Article
Environmental Sciences
Yanchao Zhang, Wen Yang, Ying Sun, Christine Chang, Jiya Yu, Wenbo Zhang
Summary: This study examined the fusion of spectral bands information and vegetation indices for almond plantation classification using different machine learning algorithms. It was found that spectral information can be used for ground classification, with SVM performing the best among the algorithms tested. The combination of multispectral bands and vegetation indices can improve classification accuracy, with specific vegetation indices like NDEGE, NDVIG, and NDVGE showing consistent performance in enhancing accuracy.
Article
Agriculture, Multidisciplinary
Yi-Ping Wang, Yu-Chieh Chang, Yuan Shen
Summary: This study used aerial images acquired by a quadcopter UAV equipped with a multispectral sensor to estimate plant nitrogen content in rice crops. By introducing a variable N-index, the rapid changes in plant nitrogen content during the vegetative phase were addressed, and the most suitable vegetation indices and period were identified to capture the variations in N-index of rice plants.
PRECISION AGRICULTURE
(2022)
Article
Agriculture, Multidisciplinary
Jorge Rodriguez, Ivan Lizarazo, Flavio Prieto, Victor Angulo-Morales
Summary: This study evaluates a method for potato late blight assessment and detection using UAV-based multispectral imagery, incorporating machine learning algorithms for improved efficiency and accuracy in detection.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Biodiversity Conservation
Ning Wang, Yuchuan Guo, Xuan Wei, Mingtong Zhou, Huijing Wang, Yunbao Bai
Summary: This study used unmanned aerial vehicles and machine learning methods to map and predict the natural vegetation of an oasis in the Taklamakan Desert in China. The results showed that using a combination of visible and multispectral vegetation indices can effectively obtain the fractional vegetation cover of sparse vegetation.
ECOLOGICAL INDICATORS
(2022)
Article
Environmental Sciences
Yang Liu, Haikuan Feng, Jibo Yue, Zhenhai Li, Xiuliang Jin, Yiguang Fan, Zhihang Feng, Guijun Yang
Summary: The study used UAV hyperspectral images to extract characteristic data of potatoes in different growth periods and optimized and screened the data using various methods, combined with PLSR and GPR for AGB estimation. The results show that vegetation indexes, spectral-location feature parameters, and spectral-reflection feature parameters are correlated with AGB in each growth period, with correlations improving and then degrading from the budding period to the starch-storage period.
Article
Environmental Sciences
Quan Yin, Yuting Zhang, Weilong Li, Jianjun Wang, Weiling Wang, Irshad Ahmad, Guisheng Zhou, Zhongyang Huo
Summary: This study uses UAV multispectral imagery to monitor crop stress during the pre-heading stage in the mid-lower Yangtze River area of China, and proposes a fused model based on LSTM that achieves high accuracy and robust generalization, aiding in mitigating winter wheat frost risks and increasing yields.
Article
Environmental Sciences
Amarasingam Narmilan, Felipe Gonzalez, Arachchige Surantha Ashan Salgadoe, Unupen Widanelage Lahiru Madhushanka Kumarasiri, Hettiarachchige Asiri Sampageeth Weerasinghe, Buddhika Rasanjana Kulasekara
Summary: This research utilizes unmanned aerial vehicles and spectral vegetation indices to infer chlorophyll content in sugarcane crops and compares the performance of multiple machine learning algorithms in predicting chlorophyll content. The findings demonstrate the accuracy of estimating chlorophyll content using multispectral UAVs and emphasize the importance of this methodology in crop nutrition management in sugarcane plantations.
Article
Agriculture, Multidisciplinary
Lang Qiao, Dehua Gao, Ruomei Zhao, Weijie Tang, Lulu An, Minzan Li, Hong Sun
Summary: This study explores the potential of fusing morphological information and spectral information to improve the accuracy of leaf area index (LAI) estimation for maize in multiple growth stages. The results show that the fusion of canopy morphological parameters and vegetation indices can improve the dynamic estimate accuracy of maize LAI and provide a feasible method for crop growth information monitoring based on UAV platform. The study highlights the importance of accurately and rapidly monitoring LAI for precision agriculture.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Environmental Sciences
Salvatore Filippo Di Gennaro, Piero Toscano, Matteo Gatti, Stefano Poni, Andrea Berton, Alessandro Matese
Summary: Analysis of the spectral response of vegetation using optical sensors is crucial for non-destructive remote monitoring of crops. This study compares the performance of two commercial UAV-based products, DJI P4M and SENOP HSC-2, for acquiring multispectral and hyperspectral images in vineyards. The results show the importance of radiometric calibration using reference panels for multispectral images, and the higher accuracy of the hyperspectral camera. The use of segmentation techniques can resolve performance issues caused by mixed targets.
Article
Environmental Sciences
Shubham Anchal, Sonam Bahuguna, Priti, Probir Kumar Pal, Devshree Kumar, P. V. S. Murthy, Amit Kumar
Summary: The UAV-based remote sensing technique, with the combination of linear regression model and multispectral sensor, successfully estimated the biomass and nitrogen level of Stevia rebaudiana, providing reliable data support for its optimized cultivation.
GEOCARTO INTERNATIONAL
(2022)
Article
Environmental Sciences
Kai O. Bergmueller, Mark C. Vanderwel
Summary: This study used spectral information from UAV imagery to predict tree mortality in 38 forest stands in western Canada. The inclusion of multispectral indices improved the prediction accuracy, and different tree species had varying levels of prediction performance. However, all models tended to overpredict tree mortality.
Article
Agronomy
Jun Zhang, Xinxin Wang, Jingyan Liu, Dongfang Zhang, Yin Lu, Yuhong Zhou, Lei Sun, Shenglin Hou, Xiaofei Fan, Shuxing Shen, Jianjun Zhao
Summary: The phenotypic parameters of Chinese cabbage were accurately and quickly evaluated using a low-cost UAV system equipped with imaging equipment. The results showed that the UAV imaging predictions of Chinese cabbage dimensions and SPAD value were comparable to manual measurements in the field, demonstrating the usefulness of UAVs for acquiring quantitative phenotypic data and providing a reliable phenotyping tool for Chinese cabbage breeding traits.
Article
Biochemical Research Methods
Wen Pan, Xiaoyu Wang, Yan Sun, Jia Wang, Yanjie Li, Sheng Li
Summary: In this study, UAV multispectral remote sensing data was used to detect vegetation in karst areas, and the performance of four machine learning models was compared. The results showed that the Gradient Boosting Machine model achieved the highest accuracy in detecting karst vegetation. This study provided a methodological reference for vegetation detection in karst areas in eastern China.
Article
Plant Sciences
Yiru Ma, Lulu Ma, Qiang Zhang, Changping Huang, Xiang Yi, Xiangyu Chen, Tongyu Hou, Xin Lv, Ze Zhang
Summary: UAV remote sensing is of great significance in cotton yield monitoring. Visible vegetation indices and texture features extracted from RGB images obtained by UAVs are significantly correlated with cotton yield. Combining these indices and features in a model can accurately monitor cotton yield.
FRONTIERS IN PLANT SCIENCE
(2022)
Article
Agriculture, Multidisciplinary
Mingchao Shao, Chenwei Nie, Aijun Zhang, Liangsheng Shi, Yuanyuan Zha, Honggen Xu, Hongye Yang, Xun Yu, Yi Bai, Shuaibing Liu, Minghan Cheng, Tao Lin, Ningbo Cui, Wenbin Wu, Xiuliang Jin
Summary: This study used deep learning segmentation methods to quantify the impact of maize tassels on LAI estimates and evaluated the influence of variable quantity on LAI estimates. The results show that the VGG-encoded U-Net model achieved the highest accuracy in segmenting the multispectral dataset. The growth of tassels affects the segmentation accuracy, and tassels have the greatest impact on the modified nonlinear vegetation index. Removing tassels from images significantly improves the accuracy of LAI estimation using the gradient-boosting decision tree method. The estimation method using nine vegetation indices achieved the highest accuracy.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2023)
Article
Environmental Sciences
Manuel Garcia Rincon, Diego Mendez, Julian D. Colorado
Summary: This paper proposes a low-cost plant phenotyping system that combines low-density LiDAR data with multispectral imagery for real-time plant morphology data acquisition and the establishment of four-dimensional plant models.
Article
Chemistry, Multidisciplinary
Ivan F. Mondragon Bernal, Natalia E. Lozano-Ramirez, Julian M. Puerto Cortes, Sergio Valdivia, Rodrigo Munoz, Juan Aragon, Rodolfo Garcia, Giovanni Hernandez
Summary: This paper introduces and evaluates a system for power substation operational training using virtual reality and serious games. The system creates interactive models for professional training and has shown acceptable usability and engagement, making it suitable for utility companies' vocational training in safety-focused operations.
APPLIED SCIENCES-BASEL
(2022)
Article
Energy & Fuels
Miguel Vivert, Rafael Diez, Marc Cousineau, Diego Bernal Cobaleda, Diego Patino, Philippe Ladoux
Summary: This article proposes a real-time selective harmonic elimination technique for a single-phase cascaded multilevel inverter, which dynamically adjusts switching angles and utilizes a digital PI controller to eliminate harmonics and stabilize the fundamental component of the output voltage.
Article
Infectious Diseases
W. M. Fong Amaris, Carol Martinez, Liliana J. Cortes-Cortes, Daniel R. Suarez
Summary: This study presents an image-based approach to evaluate the coloration quality of TBS used for malaria diagnosis. By conducting background segmentation and using the histogram of the H component from the HSV colour space as a feature vector, the coloration quality of the smears can be effectively discriminated.
Article
Forestry
Willintong Marin, Ivan F. Mondragon, Julian D. Colorado
Summary: This paper introduces an integrated aerial system for identifying Amazonian Moriche palm in dense forests using a deep learning approach. Through field experiments, the model achieved a precision identification rate of 98%. The model is fully automatic and suitable for identifying and inventorying this species under complex conditions.
Article
Energy & Fuels
Juan Sebastian Roncancio, Jose Vuelvas, Diego Patino, Carlos Adrian Correa-Florez
Summary: Access to electricity is crucial for human growth, yet a significant portion of the global population still lacks energy access. In rural communities in Colombia, the National Interconnected System's lack of quality and stability poses challenges to energy access. This research proposes a flexible energy market based on bi-level mixed-integer linear programming to improve the rural power grid's quality. The study focuses on utilizing energy from the rural grid to power a heating, ventilation, and air-conditioning system in a flower greenhouse, and evaluates the flexibility of the system under different pricing schemes.
Article
Plant Sciences
Edgar Andres Gutierrez, Ivan Fernando Mondragon, Julian D. Colorado, Diego Mendez Ch
Summary: This paper proposes an integrated method for estimating soil moisture in potato crops using a low-cost wireless sensor network. Results show that 25 nodes are optimal for achieving estimation errors less than 2%.
Article
Plant Sciences
Andres Jaramillo-Botero, Julian Colorado, Mauricio Quimbaya, Maria Camila Rebolledo, Mathias Lorieux, Thaura Ghneim-Herrera, Carlos A. Arango, Luis E. Tobon, Jorge Finke, Camilo Rocha, Fernando Munoz, John J. Riascos, Fernando Silva, Ngonidzashe Chirinda, Mario Caccamo, Klaas Vandepoele, William A. Goddard
Summary: The OMICAS alliance is a part of the Colombian government's Scientific Ecosystem and aims to promote world-class research and improve higher education across the nation. It focuses on advancing plant science and developing technological solutions for agricultural productivity and sustainability. The alliance uses multi-scale, multi-institutional, and multi-disciplinary strategies and infrastructure to characterize crop models and elucidate the relationships between different levels of omics data for the production of new germplasm with improved agricultural traits.
FRONTIERS IN PLANT SCIENCE
(2022)
Article
Robotics
Sofia Coloma, Carol Martinez, Baris Can Yalcin, Miguel A. Olivares-Mendez
Summary: Geological formations, environmental conditions, and soil mechanics have negative effects on rovers' mobility, and underestimating these effects can jeopardize missions. This letter proposes a system that estimates rover's mobility risks using sensors and machine learning, and assists human-teleoperation tasks through a graphical user interface. Experimental results demonstrate the system's importance in decision-making processes for overcoming hazardous situations.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Engineering, Aerospace
Vivek Muralidharan, Mohatashem Reyaz Makhdoomi, Kuldeep Rambhai Barad, Lina Maria Amaya-Mejia, Kathleen C. Howell, Carol Martinez, Miguel Olivares-Mendez
Summary: This investigation proposes a rendezvous technique in cislunar space that leverages orbit and attitude dynamics in the Circular Restricted Three-body Problem. An autonomous Guidance, Navigation and Control (GNC) technique is demonstrated where a chaser spacecraft approaches a target spacecraft in a synodic-resonant orbit. The efficacy of the technique is tested through hardware-in-the-loop laboratory experiments.
Article
Chemistry, Analytical
Andres F. Duque, Diego Patino, Julian D. Colorado, Eliel Petro, Maria C. Rebolledo, Ivan F. Mondragon, Natalia Espinosa, Nelson Amezquita, Oscar D. Puentes, Diego Mendez, Andres Jaramillo-Botero
Summary: The use of UAV images for biomass and nitrogen estimation in rice cultivation offers opportunities for improving yields and supporting ecosystem monitoring. In this study, machine learning models were used to estimate biomass and nitrogen based on ground-truth data and Vegetation Indices derived from UAV images.
Article
Chemistry, Multidisciplinary
Maxime Hubert Delisle, Olga-Orsalia Christidi-Loumpasefski, Baris C. Yalcin, Xiao Li, Miguel Olivares-Mendez, Carol Martinez
Summary: The paper proposes a hybrid-compliant mechanism for capturing space debris, which can target a wide range of small uncooperative debris in low Earth orbit. The study conducts a simulation to validate the necessity of hybrid compliance and demonstrates its importance in ensuring the safe and reliable capture of a broader range of debris.
APPLIED SCIENCES-BASEL
(2023)
Article
Biotechnology & Applied Microbiology
Daniel Bonilla, Manuela Bravo, Stephany P. Bonilla, Angela M. Iragorri, Diego Mendez, Ivan F. Mondragon, Catalina Alvarado-Rojas, Julian D. Colorado
Summary: Stroke is a prevalent condition worldwide, causing disability and death. Physical rehabilitation is crucial for motor recovery in stroke patients. We developed a robotic hand exoskeleton that uses EMG signals to predict hand movements and adaptive control to compensate for muscle fatigue during rehabilitation exercises. This system could assist in repetitive and intense rehabilitation therapy for stroke patients.
BIOENGINEERING-BASEL
(2023)
