against multiple nematode species in soybean. C Overstreet, EC McGawley, DM Xavier-Mis, and JS Rezende Field efficacy of two new seed-applied biological agents for suppression of root-knot nematodes in soybean. Travis R Faske, Michael Emerson, and Katherine Hurd Evaluation of 418 soybean plant introductions with reported resistance to soybean cyst nematode for reniform nematode resistance. Robert T Robbins and Devany Crippen Studies on Soybean vein necrosis virus in Alabama. EJ Sikora, D Delaney, A Jacobson, K Conner, A Chitturi, and J Kemble Using unmanned aerial vehicle (UAV) photograph analysis to generate corroborating data in small plot soybean fungicide efficacy trials. P Price, MA Purvis, and P Washam Building a foundation for cultivar and fungicide selection decisions in soybean. HM Kelly Results from the 2017 Mississippi State University target spot fungicide efficacy program. TW Allen and TH Wilkerson Relating temperature and relative humidity in commercial warehouses to decline in vigor of soybean seed stored for late plantings in Arkansas. JC Rupe, JA Lee, A Palmer, RT Holland, J Robinson, RD Cartwright, and G Atungulu Seed treatment for sudden death syndrome management in soybean. Yuba R Kandel, Carl A Bradley, Martin I Chilvers, Febina M Mathew, Albert U Tenuta, Damon L Smith, Kiersten A Wise, and Daren S Mueller Evaluation of screening methods for soil-borne diseases of soybean (Glycine max) in west Tennessee. R Guyer, E Zuchelli, and H Kelly Abstracts for student papers An update on taproot decline in Arkansas. J Bailey, AC Tolbert, B Boney, and TN Spurlock Taproot decline of soybean is caused by an undescribed species in the Genus Xylaria. T Garcia-Aroca, P Price, M Tomaso-Peterson, T Spurlock, TR Faske, B Bluhm, K Conner, EJ Sikora, R Guyer, H Kelly, TW Allen, and VP Doyle Assessing pathogenicity and virulence of Xylaria sp. isolates from Mississippi soybean. H Renfroe, T Wilkerson, T Allen, and M Tomaso-Peterson Effects of mycovirus infection on virulence of Rhizoctonia solani in soybean. TJ Stetina, CS Rothrock, and TN Spurlock Is the emergence of Soybean vein necrosis virus linked to the re-emergence of Tobacco streak virus? C Zambrana-EchevarrĂ­a, CL Groves, TL German, and DL Smith Is soybean vein necrosis a threat to soybean production? NR Anderson, MD Irizarry, CA Bloomingdale, DL Smith, CA Bradley, DP Delaney, NM Kleczewski, EJ Sikora, DS Mueller, and KA Wise Yield prediction in soybean fields using satellite imagery. B Boney and TN Spurlock Elucidating the race population structure of Cercospora sojina through genotypic patterns. Wagner Fagundes, Marcio Zaccaron, Alex Zaccaron, and Burton H Bluhm Impact of foliar fungicide applications in soybean fields across aggregated distributions of disease. M Patterson, AC Tolbert, and TN Spurlock Abstract for presented poster Assessing the genetic diversity of Cercospora spp. associated with Cercospora leaf blight of soybean in North America. Kona Swift and Burt Bluhm Southern United States soybean disease loss estimates for 2017. TW Allen, K Bissonnette, CA Bradley, JP Damicone, NS Dufault, TR Faske, CA Hollier, T Isakeit, RC Kemerait, NM Kleczewski, HL Mehl, JD Mueller, C Overstreet, PP Price, EJ Sikora, TN Spurlock, L Thiessen, and H Young Proceedings of the Southern Soybean Disease Workers area published annually by the Southern Soybean Disease Workers. Text, references, figures, and tables are reproduced as they were submitted by authors. The opinions expressed by the participants at this conference are their own and do not necessarily represent those of the Southern Soybean Disease Workers. Mention of a trademark or proprietary products in this publication does not constitute a guarantee, warranty, or endorsement of that product by the Southern Soybean Disease Workers. Appreciation is given to the staff at the University of Kentucky Research & Education Center, Princeton, KY, for their assistance in assembling these Proceedings

    An Evaluation of Unmanned Aircraft Systems\u27 Ability to Assess Stripe Rust in Large Wheat Breeding Nursies

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    Stripe Rust (Puccinia striiformis f. sp. tritici) is a foliar disease that significantly impacts global wheat production, and resistant cultivars provide the most efficient method of control. High-throughput phenotyping using unmanned aircraft systems (UAS) offers a potentially more efficient method for field-based phenotyping compared to visual assessment. Here we tested the ability of remote sensing to predict stripe rust severity in a diverse population of 594 soft red winter wheat lines, planted in single-rows, and evaluated them by visually rating stripe rust intensity and remotely using the dark green color index (DGCI), normalized difference vegetation index (NDVI) and blue NDVI. Significant relationships (p In a second study, the effect of plot size (single-row, two-row and four-row) on relationship between visual and remote sensing data (DGCI and NDVI) was explored. We evaluated a panel of 13 genotypes preselected to range from 0 to 100% severity, planted in three plot sizes across two measurement days. Significant (

    High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms

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    Crop yields need to be improved in a sustainable manner to meet the expected worldwide increase in population over the coming decades as well as the effects of anticipated climate change. Recently, genomics-assisted breeding has become a popular approach to food security; in this regard, the crop breeding community must better link the relationships between the phenotype and the genotype. While high-throughput genotyping is feasible at a low cost, highthroughput crop phenotyping methods and data analytical capacities need to be improved. High-throughput phenotyping offers a powerful way to assess particular phenotypes in large-scale experiments, using high-tech sensors, advanced robotics, and imageprocessing systems to monitor and quantify plants in breeding nurseries and field experiments at multiple scales. In addition, new bioinformatics platforms are able to embrace large-scale, multidimensional phenotypic datasets. Through the combined analysis of phenotyping and genotyping data, environmental responses and gene functions can now be dissected at unprecedented resolution. This will aid in finding solutions to currently limited and incremental improvements in crop yields

    Thermography to explore plant-environment interactions

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    Review PaperStomatal regulation is a key determinant of plant photosynthesis and water relations, influencing plant survival, adaptation, and growth. Stomata sense the surrounding environment and respond rapidly to abiotic and biotic stresses. Stomatal conductance to water vapour (gs) and/or transpiration (E) are therefore valuable physiological parameters to be monitored in plant and agricultural sciences. However, leaf gas exchange measurements involve contact with leaves and often interfere with leaf functioning. Besides, they are time consuming and are limited by the sampling characteristics (e.g. sample size and/or the high number of samples required). Remote and rapid means to assess gs or E are thus particularly valuable for physiologists, agronomists, and ecologists. Transpiration influences the leaf energy balance and, consequently, leaf temperature (Tleaf). As a result, thermal imaging makes it possible to estimate or quantify gs and E. Thermal imaging has been successfully used in a wide range of conditions and with diverse plant species. The technique can be applied at different scales (e.g. from single seedlings/leaves through whole trees or field crops to regions), providing great potential to study plant–environment interactions and specific phenomena such as abnormal stomatal closure, genotypic variation in stress tolerance, and the impact of different management strategies on crop water status. Nevertheless, environmental variability (e.g. in light intensity, temperature, relative humidity, wind speed) affects the accuracy of thermal imaging measurements. This review presents and discusses the advantages of thermal imaging applications to plant science, agriculture, and ecology, as well as its limitations and possible approaches to minimize them, by highlighting examples from previous and ongoing researchinfo:eu-repo/semantics/publishedVersio

    Remote Detection of water and nutritional status of soybeans using UAV-based images.

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    Digital aerial images obtained by cameras embedded in remotely piloted aircraft (RPA) have been used to detect and monitor abiotic stresses in soybeans, such as water and nutritional deficiencies. This study aimed to evaluate the ability of vegetation indexes (VIs) from RPA images to remotely detect water and nutritional status in two soybean cultivars for nitrogen. The soybean cultivars BONUS and BRS-8980 were evaluated at the phenological stages R5 and R3 (beginning of seed enlargement), respectively. To do so, plants were subjected to two water regimes (100% ETc and 50% ETc) and two nitrogen (N) supplementation levels (with and without). Thirty-five VIs from multispectral aerial images were evaluated and correlated with stomatal conductance (gs) and leaf N content (NF) measurements. Near-infrared (NIR) spectral band, enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), and renormalized difference vegetation index (RDVI) showed linear correlation (p<0.001) with gs, standing out as promising indexes for detection of soybean water status. In turn, simplified canopy chlorophyll content index (SCCCI), red-edge chlorophyll index (RECI), green ratio vegetation index (GRVI), and chlorophyll vegetation index (CVI) were correlated with NF (p<0.001), thus being considered promising for the detection of leaf N content in soybeans

    DRONE TECHNOLOGY: IS IT WORTH THE INVESTMENT IN AGRICULTURE

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    From the earliest of times, the human race has sought to better understand this world and its surroundings. In the last century, aeronautical engineering and aerial imagery have evolved to allow a deeper understanding into how this world lives and breathes. Now more than ever, these two technological advancements are changing the way we view this world and how we are to sustain it for a brighter, healthier future. Over time, the advances of these two technologies were combined and the birth of spectral sensing and drone technology arrived. In their earliest years, drones and spectral imaging were only available to government agencies. In the mid-1990s, President Clinton declassified this technology and allowed the public to utilize and invest in their development. Today, the world has incorporated these technologies into a number of applications; one of these being in agriculture. In the last decade, significant interest into drone technology and its possible applications have been researched. Many benefits have been discovered in the agricultural sector by incorporating drone and spectral technology. A big part of incorporating a new piece of equipment or technology into any operation is the economic feasibility. Understanding drone and spectral technology can do and what it can provide, is crucial in making a sound decision when considering investing in drone technology. This document discusses the earliest developments of drone technology, its current status, and the predicted future. It also provides basic information about drone designs, drone regulations, types of spectral sensors, their capabilities, and some of the research being done in agriculture to advance these technologies. Additionally, a case study looking at a wild oat infestation in spring wheat will be addressed. This case study involves two crop consultants and their decision to invest in drone technology. Advisor: Gary L. Hei
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