Variability of seasonal CASI image data products and potential application for management zone delineation for precision agriculture

2005 ◽  
Vol 31 (5) ◽  
pp. 400-411 ◽  
Author(s):  
Jiangui Liu ◽  
John R Miller ◽  
Driss Haboudane ◽  
Elizabeth Pattey ◽  
Michel C Nolin
Keyword(s):  
Precision Agriculture ◽  
Potential Application ◽  
Image Data ◽  
Management Zone ◽  
Data Products

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 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 Spatial influence of physical and chemical parameters on management zone definition in apple orchards

2015 ◽  
Vol 35 (6) ◽  
pp. 1160-1171
Author(s):  
Luciano Gebler ◽  
Celia R. Grego ◽  
Abel L. Vieira ◽  
Leonardo da R. Kuse
Keyword(s):  
Fruit Quality ◽  
Precision Agriculture ◽  
Apple Orchard ◽  
Apple Orchards ◽  
Management Zones ◽  
Digital Mapping ◽  
Management Zone ◽  
Soil Penetration Resistance ◽  
Representative Points ◽  
Physical And Chemical

ABSTRACT Precision agriculture adoption in Brazilian apple orchards is still incipient. This study aimed at evaluating the spatial variability of certain soil properties as soil density, soil penetration resistance, electrical conductivity, yield, and fruit quality in an apple orchard through digital mapping, as well as assessing the correlation between these factors by means of geostatistics, establishing management zones. Forty representative points were set within 2.5 hectares of apple orchard, wherein soil samples were collected and analyzed, besides measurements of fruit quality (Brix degree, size or diameter, pulp firmness and color) to generate an overall index quality. We concluded that the fruit quality indexes, when isolated, did not show strong spatial dependence, unlike the index of fruit quality (FQI), derived from a combination of these parameters, allowing orchard planning according to management zones based on quality.

Plant Classification and Position Estimation for Autonomous Field Robots

2020 ◽  
Author(s):  
Sebastian Haug
Keyword(s):  
Real World ◽  
Precision Agriculture ◽  
Image Data ◽  
Position Estimation ◽  
Single Plant ◽  
Field Robots ◽  
Weed Plants ◽  
Generic Problem ◽  
Vegetable Farming ◽  
Leaf Segmentation

This work presents new approaches to plant classifcation and plant position estimation to enable feld robot based precision agriculture. The developed methods are designed for challenging real world feld situations with small crop plants, presence of close-to-crop weed and overlap of plants. The plant classifcation system is able to distinguish two or more plant classes in feld images without the need for error-prone plant or leaf segmentation. The plant position estimation pipeline solves the generic problem of determining the position of both crop and weed plants only from image data. The combination of both methods allows feld robots to autonomously determine the type and position of plants in the feld to realize precision agriculture tasks such as single plant weed control. Experiments with a feld robot prove the applicability of the presented methods for challenging feld scenarios encountered for example in organic vegetable farming. Contents Symbols and Abbreviations  . . . . . ...

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 Airborne Spectral Polarimeter for Ocean Wave Research

2015 ◽  
Vol 32 (4) ◽  
pp. 805-815 ◽  
Author(s):  
Brett A. Hooper ◽  
Becky Van Pelt ◽  
J. Z. Williams ◽  
J. P. Dugan ◽  
M. Yi ◽  
...  
Keyword(s):  
Time Series ◽  
Near Infrared ◽  
Dynamic Range ◽  
Image Data ◽  
Environmental Data ◽  
Ocean Wave ◽  
Subsurface Water ◽  
Optical Remote Sensing ◽  
Camera System ◽  
Data Products

AbstractThe Airborne Remote Optical Spotlight System (AROSS) family of sensors consists of airborne imaging systems that provide passive, high-dynamic range, time series image data and has been used successfully to characterize currents and bathymetry of nearshore ocean, tidal flat, and riverine environments. AROSS–multispectral polarimeter (AROSS-MSP) is a 12-camera system that extends this time series capability to simultaneous color and polarization measurements for the full linear polarization of the imaged scene in red, green, and blue, and near-infrared (RGB–NIR) wavelength bands. Color and polarimetry provide unique information for retrieving dynamic environmental parameters over a larger area (square kilometers) than is possible with typical in situ measurements. This particular field of optical remote sensing is developing rapidly, and simultaneous color and polarimetric data are expected to enable the development of a number of additional important environmental data products, such as the improved ability to image the subsurface water column or maximizing wave contrast to improve oceanographic parameter retrievals of wave spectra and wave heights.One of the main obstacles to providing good-quality polarimetric image data from a multicamera system is the ability to accurately merge imagery from the cameras to a subpixel level. This study shows that the imagery from AROSS-MSP can be merged to an accuracy better than one-twentieth of a pixel, comparing two different automated algorithmic techniques. This paper describes the architecture of AROSS-MSP, the approach for providing simultaneous color and polarization imagery in space and time, an error analysis to characterize the measurements, and example color and polarization data products from ocean wave imagery.

An improved YOLO network for unopened cotton boll detection in the field

2021 ◽  
pp. 1-14
Author(s):  
Yan Zhang ◽  
Gongping Yang ◽  
Yikun Liu ◽  
Chong Wang ◽  
Yilong Yin
Keyword(s):  
Precision Agriculture ◽  
Cost Model ◽  
Computational Cost ◽  
Image Data ◽  
Learning Technologies ◽  
Detection Accuracy ◽  
Cotton Boll ◽  
Feature Maps ◽  
Network Layers ◽  
Cotton Bolls

Detection of cotton bolls in the field environments is one of crucial techniques for many precision agriculture applications, including yield estimation, disease and pest recognition and automatic harvesting. Because of the complex conditions, such as different growth periods and occlusion among leaves and bolls, detection in the field environments is a task with considerable challenges. Despite this, the development of deep learning technologies have shown great potential to effectively solve this task. In this work, we propose an Improved YOLOv5 network to detect unopened cotton bolls in the field accurately and with lower cost, which combines DenseNet, attention mechanism and Bi-FPN. Besides, we modify the architecture of the network to get larger feature maps from shallower network layers to enhance the ability of detecting bolls due to the size of cotton boll is generally small. We collect image data of cotton in Aodu Farm in Xinjiang Province, China and establish a dataset containing 616 high-resolution images. The experiment results show that the proposed method is superior to the original YOLOv5 model and other methods such as YOLOv3,SSD and FasterRCNN considering the detection accuracy, computational cost, model size and speed at the same time. The detection of cotton boll can be further applied for different purposes such as yield prediction and identification of diseases and pests in earlier stage which can effectively help farmers take effective approaches in time and reduce the crop losses and therefore increase production.

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