scholarly journals Monitoring Forage Mass with Low-Cost UAV Data: Case Study at the Rengen Grassland Experiment

2020 ◽  
Vol 88 (5) ◽  
pp. 407-422 ◽  
Author(s):  
Ulrike Lussem ◽  
Jürgen Schellberg ◽  
Georg Bareth
Keyword(s):  
Plant Community ◽  
Precision Agriculture ◽  
Imaging System ◽  
Low Cost ◽  
Block Design ◽  
Image Data ◽  
Grassland Management ◽  
Forage Yield ◽  
Management Regime ◽  
Aerial Imaging

Abstract Monitoring and predicting above ground biomass yield of grasslands are of key importance for grassland management. Established manual methods such as clipping or rising plate meter measurements provide accurate estimates of forage yield, but are time consuming and labor intensive, and do not provide spatially continuous data as required for precision agriculture applications. Therefore, the main objective of this study is to investigate the potential of sward height metrics derived from low-cost unmanned aerial vehicle-based image data to predict forage yield. The study was conducted over a period of 3 consecutive years (2014–2016) at the Rengen Grassland Experiment (RGE) in Germany. The RGE was established in 1941 and is since then under the same management regime of five treatments in a random block design and two harvest cuts per year. For UAV-based image acquisition, a DJI Phantom 2 with a mounted Canon Powershot S110 was used as a low-cost aerial imaging system. The data were investigated at different levels (e.g., harvest date-specific, year-specific, and plant community-specific). A pooled data model resulted in an R2 of 0.65 with a RMSE of 956.57 kg ha−1, although cut-specific or date-specific models yielded better results. In general, the UAV-based metrics outperformed the traditional rising plate meter measurements, but was affected by the timing of the harvest cut and plant community.

scholarly journals Voxx: a PC-based, near real-time volume rendering system for biological microscopy

2002 ◽  
Vol 282 (1) ◽  
pp. C213-C218 ◽  
Author(s):  
Jeffrey L. Clendenon ◽  
Carrie L. Phillips ◽  
Ruben M. Sandoval ◽  
Shiaofen Fang ◽  
Kenneth W. Dunn
Keyword(s):  
Real Time ◽  
Imaging System ◽  
Low Cost ◽  
Three Dimensional ◽  
Image Data ◽  
Complex Structures ◽  
Two Photon ◽  
Two Photon Fluorescence ◽  
Photon Fluorescence ◽  
Biological Image

Confocal and two-photon fluorescence microscopy have advanced the exploration of complex, three-dimensional biological structures at submicron resolution. We have developed a voxel-based three-dimensional (3-D) imaging program (Voxx) capable of near real-time rendering that runs on inexpensive personal computers. This low-cost interactive 3-D imaging system provides a powerful tool for analyzing complex structures in cells and tissues and encourages a more thorough exploration of complex biological image data.

scholarly journals Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

2019 ◽  
Vol 11 (10) ◽  
pp. 1157 ◽  
Author(s):  
Jorge Fuentes-Pacheco ◽  
Juan Torres-Olivares ◽  
Edgar Roman-Rangel ◽  
Salvador Cervantes ◽  
Porfirio Juarez-Lopez ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Image Data ◽  
Ground Truth ◽  
Aerial Images ◽  
Aerial Image ◽  
Data Set ◽  
Visual Appearance ◽  
Aerial Robots ◽  
Lighting Conditions ◽  
Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

scholarly journals Irrigation Timing as a Practice of Effective Weed Management in Established Alfalfa (Medicago sativa L.) Crop

Agronomy ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 550
Author(s):  
Panagiotis Kanatas ◽  
Ioannis Gazoulis ◽  
Ilias Travlos
Keyword(s):  
Medicago Sativa ◽  
Weed Management ◽  
Cultural Practices ◽  
Block Design ◽  
Chenopodium Album ◽  
Stand Density ◽  
Climatic Conditions ◽  
Amaranthus Retroflexus ◽  
Forage Yield ◽  
Medicago Sativa L

Irrigation is an agronomic practice of major importance in alfalfa (Medicago sativa L), especially in the semiarid environments of Southern Europe. Field experimentation was conducted in Western Greece (2016–2018) to evaluate the effects of irrigation timing on weed presence, alfalfa yield performance, and forage quality. In a randomized complete block design (four replications), two cultivars (“Ypati 84” and “Hyliki”) were the main plots, while three irrigation timings were the subplots (split-plot). The irrigation timings were IT-1, IT-2, and IT-3, denoting irrigation 1 week before harvest, 1 week after harvest, and 2 weeks after harvest, respectively. IT-1 reduced Solanum nigrum L. density by 54% and 79% as compared to IT-3 and IT-2, respectively. Chenopodium album L. density was the highest under IT-2. IT-3 resulted in 41% lower Amaranthus retroflexus L. density in comparison to IT-2, while the lowest values were observed under IT-1. Stand density and stems·plant−1 varied between years (p ≤ 0.05). Mass·stem−1 and alfalfa forage yield were affected by the irrigation timings (p ≤ 0.001). Total weed density and forage yield were negatively correlated in both the second (R2 = 87.013%) and the fourth (R2 = 82.691%) harvests. IT-1 and IT-3 increased forage yield, leaf per stem ratio, and crude protein as compared to IT-2. Further research is required to utilize the use of cultural practices for weed management in perennial forages under different soil and climatic conditions.

Low-cost high-performance CMOS terahertz imaging system

2015 ◽  
Author(s):  
Chung Hsing Li ◽  
Tzu-Chao Yan ◽  
Yuhsin Chang ◽  
Chyong Chen ◽  
Chien-Nan Kuo
Keyword(s):  
High Performance ◽  
Imaging System ◽  
Low Cost ◽  
Terahertz Imaging

A low-cost laboratory thermal wave imaging system

1984 ◽  
Vol 17 (6) ◽  
pp. 526-532 ◽  
Author(s):  
G F Kirkbright ◽  
R M Miller ◽  
A Rzadkiewicz
Keyword(s):  
Thermal Wave ◽  
Imaging System ◽  
Low Cost ◽  
Wave Imaging ◽  
Thermal Wave Imaging

scholarly journals Remote Sensing and Machine Learning in Crop Phenotyping and Management, with an Emphasis on Applications in Strawberry Farming

2021 ◽  
Vol 13 (3) ◽  
pp. 531
Author(s):  
Caiwang Zheng ◽  
Amr Abd-Elrahman ◽  
Vance Whitaker
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Precision Agriculture ◽  
Image Data ◽  
Fruit Shape ◽  
Biological Information ◽  
Genetic Traits ◽  
Multiple Sensors ◽  
Fruit Maturity ◽  
High Throughput Phenotyping

Measurement of plant characteristics is still the primary bottleneck in both plant breeding and crop management. Rapid and accurate acquisition of information about large plant populations is critical for monitoring plant health and dissecting the underlying genetic traits. In recent years, high-throughput phenotyping technology has benefitted immensely from both remote sensing and machine learning. Simultaneous use of multiple sensors (e.g., high-resolution RGB, multispectral, hyperspectral, chlorophyll fluorescence, and light detection and ranging (LiDAR)) allows a range of spatial and spectral resolutions depending on the trait in question. Meanwhile, computer vision and machine learning methodology have emerged as powerful tools for extracting useful biological information from image data. Together, these tools allow the evaluation of various morphological, structural, biophysical, and biochemical traits. In this review, we focus on the recent development of phenomics approaches in strawberry farming, particularly those utilizing remote sensing and machine learning, with an eye toward future prospects for strawberries in precision agriculture. The research discussed is broadly categorized according to strawberry traits related to (1) fruit/flower detection, fruit maturity, fruit quality, internal fruit attributes, fruit shape, and yield prediction; (2) leaf and canopy attributes; (3) water stress; and (4) pest and disease detection. Finally, we present a synthesis of the potential research opportunities and directions that could further promote the use of remote sensing and machine learning in strawberry farming.

scholarly journals Fast, Robust, and Low-Cost Microwave Imaging of Multiple Non-Metallic Pipes

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1762
Author(s):  
Yuki Gao ◽  
Maryam Ravan ◽  
Reza K. Amineh
Keyword(s):  
Antenna Arrays ◽  
Oil And Gas ◽  
Imaging System ◽  
Imaging Techniques ◽  
Low Cost ◽  
Microwave Imaging ◽  
Airport Security ◽  
Destructive Testing ◽  
Security Screening ◽  
Industrial Sectors

The use of non-metallic pipes and composite components that are low-cost, durable, light-weight, and resilient to corrosion is growing rapidly in various industrial sectors such as oil and gas industries in the form of non-metallic composite pipes. While these components are still prone to damages, traditional non-destructive testing (NDT) techniques such as eddy current technique and magnetic flux leakage technique cannot be utilized for inspection of these components. Microwave imaging can fill this gap as a favorable technique to perform inspection of non-metallic pipes. Holographic microwave imaging techniques are fast and robust and have been successfully employed in applications such as airport security screening and underground imaging. Here, we extend the use of holographic microwave imaging to inspection of multiple concentric pipes. To increase the speed of data acquisition, we utilize antenna arrays along the azimuthal direction in a cylindrical setup. A parametric study and demonstration of the performance of the proposed imaging system will be provided.

Near-infrared fundus imaging system with light illumination from an electronic contact lens

2022 ◽  
Vol 15 (2) ◽  
pp. 027001
Author(s):  
Yang Cui ◽  
Taiki Takamatsu ◽  
Koichi Shimizu ◽  
Takeo Miyake
Keyword(s):  
Contact Lens ◽  
Near Infrared ◽  
Imaging System ◽  
Low Cost ◽  
Light Illumination ◽  
Fundus Images ◽  
Fundus Imaging ◽  
Point Spread ◽  
Spread Function ◽  
Dependent Point

Abstract As for the diagnosis and treatment of eye diseases, an ideal fundus imaging system is expected to be portability, low cost, and high resolution. Here, we demonstrate a non-mydriatic near-infrared fundus imaging system with light illumination from an electronic contact lens (E-lens). The E-lens can illuminate the retinal and choroidal structures for capturing the fundus images when voltage is applied wirelessly to the lens. And we also reconstruct the images with a depth-dependent point-spread function to suppress the scattering effect that eventually visualizes the clear fundus images.

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