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Olorunfemi BO, Nwulu NI, Ogbolumani OA. Solar panel surface dirt detection and removal based on arduino color recognition. MethodsX 2022; 10:101967. [PMID: 36593762 PMCID: PMC9803813 DOI: 10.1016/j.mex.2022.101967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 12/11/2022] [Indexed: 12/14/2022] Open
Abstract
Color sensing is a technique for identifying physical changes in materials based on appearance assessment. Dirt deposition on solar panels can change their physical appearance and performance. Considering that dirt accumulation on solar panels needs monitoring to make efficient cleaning schedules, reduce unnecessary costs, and optimize solar panel output generation. Color sensing can achieve fast, accurate, and economical dirt detection, unlike the use of robotic cameras, mathematical formulae, and considering varying output current and voltage methods. Here, we introduce a method that detects and removes dirt on solar panels based on TCS3200 and Arduino Uno components. The approach targets (i.) Panel color measurement, calibration, threshold selection process, (ii.) comparison of color measurement values, and (iii.) align further calibration in response to discoloration of solar panels. This method aims to correct the dirt detection methods previously in use. Hence, a high-speed rolling brush arrangement is designed to improve the cleaning of the solar panel without using water. Further investigations of the panel's color may require some improvement in terms of increasing the sensitivity of the color sensor even with increased distance from the solar panel. Combining multiple color sensors may also be necessary.
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Christ A, Schmittmann O, Schulze Lammers P. Capability of the TrueColor Sensor Array for Determining the Nitrogen Supply in Winter Barley ( Hordeum vulgare L.). SENSORS (BASEL, SWITZERLAND) 2022; 22:6032. [PMID: 36015794 PMCID: PMC9414219 DOI: 10.3390/s22166032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/01/2022] [Accepted: 08/09/2022] [Indexed: 06/15/2023]
Abstract
In agriculture, efforts are being made to reduce pesticides and fertilizers because of the possible negative environmental impacts, high costs, political requirements, and declining social acceptance. With precision farming, significant savings can be achieved by the site-specific application of fertilizers. In contrast to currently available single sensors and camera-based systems, arrays or line sensors provide a suitable spatial resolution without requiring complex signal processing and promise significant potential regarding price and precision. Such systems comprise a cost-effective and compact unit that can be extended to any working width by cascading into arrays. In this study, experiments were performed to evaluate the applicability of a TrueColor sensor array in monitoring the nitrogen supply of winter barley during its growth. This sensor is based on recording the reflectance values in various channels of the CIELab color space: luminosity, green-red, and blue-yellow. The unique selling point of this sensor is the detection of luminosity because only the CIELab color space provides this opportunity. Strong correlations were found between the different reflection channels and the nitrogen level (R² = 0.959), plant coverage (R² = 0.907), and fresh mass yield (R² = 0.866). The fast signal processing allows this sensor to meet stringent demands for the operating speed, spatial resolution, and price structure.
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Guo Y, Zhao Y, Rothfus TA, Avalos AS. A novel invasive plant detection approach using time series images from unmanned aerial systems based on convolutional and recurrent neural networks. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07560-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Disease Control and Prevention in Rare Plants Based on the Dominant Population Selection Method in Opportunistic Social Networks. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:1489988. [PMID: 35087578 PMCID: PMC8789441 DOI: 10.1155/2022/1489988] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 12/27/2021] [Accepted: 12/31/2021] [Indexed: 11/18/2022]
Abstract
The spread of seeds of rare and dangerous plants affects the regeneration, pattern, genetic structure, invasion, and settlement of plant populations. However, seed transmission is a relatively weak research link. The spread of plant seeds is not controlled by the communicator. Rather, this event results from the interaction between the host and the external environment determined by the mother. The way plants transmit and accept seeds is similar to how user nodes accept data transmission requests in social networks. Plants select the characteristics including seed size, maturity time, and gene matching, which are consistent with the size, delay, and keywords of the data received by the user. In this study, we selected rare and endangered Pterospermum heterophyllum as the research object and applied them to a social network. All plants were considered nodes and all seeds as transmitted data. This method avoids the influence of errors in actual sampling and statistical laws. By using historical information to record the reception of seeds, the Infection and Immunity Algorithm (IAIA) in opportunistic social networks was established. This method selects healthy plants through plant social populations and reduces the number of diseased plants. The experimental results show that the IAIA algorithm has a good effect in distinguishing dominant seedlings from seedlings with disease genes and realizes the selection of dominant plants in social networks.
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Forero MG, Mambuscay CL, Monroy MF, Miranda SL, Méndez D, Valencia MO, Gomez Selvaraj M. Comparative Analysis of Detectors and Feature Descriptors for Multispectral Image Matching in Rice Crops. PLANTS (BASEL, SWITZERLAND) 2021; 10:plants10091791. [PMID: 34579324 PMCID: PMC8465351 DOI: 10.3390/plants10091791] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 08/18/2021] [Accepted: 08/26/2021] [Indexed: 06/13/2023]
Abstract
Precision agriculture has greatly benefited from advances in machine vision and image processing techniques. The use of feature descriptors and detectors allows to find distinctive keypoints in an image and the use of this approach for agronomical applications has become a widespread field of study. By combining near infrared (NIR) images, acquired with a modified Nikon D80 camera, and visible spectrum (VIS) images, acquired with a Nikon D300s, a proper crop identification could be obtained. Still, the use of different sensors brings an image matching challenge due to the difference between cameras and the possible distortions from each imaging technique. The aim of this paper is to compare the performance of several feature descriptors and detectors by comparing near infrared and visual spectral bands in rice crop images. Therefore, a group of 20 different scenes with different cameras and growth stages in a rice crop were evaluated. Thus, red, green, blue (RGB) and L, a, b (CIE L*a*b*) channels were extracted from VIS images in order to compare the matches obtained between each of them and the corresponding NIR image. The BRISK, SURF, SIFT, ORB, KAZE, and AKAZE methods were implemented, which act as descriptors and detectors. Additionally, a combination was made between the FAST algorithm for the detection of keypoints with the BRIEF, BRISK, and FREAK methods for features description. BF and FLANN matching methods were used. The algorithms were implemented in Python using OpenCV library. The green channel presented the highest number of correct matches in all methods. In turn, the method that presented the highest performance both in time and in the number of correct matches was the combination of the FAST feature detector and the BRISK descriptor.
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Affiliation(s)
- Manuel G. Forero
- Semillero Lún, Facultad de Ingeniería, Universidad de Ibagué, Ibagué 730002, Colombia; (C.L.M.); (M.F.M.); (S.L.M.); (D.M.)
| | - Claudia L. Mambuscay
- Semillero Lún, Facultad de Ingeniería, Universidad de Ibagué, Ibagué 730002, Colombia; (C.L.M.); (M.F.M.); (S.L.M.); (D.M.)
| | - María F. Monroy
- Semillero Lún, Facultad de Ingeniería, Universidad de Ibagué, Ibagué 730002, Colombia; (C.L.M.); (M.F.M.); (S.L.M.); (D.M.)
| | - Sergio L. Miranda
- Semillero Lún, Facultad de Ingeniería, Universidad de Ibagué, Ibagué 730002, Colombia; (C.L.M.); (M.F.M.); (S.L.M.); (D.M.)
| | - Dehyro Méndez
- Semillero Lún, Facultad de Ingeniería, Universidad de Ibagué, Ibagué 730002, Colombia; (C.L.M.); (M.F.M.); (S.L.M.); (D.M.)
| | - Milton Orlando Valencia
- International Center for Tropical Agriculture (CIAT), Cali 763537, Colombia; (M.O.V.); (M.G.S.)
| | - Michael Gomez Selvaraj
- International Center for Tropical Agriculture (CIAT), Cali 763537, Colombia; (M.O.V.); (M.G.S.)
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He M, Xiong X, Wang L, Hou D, Bolan NS, Ok YS, Rinklebe J, Tsang DCW. A critical review on performance indicators for evaluating soil biota and soil health of biochar-amended soils. JOURNAL OF HAZARDOUS MATERIALS 2021; 414:125378. [PMID: 33652215 DOI: 10.1016/j.jhazmat.2021.125378] [Citation(s) in RCA: 95] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 12/25/2020] [Accepted: 02/06/2021] [Indexed: 05/27/2023]
Abstract
Amendment of soil with biochar has been widely investigated for soil quality improvement in terms of biotic and abiotic functionalities. The performance of biochar-based amendment varies according to the site characteristics, biochar properties, and soil management targets. There is no existing review that summarizes a broad range of performance indicators to evaluate the health of biochar-amended soil. Based on the latest studies on soil amendment with biochar, this review critically analyzes the soil health indicators that reveal the potential impact of biochar amendment with respect to physicochemical properties, biological properties, and overall soil quality. It is found that soil pH, soil aggregate stability, and soil organic matter are the basic indicators that could influence most of the soil functions, which should be prioritized for measurement. Relevant functional indicators (e.g., erosion rate, crop productivity, and ecotoxicity) should be selected based on the soil management targets of biochar application in agricultural soils. With this review, it is expected that target-oriented performance indicators can be selected in future studies for field-relevant evaluation of soil amendment by biochar under different situations. Therefore, a more cost-effective and purpose-driven assessment protocol for biochar-amended soils can be devised by using relevant measurable attributes suggested in this review.
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Affiliation(s)
- Mingjing He
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Xinni Xiong
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Lei Wang
- Institute of Construction Materials, Technische Universität Dresden, 01062 Dresden, Germany
| | - Deyi Hou
- School of Environment, Tsinghua University, Beijing 100084, China
| | - Nanthi S Bolan
- Global Centre for Environmental Remediation, Faculty of Science, Enginnering and Environment, The University of Newcastle, Callaghan, NSW 2308, Australia; Cooperative Research Centre for High Performance Soils (Soil CRC), Callaghan, NSW 2308, Australia
| | - Yong Sik Ok
- Korea Biochar Research Center & Division of Environmental Science and Ecological Engineering, Korea University, Seoul, Republic of Korea
| | - Jörg Rinklebe
- Laboratory of Soil, and Groundwater-Management, School of Architecture and Civil Engineering, Institute of Foundation Engineering, Water, and Waste, Management, University of Wuppertal, Wuppertal, Germany; Department of Environment, Energy and Geoinformatics, Sejong University, Seoul 05006, Republic of Korea
| | - Daniel C W Tsang
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China.
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Tackling Heterogeneous Color Registration: Binning Color Sensors. SENSORS 2021; 21:s21092950. [PMID: 33922365 PMCID: PMC8122774 DOI: 10.3390/s21092950] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/19/2021] [Accepted: 04/20/2021] [Indexed: 11/29/2022]
Abstract
Intelligent systems for interior lighting strive to balance economical, ecological, and health-related needs. For this purpose, they rely on sensors to assess and respond to the current room conditions. With an augmented demand for more dedicated control, the number of sensors used in parallel increases considerably. In this context, the present work focuses on optical sensors with three spectral channels used to capture color-related information of the illumination conditions such as their chromaticities and correlated color temperatures. One major drawback of these devices, in particular with regard to intelligent lighting control, is that even same-type color sensors show production related differences in their color registration. Standard methods for color correction are either impractical for large-scale production or they result in large colorimetric errors. Therefore, this article shows the feasibility of a novel sensor binning approach using the sensor responses to a single white light source for cluster assignment. A cluster specific color correction is shown to significantly reduce the registered color differences for a selection of test stimuli to values in the range of 0.003–0.008 Δu′v′, which enables the wide use of such sensors in practice and, at the same time, requires minimal additional effort in sensor commissioning.
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Cotrim WDS, Minim VPR, Felix LB, Minim LA. Short convolutional neural networks applied to the recognition of the browning stages of bread crust. J FOOD ENG 2020. [DOI: 10.1016/j.jfoodeng.2020.109916] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Cranston LM, Pembleton KG, Burkitt LL, Curtis A, Donaghy DJ, Gourley CJP, Harrington KC, Hills JL, Pembleton LW, Rawnsley RP. The role of forage management in addressing challenges facing Australasian dairy farming. ANIMAL PRODUCTION SCIENCE 2020. [DOI: 10.1071/an18570] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Forage management underpins the viability of pastoral dairy systems. This review investigated recent developments in forage research and their potential to enable pastoral dairy systems to meet the challenges that will be faced over the next 10 years. Grazing management, complementary forages, pasture diversity, fertiliser use, chemical restriction, irrigation management and pasture breeding are considered. None of these areas of research are looking to increase production directly through increased inputs, but, rather, they aim to lift maximum potential production, defend against production decline or improve the efficiency of the resource base and inputs. Technology approaches consistently focus on improving efficiency, while genetic improvement or the use of complementary forages and species diversity aim to lift production. These approaches do not require additional labour to implement, but many will require an increase in skill level. Only a few areas will help address animal welfare (e.g. the use of selected complementary forages and novel endophytes) and only complementary forages will help address increased competition from non-dairy alternatives, by positively influencing the properties of milk. Overall, the diversity of activity and potential effects will provide managers of pastoral dairy systems with the best tools to respond to the production and environmental challenges they face over the next 10 years.
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Hussain MA, Sheikh-Akbari A, Mporas I. Colour Constancy for Image of Non-Uniformly Lit Scenes. SENSORS (BASEL, SWITZERLAND) 2019; 19:E2242. [PMID: 31096584 PMCID: PMC6567322 DOI: 10.3390/s19102242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 05/09/2019] [Accepted: 05/10/2019] [Indexed: 06/09/2023]
Abstract
Digital camera sensors are designed to record all incident light from a captured scene, but they are unable to distinguish between the colour of the light source and the true colour of objects. The resulting captured image exhibits a colour cast toward the colour of light source. This paper presents a colour constancy algorithm for images of scenes lit by non-uniform light sources. The proposed algorithm uses a histogram-based algorithm to determine the number of colour regions. It then applies the K-means++ algorithm on the input image, dividing the image into its segments. The proposed algorithm computes the Normalized Average Absolute Difference (NAAD) for each segment and uses it as a measure to determine if the segment has sufficient colour variations. The initial colour constancy adjustment factors for each segment with sufficient colour variation is calculated. The Colour Constancy Adjustment Weighting Factors (CCAWF) for each pixel of the image are determined by fusing the CCAWFs of the segments, weighted by their normalized Euclidian distance of the pixel from the center of the segments. Results show that the proposed method outperforms the statistical techniques and its images exhibit significantly higher subjective quality to those of the learning-based methods. In addition, the execution time of the proposed algorithm is comparable to statistical-based techniques and is much lower than those of the state-of-the-art learning-based methods.
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Affiliation(s)
- Md Akmol Hussain
- School of Computing, Creative Technology and Engineering, Leeds Beckett University, Leeds LS1 3HE, UK.
| | - Akbar Sheikh-Akbari
- Communications and Intelligent Systems Group, School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9EU, UK.
| | - Iosif Mporas
- Communications and Intelligent Systems Group, School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9EU, UK.
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Sandino J, Gonzalez F, Mengersen K, Gaston KJ. UAVs and Machine Learning Revolutionising Invasive Grass and Vegetation Surveys in Remote Arid Lands. SENSORS 2018; 18:s18020605. [PMID: 29462912 PMCID: PMC5856113 DOI: 10.3390/s18020605] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 01/25/2018] [Accepted: 02/11/2018] [Indexed: 11/16/2022]
Abstract
The monitoring of invasive grasses and vegetation in remote areas is challenging, costly, and on the ground sometimes dangerous. Satellite and manned aircraft surveys can assist but their use may be limited due to the ground sampling resolution or cloud cover. Straightforward and accurate surveillance methods are needed to quantify rates of grass invasion, offer appropriate vegetation tracking reports, and apply optimal control methods. This paper presents a pipeline process to detect and generate a pixel-wise segmentation of invasive grasses, using buffel grass (Cenchrus ciliaris) and spinifex (Triodia sp.) as examples. The process integrates unmanned aerial vehicles (UAVs) also commonly known as drones, high-resolution red, green, blue colour model (RGB) cameras, and a data processing approach based on machine learning algorithms. The methods are illustrated with data acquired in Cape Range National Park, Western Australia (WA), Australia, orthorectified in Agisoft Photoscan Pro, and processed in Python programming language, scikit-learn, and eXtreme Gradient Boosting (XGBoost) libraries. In total, 342,626 samples were extracted from the obtained data set and labelled into six classes. Segmentation results provided an individual detection rate of 97% for buffel grass and 96% for spinifex, with a global multiclass pixel-wise detection rate of 97%. Obtained results were robust against illumination changes, object rotation, occlusion, background cluttering, and floral density variation.
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Affiliation(s)
- Juan Sandino
- Institute for Future Environments; Robotics and Autonomous Systems, Queensland University ofTechnology (QUT), 2 George St, Brisbane City QLD 4000, Australia.
| | - Felipe Gonzalez
- Institute for Future Environments; Robotics and Autonomous Systems, Queensland University ofTechnology (QUT), 2 George St, Brisbane City QLD 4000, Australia.
| | - Kerrie Mengersen
- School of Mathematical Sciences; ARC Centre of Excellence for Mathematical & Statistical Frontiers(ACEMS), Queensland University of Technology (QUT), 2 George St, Brisbane City QLD 4000, Australia.
| | - Kevin J Gaston
- Environment and Sustainability Institute, University of Exeter, Penryn, Cornwall TR10 9FE, UK.
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