Machine Vision Extracted Plant Movement for Early Detection of Plant Water Stress

Kaçıra, Murat; Ling, Peter P.; Short, T. H.

doi:10.13031/2013.9923

Cited by 46 publications

(22 citation statements)

References 6 publications

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“…Therefore, early detection of plant stress is critical for minimizing the loss of productivity. Machine vision algorithms and infrared imagery have already been applied for estimating plant water stress in tomato canopies, olive orchards, and deciduous trees by Kurata and Yan (1996), Kacira et al (2002), Sepulcre-Canto et al (2006), and Naor (2008), respectively. However, plant stress can be a compound result of the effects of water and nutrient levels, disease, and insects, which makes accurate stress detection very challenging.…”

Section: Plant Stress Detectionmentioning

confidence: 99%

Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

Ahmed¹,

Yasmin²,

Mo³

et al. 2016

Journal of Biosystems Engineering

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Background: Although hyperspectral imaging was originally introduced for military, remote sensing, and astrophysics applications, the use of analytical hyperspectral imaging techniques has been expanded to include monitoring of agricultural crops and commodities due to the broad range and highly specific and sensitive spectral information that can be acquired. Combining hyperspectral imaging with remote sensing expands the range of targets that can be analyzed. Results: Hyperspectral imaging technology can rapidly provide data suitable for monitoring a wide range of plant conditions such as plant stress, nitrogen status, infections, maturity index, and weed discrimination very rapidly, and its use in remote sensing allows for fast spatial coverage. Conclusions: This paper reviews current research on and potential applications of hyperspectral imaging and remote sensing for outdoor field monitoring of agricultural crops. The instrumentation and the fundamental concepts and approaches of hyperspectral imaging and remote sensing for agriculture are presented, along with more recent developments in agricultural monitoring applications. Also discussed are the challenges and limitations of outdoor applications of hyperspectral imaging technology such as illumination conditions and variations due to leaf and plant orientation.

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Section: Plant Stress Detectionmentioning

confidence: 99%

Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

Ahmed¹,

Yasmin²,

Mo³

et al. 2016

Journal of Biosystems Engineering

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“…Irrigation scheduling systems have been developed using leaf tip tracking for wilt detection (Seginer et al, 1992) (manual system), change in side projected area (Murase et al, 1997) and change in top projected area (Kacira & Ling, 2001;Kacira et al, 2002). In these applications, the plant parameter of interest is isolated from a binary image in which the plant is segmented from the background.…”

Section: Indoorsmentioning

confidence: 99%

Applied machine vision of plants: a review with implications for field deployment in automated farming operations

McCarthy

Hancock

Raine

2010

Intel Serv Robotics

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Automated visual assessment of plant condition, specifically foliage wilting, reflectance and growth parameters, using machine vision has potential use as input for real-time variable-rate irrigation and fertigation systems in precision agriculture. This paper reviews the research literature for both outdoor and indoor applications of machine vision of plants, which reveals that different environments necessitate varying levels of complexity in both apparatus and nature of plant measurement which can be achieved. Deployment of systems to the field environment in precision agriculture applications presents the challenge of overcoming image variation caused by the diurnal and seasonal variation of sunlight.From the literature reviewed, it is argued that augmenting a monocular RGB vision system with additional sensing techniques potentially reduces image analysis complexity while enhancing system robustness to environmental variables. Therefore, machine vision systems with a foundation in optical and lighting design may potentially expedite the transition from laboratory and research prototype to robust field tool.

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“…While these small features may at first seem insignificant, they are important for a proper characterization of growth and development. For example, accurately capturing the entire leaf, including tips and twists, provides a more accurate Leaf Area Index (LAI) used to measure relative growth rates of plants [6, 7]. In the application of 3D reconstruction [8–11], from which information on the form and size of a plant can be extracted, failing to capture leaf twists during the segmentation stage will result in a disjointed 3D approximation to the plant.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Improving Image Segmentation: Application to Phenotyping of Wheat Leaves

2016

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In this article we propose a novel tool that takes an initial segmented image and returns a more accurate segmentation that accurately captures sharp features such as leaf tips, twists and axils. Our algorithm utilizes basic a-priori information about the shape of plant leaves and local image orientations to fit active contour models to important plant features that have been missed during the initial segmentation. We compare the performance of our approach with three state-of-the-art segmentation techniques, using three error metrics. The results show that leaf tips are detected with roughly one half of the original error, segmentation accuracy is almost always improved and more than half of the leaf breakages are corrected.

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Machine Vision Extracted Plant Movement for Early Detection of Plant Water Stress

Cited by 46 publications

References 6 publications

Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

Applied machine vision of plants: a review with implications for field deployment in automated farming operations

A Hybrid Approach for Improving Image Segmentation: Application to Phenotyping of Wheat Leaves

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