Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1-the summer 2015 and winter 2016 growing seasons-of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project's goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs

The Adoption of Unmanned Aerial Systems by Farmers in Texas

Though Unmanned Aerial System (UAS) technology is a promising tool that can aid farmers in production efficiency, little research has been found on the adoption/acceptance of this new technology in agriculture. The study aims to describe Texas farmers’ perspectives regarding UASs and identify the attributes and applications most salient to each perspective. The study used a Q methodological approach to identify the different viewpoints held by 25 Texas crop farmers who reside in five different agricultural regions of the state. Rogers’ Diffusion of Innovation theory and previous precision agriculture and UAS adoption studies were used as a conceptual framework to guide this mixed-method study. Identified perspectives include high-tech harvesters (innovators), purposeful propagators (early adopters), and conventional cultivators (laggards). Technical applications of detecting invasive insects and weeds and overall crop health emerged as the primary capabilities of interest for high-tech harvesters and purposeful propagators, accounting for the majority of the farmers in the study. The conventional cultivators showed little interest in technical applications but were interested in UASs reducing labor requirements. Findings suggest that future research and development continue to make the technology more user-friendly and economical while focusing on the application of using UASs to monitor fields proactively to prevent yield losses. Furthermore, steps should be taken to implement statewide trainings, geared towards high-tech harvesters and purposeful propagators, to educate them on this innovation in precision agriculture

The Adoption of Unmanned Aerial Systems by Farmers in Texas

Though Unmanned Aerial System (UAS) technology is a promising tool that can aid farmers in production efficiency, little research has been found on the adoption/acceptance of this new technology in agriculture. The study aims to describe Texas farmers’ perspectives regarding UASs and identify the attributes and applications most salient to each perspective. The study used a Q methodological approach to identify the different viewpoints held by 25 Texas crop farmers who reside in five different agricultural regions of the state. Rogers’ Diffusion of Innovation theory and previous precision agriculture and UAS adoption studies were used as a conceptual framework to guide this mixed-method study. Identified perspectives include high-tech harvesters (innovators), purposeful propagators (early adopters), and conventional cultivators (laggards). Technical applications of detecting invasive insects and weeds and overall crop health emerged as the primary capabilities of interest for high-tech harvesters and purposeful propagators, accounting for the majority of the farmers in the study. The conventional cultivators showed little interest in technical applications but were interested in UASs reducing labor requirements. Findings suggest that future research and development continue to make the technology more user-friendly and economical while focusing on the application of using UASs to monitor fields proactively to prevent yield losses. Furthermore, steps should be taken to implement statewide trainings, geared towards high-tech harvesters and purposeful propagators, to educate them on this innovation in precision agriculture

Senecio nikoensis Miq.

原著和名: サハギク科名: キク科 = Compositae採集地: 東京都 高尾山 (武蔵 高尾山)採集日: 1986/7/15採集者: 萩庭丈壽整理番号: JH007357国立科学博物館整理番号: TNS-VS-95735

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NDVI map generated from multispectral data collected with the Sentek sensor onboard the Anaconda fixed wing UAV platform.

<p>NDVI map generated from multispectral data collected with the Sentek sensor onboard the Anaconda fixed wing UAV platform.</p

Comparison of different types of UAV platforms.

<p>Comparison of different types of UAV platforms.</p

Sensors carried by the UAVs used in this study.

<p>(A) Sentek GEMS multispectral camera carried by the Anaconda fixed-wing UAV. (B) Nikon J3 digital camera (left) and modified multispectral camera (right) carried by the Lancaster fixed-wing UAV. (C) DJI P3-005 4K camera carried by the X88 octocopter.</p

Generalization of the integration of teams and responsibilities in the TAMU-UAS project.

<p>Field researchers are end users and primarily involved in experimental design and ground-truthing data. Aerospace and mechanical engineers, and ecosystem scientists are primarily involved in raw UAS data collection. Geospatial scientists serve as a clearinghouse for the UAS data and also perform mosaicking—the stitching together of many small images to build one ortho-rectified and radiometrically seamless large image. Agricultural engineers are the nexus of the project, turning the UAS data into actionable results for the end users. The administration team provides and manages funds, facilitates meetings, and coordinates communications and initiatives.</p