Anomaly detection in plant growth in a controlled environment using 3D scanning techniques and deep learning

Xhimitiku, Iva; Bianchi, Federico; Proietti, Massimiliano; Tocci, Tommaso; Marini, Andrea; Menculini, Lorenzo; Termite, Loris Francesco; Pucci, Edvige; Garinei, Alberto; Marconi, Marcello; Rossi, Gianluca

doi:10.1109/metroagrifor52389.2021.9628481

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…Plant growth monitoring is one of the applications where DL techniques have been applied to greenhouse production. Plant growth monitoring encompasses various areas such as length estimation at all crop growth stages as demonstrated in [ 76 , 77 ], and anomalies in plant growth in [ 78 , 82 ]. Other areas where plant growth monitoring is applied are in the prediction of Phyto-morphological descriptors as demonstrated in [ 79 ], seedling vigor rating in [ 80 ], leaf-shape estimation [ 83 ], and spike detection and segmentation in [ 81 ].…”

Section: Deep Learning In Ceamentioning

confidence: 99%

Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

Ojo

Zahid

2022

Sensors

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Controlled environment agriculture (CEA) is an unconventional production system that is resource efficient, uses less space, and produces higher yields. Deep learning (DL) has recently been introduced in CEA for different applications including crop monitoring, detecting biotic and abiotic stresses, irrigation, microclimate prediction, energy efficient controls, and crop growth prediction. However, no review study assess DL’s state of the art to solve diverse problems in CEA. To fill this gap, we systematically reviewed DL methods applied to CEA. The review framework was established by following a series of inclusion and exclusion criteria. After extensive screening, we reviewed a total of 72 studies to extract the useful information. The key contributions of this article are the following: an overview of DL applications in different CEA facilities, including greenhouse, plant factory, and vertical farm, is presented. We found that majority of the studies are focused on DL applications in greenhouses (82%), with the primary application as yield estimation (31%) and growth monitoring (21%). We also analyzed commonly used DL models, evaluation parameters, and optimizers in CEA production. From the analysis, we found that convolutional neural network (CNN) is the most widely used DL model (79%), Adaptive Moment Estimation (Adam) is the widely used optimizer (53%), and accuracy is the widely used evaluation parameter (21%). Interestingly, all studies focused on DL for the microclimate of CEA used RMSE as a model evaluation parameter. In the end, we also discussed the current challenges and future research directions in this domain.

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Section: Deep Learning In Ceamentioning

confidence: 99%

Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

Ojo

Zahid

2022

Sensors

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“…However, the assessment of such responsive behaviour requires a measurement method able to track and compare the displacements of multiple composite samples. This kind of evaluation can be done through conventional image analysis techniques (Xhimitiku et al, 2021:), such as Digital Image Correlation (Montanini et al, 2020:;Pan et al, 2009:). The use of a stereovision 3D DIC technique with commercial software can involve considerable costs due to the specific equipment and software licensing.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Depth-Camera: Open-Source 3d Displacement Measurements for 4d Printed Hygroscopic Composites

Bianconi

Filippucci

Pelliccia

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. 4D printing (4DP) is a growing branch of 3D printing technology that involves the design of composite material architectures capable of shape-change transformations, which occur post printing, in response to external stimulus. Among these, Wood Polymer Composites (WPCs) change their shape in reaction to changes of moisture content, shrinking or swelling like natural wood until the equilibrium with the environment is reached. Such intrinsic material behavior can be particularly useful in the development of passive moisture airflow controllers that can modulate humidity and airflow in indoor environments to improve air quality. Precise measurement of the time-based stimulus induced shape-change response of these composites is critical to assess the responsiveness, velocity of reaction and overall deformation of the designed 4DP composite mechanisms. Up until now, Digital Image Correlation (DIC) techniques have been widely used for such purpose. However, DIC methods require expensive equipment and costly commercial software. This paper presents a Low-Cost Depth-Camera (LCDC) method that uses a free custom algorithm that returns a 3D coloured displacement map with the corresponding meshes of the acquired object. The LCDC method does not require specialized equipment and allows for an overall understanding of the time-dependent deformation of 4DP actuators, this method also facilitates the comparison between composites with different properties under the same external conditions. This new LCDC method has the potential to further 4DP research by providing an open-source, accessible and reliable tool to assess 3D displacement measurements.

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Studies on the Metrological Need and Capabilities of 3D Scanning Technologies

Javaid

Haleem

Singh

et al. 2022

J. Ind. Intg. Mgmt.

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3D scanning technologies are deployed toward developing a digital 3D model for Additive Manufacturing (AM) applications. It collects data, turning it into a 3D model that uses designated 3D printing processes. Many scanners, ranging from low-cost alternatives to professional series that are far more accurate and reliable, are now available to assist in bringing designs to reality. 3D scanning solutions enable the appropriate measurement of 3D physical parts into the virtual world, allowing factory production teams and corporate offices to share critical design information. These techniques are utilized everywhere in the design process, including product design and development, reverse engineering, quality control and quality assurance. The manufacturing sector can decrease costs while accelerating time to market and resolving quality issues. This study investigates the metrological need as per the advancements of 3D scanners. The procedural steps of the 3D scanners, along with specific metrological components and soft tools for 3D scanning, are discussed briefly. Finally, various 3D scanning applications are identified and discussed in detail. Because of the overall relative advantages of these non-contact measurement techniques, 3D metrological tools are crucial for modern production. Almost every sector aims for smaller, more complex components, which are more vulnerable to contamination or injury from even the slightest touch with a probe. The market is driven by global Research and Development (R&D) investment to develop game-changing technologies and solutions. Precision inspection and quality control are significant market drivers for industry progress. Smart factories will have lifetime access to 3D metrological data, allowing them to enhance quality and gain a competitive advantage in the marketplace.

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Anomaly detection in plant growth in a controlled environment using 3D scanning techniques and deep learning

Cited by 3 publications

References 12 publications

Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

Low-Cost Depth-Camera: Open-Source 3d Displacement Measurements for 4d Printed Hygroscopic Composites

Studies on the Metrological Need and Capabilities of 3D Scanning Technologies

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