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12 Results

Search results for 'Orchards'

  • Alan Taylor

    Interests:

    Spring is about to pop - Benton City, WA

    By #SoilMatters

    Published 2 years, 8 months ago

    Soil temperatures have risen 4-6F in the past week. This has signaled these Chelan #cherry buds to swell. This is an early ripening variety. Which means the number of days a farmer has to grow and nurture the crop are limited... Last fall, many orchards were not fertilized as usual due to a late harvest (~3 weeks) and an early snow...

  • Alan Taylor

    Interests:

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  • Spring is about to pop - Benton City, WA

    By #SoilMatters

    Published 2 years, 8 months ago

    Soil temperatures have risen 4-6F in the past week. This has signaled these Chelan #cherry buds to swell. This is an early ripening variety. Which means the number of days a farmer has to grow and nurture the crop are limited... Last fall, many orchards were not fertilized as usual due to a late harvest (~3 weeks) and an early snow...

  • Posted By #SoilMatters
    2 years, 8 months ago

    https://agfuse.com/article/spring-is-about-to-pop---benton-city-wa

    Posted By Technology
    2 days ago

    https://www.growingproduce.com/fruits/bee-free-how-growers-can-pollinate-with-drones/
    Data analysis and inference model for automating operational monitoring activities in Precision Farming and Precision Forestry applications

    Authors: Pasqualina Sacco, Raimondo Gallo and Fabrizio Mazzetto

    Published under licence by IOP Publishing Ltd
    IOP Conference Series: Earth and Environmental Science, Volume 275, conference 1

    Each application of Precision Agriculture or Forestry should be supported by a technological platform able to perform, in an integrated way, the following data-information cycle functions: 1) data collection; 2) data processing; 3) data analysis and evaluation; 4) use of information. In accordance to this view, information are data that are usefully used in a decision making process or within a reporting protocol destined to users external to the enterprise (certification tasks). In order to manage the platform in a complete and efficient manner an adequate information system is needed. Firstly, the paper shows a classification of the possible monitoring solutions based on the different enterprise typologies, highlighting the main technological and interpretative requirements. Secondly, some case studies related to the application of operational monitoring in orchards and forestry are introduced, mainly focusing on some peculiar aspects of the algorithms developed for the implementation of the inference engines.

    Figure: Schema of the operational monitoring system in orchards. Credits: Pasqualina Sacco, Raimondo Gallo and Fabrizio Mazzetto

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    U.S. Call for Applications: 2020 Farm Bureau Ag Innovation Challenge

    The American Farm Bureau Federation, in partnership with Farm Credit, has opened online applications for the 2020 Farm Bureau Ag Innovation Challenge. In its sixth year, the Farm Bureau Ag Innovation Challenge is a national business competition for U.S. food and agriculture startups. Entrepreneurs will compete for $145,000 in startup funds.

    Launched in 2015 as the first national competition focused exclusively on rural entrepreneurs, the competition continues to provide an opportunity for U.S. startups to showcase business innovations in food and agriculture.

    Entrepreneurs and startups with businesses in the following categories are encouraged to apply:

    Farm, ranch, greenhouse, aquaponics
    Input product or crop variety
    Method or tool for growing, monitoring or harvesting crops or livestock
    Production support services
    Retail, agritourism or farm-to-table business
    Food/beverage/textile product or ingredient
    Method of production, preparation, or packaging of food/beverage/textile products or ingredients
    Value-added processing including yogurts, cheese and processed meats, wineries, breweries, cideries and distilleries

    Dateline for submission: September 30, 2019

    Illustration Photo: Kiyokawa Family Orchards – Parkdale, Oregon, USA (credits: Oregon Department of Agriculture / Flickr Creative Commons Attribution-NonCommercial-NoDerivs 2.0 Generic (CC BY-NC-ND 2.0))

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    Development of a Recognition System for Spraying Areas from Unmanned Aerial Vehicles Using a Machine Learning Approach

    Authors: Pengbo Gao, Yan Zhang, Linhuan Zhang, Ryozo Noguchi and Tofael Ahamed

    Journal Title: Sensors

    ISSN: 1424-8220 (Online)

    Publisher: MDPI AG

    Unmanned aerial vehicle (UAV)-based spraying systems have recently become important for the precision application of pesticides, using machine learning approaches. Therefore, the objective of this research was to develop a machine learning system that has the advantages of high computational speed and good accuracy for recognizing spray and non-spray areas for UAV-based sprayers. A machine learning system was developed by using the mutual subspace method (MSM) for images collected from a UAV. Two target lands: agricultural croplands and orchard areas, were considered in building two classifiers for distinguishing spray and non-spray areas. The field experiments were conducted in target areas to train and test the system by using a commercial UAV (DJI Phantom 3 Pro) with an onboard 4K camera. The images were collected from low (5 m) and high (15 m) altitudes for croplands and orchards, respectively. The recognition system was divided into offline and online systems. In the offline recognition system, 74.4% accuracy was obtained for the classifiers in recognizing spray and non-spray areas for croplands. In the case of orchards, the average classifier recognition accuracy of spray and non-spray areas was 77%. On the other hand, the online recognition system performance had an average accuracy of 65.1% for croplands, and 75.1% for orchards. The computational time for the online recognition system was minimal, with an average of 0.0031 s for classifier recognition. The developed machine learning system had an average recognition accuracy of 70%, which can be implemented in an autonomous UAV spray system for recognizing spray and non-spray areas for real-time applications.

    Illustration Photo: Spraying drone (CC0 Creative Commons from Pixabay.com)

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    A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses

    Author: Jayme Garcia Arnal Barbedo

    Journal Title: Drones

    ISSN: 2504-446X (Online)

    Publisher: MDPI AG

    Unmanned aerial vehicles (UAVs) are becoming a valuable tool to collect data in a variety of contexts. Their use in agriculture is particularly suitable, as those areas are often vast, making ground scouting difficult, and sparsely populated, which means that injury and privacy risks are not as important as in urban settings. Indeed, the use of UAVs for monitoring and assessing crops, orchards, and forests has been growing steadily during the last decade, especially for the management of stresses such as water, diseases, nutrition deficiencies, and pests. This article presents a critical overview of the main advancements on the subject, focusing on the strategies that have been used to extract the information contained in the images captured during the flights. Based on the information found in more than 100 published articles and on our own research, a discussion is provided regarding the challenges that have already been overcome and the main research gaps that still remain, together with some suggestions for future research.

    Illustration Photo: Drone flying over a crop field (credits: Greg Shine, BLM / Flickr Creative Commons Attribution 2.0 Generic (CC BY 2.0))

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    Posted By Technology
    7 months ago

    https://www.farmprogress.com/technology/project-uses-drones-predict-frost-damage-orchards

    Posted By Technology
    8 months ago

    https://agfax.com/2019/03/26/tree-crops-will-robots-help-protect-orchards-from-frost-damage/

    Posted By Irrigation
    1 years, 3 months ago

    http://www.sacvalleyorchards.com/blog/advances-in-on-farm-water-management/

    Posted By Ag Sustainability And Innovation
    2 years, 1 month ago

    An Electrically driven, Computer controlled Robotics platform for Orchard use

    Authors: Jones M H, Seabright M, Barnett J, Neshausen G, Duke M, Scarfe A

    Publisher: Zenodo

    As automation in agriculture progresses, more automation systems will be placed into farms & orchards.

    Systems that service the crops directly are likely to have positioning requirements that make traditional tractor-trailer units unsuitable.

    This paper introduces a platform built for transporting robotics modules through a kiwifruit orchard.

    Performance figures, design considerations and a general hardware overview are presented.

    The platform is controlled either by remote or computer generated drive commands - facilitating autonomous navigation.

    In-orchard testing shows the system is well suited for the target application, achieving stable speed control and repeatable positioning.

    Photo: Robotics platform for Orchard use (credits: Jones M H, Seabright M, Barnett J, Neshausen G, Duke M, Scarfe A)

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