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Predicting Crop Phenological Stages For A Greenhouse Scheduling System


Predicting Crop Phenological Stages For A Greenhouse Scheduling System
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Predicting Crop Phenological Stages For A Greenhouse Scheduling System


Predicting Crop Phenological Stages For A Greenhouse Scheduling System
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Author : Gregory A. Kiker
language : en
Publisher:
Release Date : 1992

Predicting Crop Phenological Stages For A Greenhouse Scheduling System written by Gregory A. Kiker and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Greenhouse management categories.




Predicting Crop Phenology


Predicting Crop Phenology
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Author : Tom Hodges
language : en
Publisher: CRC Press
Release Date : 1990-12-26

Predicting Crop Phenology written by Tom Hodges and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990-12-26 with Technology & Engineering categories.


Predicting Crop Phenology focuses on an analysis of the issues faced in predicting the phenology of crop plants and weeds. It discusses how these issues have been handled by active crop growth simulation model developers and emphasizes areas such as the role of modeling in agricultural research and the roles of temperature, length of day, and water stress in plant growth. This comprehensive text also discusses modeling philosophy and programming techniques in modeling crop development and growth. It presents up-to-date information on phenology models for wheat, maize, sorghum, rice, cotton, and several weed species. Predicting Crop Phenology reviews important data for agricultural engineers, plant physiologists, agricultural consultants, researchers, extension agents, model developers, agricultural science instructors and students.



Agrindex


Agrindex
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Author :
language : en
Publisher:
Release Date : 1995

Agrindex written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Agriculture categories.




Modeling And Control Of Greenhouse Crop Growth


Modeling And Control Of Greenhouse Crop Growth
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Author : Francisco Rodríguez
language : en
Publisher: Springer
Release Date : 2014-11-01

Modeling And Control Of Greenhouse Crop Growth written by Francisco Rodríguez and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-01 with Technology & Engineering categories.


A discussion of challenges related to the modeling and control of greenhouse crop growth, this book presents state-of-the-art answers to those challenges. The authors model the subsystems involved in successful greenhouse control using different techniques and show how the models obtained can be exploited for simulation or control design; they suggest ideas for the development of physical and/or black-box models for this purpose. Strategies for the control of climate- and irrigation-related variables are brought forward. The uses of PID control and feedforward compensators, both widely used in commercial tools, are summarized. The benefits of advanced control techniques—event-based, robust, and predictive control, for example—are used to improve on the performance of those basic methods. A hierarchical control architecture is developed governed by a high-level multiobjective optimization approach rather than traditional constrained optimization and artificial intelligence techniques. Reference trajectories are found for diurnal and nocturnal temperatures (climate-related setpoints) and electrical conductivity (fertirrigation-related setpoints). The objectives are to maximize profit, fruit quality, and water-use efficiency, these being encouraged by current international rules. Illustrative practical results selected from those obtained in an industrial greenhouse during the last eight years are shown and described. The text of the book is complemented by the use of illustrations, tables and real examples which are helpful in understanding the material. Modeling and Control of Greenhouse Crop Growth will be of interest to industrial engineers, academic researchers and graduates from agricultural, chemical, and process-control backgrounds.



Greenhouse Technology And Management


Greenhouse Technology And Management
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Author : Nicolás Castilla
language : en
Publisher: CABI
Release Date : 2013

Greenhouse Technology And Management written by Nicolás Castilla and has been published by CABI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Gardening categories.


Translation of the second ed.: Invernaderos de plaastico: tecnologaia y manejo.



Advanced Greenhouse Horticulture


Advanced Greenhouse Horticulture
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Author : Athanasios Koukounaras
language : en
Publisher: MDPI
Release Date : 2021-03-19

Advanced Greenhouse Horticulture written by Athanasios Koukounaras and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-19 with Science categories.


Greenhouse horticulture is one of the most intensive agricultural systems, focusing on the production of high-value products. This book presents current research findings that cover a wide range of new technologies and novel agricultural practices, which are preconditions for successful production in a very competitive global environment.



On The Use Of Imaging Spectroscopy From Unmanned Aerial Systems Uas To Model Yield And Assess Growth Stages Of A Broadacre Crop


On The Use Of Imaging Spectroscopy From Unmanned Aerial Systems Uas To Model Yield And Assess Growth Stages Of A Broadacre Crop
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Author : Amirhossein Hassanzadeh
language : en
Publisher:
Release Date : 2022

On The Use Of Imaging Spectroscopy From Unmanned Aerial Systems Uas To Model Yield And Assess Growth Stages Of A Broadacre Crop written by Amirhossein Hassanzadeh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with Crop yields categories.


"Snap bean production was valued at $363 million in 2018. Moreover, the increasing need in food production, caused by the exponential increase in population, makes this crop vitally important to study. Traditionally, harvest time determination and yield prediction are performed by collecting limited number of samples. While this approach could work, it is inaccurate, labor-intensive, and based on a small sample size. The ambiguous nature of this approach furthermore leaves the grower with under-ripe and over-mature plants, decreasing the final net profit and the overall quality of the product. A more cost-effective method would be a site-specific approach that would save time and labor for farmers and growers, while providing them with exact detail to when and where to harvest and how much is to be harvested (while forecasting yield). In this study we used hyperspectral (i.e., point-based and image-based), as well as biophysical data, to identify spectral signatures and biophysical attributes that could schedule harvest and forecast yield prior to harvest. Over the past two decades, there have been immense advances in the field of yield and harvest modeling using remote sensing data. Nevertheless, there still exists a wide gap in the literature covering yield and harvest assessment as a function of time using both ground-based and unmanned aerial systems. There is a need for a study focusing on crop-specific yield and harvest assessment using a rapid, affordable system. We hypothesize that a down-sampled multispectral system, tuned with spectral features identified from hyperspectral data, could address the mentioned gaps. Moreover, we hypothesize that the airborne data will contain noise that could negatively impact the performance and the reliability of the utilized models. Thus, We address these knowledge gaps with three objectives as below: 1. Assess yield prediction of snap bean crop using spectral and biophysical data and identify discriminating spectral features via statistical and machine learning approaches. 2. Evaluate snap bean harvest maturity at both the plant growth stage and pod maturity level, by means of spectral and biophysical indicators, and identify the corresponding discriminating spectral features. 3. Assess the feasibility of using a deep learning architecture for reducing noise in the hyperspectral data. In the light of the mentioned objectives, we carried out a greenhouse study in the winter and spring of 2019, where we studied temporal change in spectra and physical attributes of snap-bean crop, from Huntington cultivar, using a handheld spectrometer in the visible- to shortwave-infrared domain (400-2500 nm). Chapter 3 of this dissertation focuses on yield assessment of the greenhouse study. Findings from this best-case scenario yield study showed that the best time to study yield is approximately 20-25 days prior to harvest that would give out the most accurate yield predictions. The proposed approach was able to explain variability as high as R2 = 0.72, with spectral features residing in absorption regions for chlorophyll, protein, lignin, and nitrogen, among others. The captured data from this study contained minimal noise, even in the detector fall-off regions. Moving the focus to harvest maturity assessment, Chapter 4 presents findings from this objective in the greenhouse environment. Our findings showed that four stages of maturity, namely vegetative growth, budding, flowering, and pod formation, are distinguishable with 79% and 78% accuracy, respectively, via the two introduced vegetation indices, as snap-bean growth index (SGI) and normalized difference snap-bean growth index (NDSI), respectively. Moreover, pod-level maturity classification showed that ready-to-harvest and not-ready-to-harvest pods can be separated with 78% accuracy with identified wavelengths residing in green, red edge, and shortwave-infrared regions. Moreover, Chapters 5 and 6 focus on transitioning the learned concepts from the mentioned greenhouse scenario to UAS domain. We transitioned from a handheld spectrometer in the visible to short-wave infrared domain (400-2500 nm) to a UAS-mounted hyperspectral imager in the visible-to-near-infrared region (400-1000 nm). Two years worth of data, at two different geographical locations, were collected in upstate New York and examined for yield modeling and harvest scheduling objectives. For analysis of the collected data, we introduced a feature selection library in Python, named “Jostar”, to identify the most discriminating wavelengths. The findings from the yield modeling UAS study show that pod weight and seed length, as two different yield indicators, can be explained with R2 as high as 0.93 and 0.98, respectively. Identified wavelengths resided in blue, green, red, and red edge regions, and 44-55 days after planting (DAP) showed to be the optimal time for yield assessment. Chapter 6, on the other hand, evaluates maturity assessment, in terms of pod classification, from the UAS perspective. Results from this study showed that the identified features resided in blue, green, red, and red-edge regions, contributing to F1 score as high as 0.91 for differentiating between ready-to-harvest vs. not ready-to-harvest. The identified features from this study is in line with those detected from the UAS yield assessment study. In order to have a parallel comparison of the greenhouse study against the UAS study, we adopted the methodology employed for UAS studies and applied it to the greenhouse studies, in Chapter 7. Since the greenhouse data were captured in the visible-to-shortwave-infrared (400-2500 nm) domain, and the UAS study data were captured in the VNIR (400-1000 nm) domain, we truncated the spectral range of the collected data from the greenhouse study to the VNIR domain. The comparison experiment between the greenhouse study and the UAS studies for yield assessment, at two harvest stages early and late, showed that spectral features in 450-470, 500-520, 650, 700-730 nm regions were repeated on days with highest coefficient of determination. Moreover, 46-48 DAP with high coefficient of determination for yield prediction were repeated in five out of six data sets (two early stages, each three data sets). On the other hand, the harvest maturity comparison between the greenhouse study and the UAS data sets showed that similar identified wavelengths reside in ∼450, ∼530, ∼715, and ∼760 nm regions, with performance metric (F1 score) of 0.78, 0.84, and 0.9 for greenhouse, 2019 UAS, and 2020 UAS data, respectively. However, the incorporated noise in the captured data from the UAS study, along with the high computational cost of the classical mathematical approach employed for denoising hyperspectral data, have inspired us to leverage the computational performance of hyperspectral denoising by assessing the feasibility of transferring the learned concepts to deep learning models. In Chapter 8, we approached hyperspectral denoising in spectral domain (1D fashion) for two types of noise, integrated noise and non-independent and non-identically distributed (non-i.i.d.) noise. We utilized Memory Networks due to their power in image denoising for hyperspectral denoising, introduced a new loss and benchmarked it against several data sets and models. The proposed model, HypeMemNet, ranked first - up to 40% in terms of signal-to-noise ratio (SNR) for resolving integrated noise, and first or second, by a small margin for resolving non-i.i.d. noise. Our findings showed that a proper receptive field and a suitable number of filters are crucial for denoising integrated noise, while parameter size was shown to be of the highest importance for non-i.i.d. noise. Results from the conducted studies provide a comprehensive understanding encompassing yield modeling, harvest scheduling, and hyperspectral denoising. Our findings bode well for transitioning from an expensive hyperspectral imager to a multispectral imager, tuned with the identified bands, as well as employing a rapid deep learning model for hyperspectral denoising."--Abstract.



Greenhouse Crop Production


Greenhouse Crop Production
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Author : Henry Gilbert
language : en
Publisher:
Release Date : 1990

Greenhouse Crop Production written by Henry Gilbert and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Greenhouse management categories.




International Symposium On Models For Plant Growth Environmental Control And Farm Management In Protected Cultivation


International Symposium On Models For Plant Growth Environmental Control And Farm Management In Protected Cultivation
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Author : H. Krug
language : en
Publisher:
Release Date : 1989

International Symposium On Models For Plant Growth Environmental Control And Farm Management In Protected Cultivation written by H. Krug and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989 with Farm management categories.




Virtual Augmented And Mixed Reality Industrial And Everyday Life Applications


Virtual Augmented And Mixed Reality Industrial And Everyday Life Applications
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Author : Jessie Y. C. Chen
language : en
Publisher: Springer Nature
Release Date : 2020-07-10

Virtual Augmented And Mixed Reality Industrial And Everyday Life Applications written by Jessie Y. C. Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-10 with Computers categories.


The 2 volume-set of LNCS 12190 and 12191 constitutes the refereed proceedings of the 12th International Conference on Virtual, Augmented and Mixed Reality, VAMR 2020, which was due to be held in July 2020 as part of HCI International 2020 in Copenhagen, Denmark. The conference was held virtually due to the COVID-19 pandemic. A total of 1439 papers and 238 posters have been accepted for publication in the HCII 2020 proceedings from a total of 6326 submissions. The 71 papers included in these HCI 2020 proceedings were organized in topical sections as follows: Part I: design and user experience in VAMR; gestures and haptic interaction in VAMR; cognitive, psychological and health aspects in VAMR; robots in VAMR. Part II: VAMR for training, guidance and assistance in industry and business; learning, narrative, storytelling and cultural applications of VAMR; VAMR for health, well-being and medicine.