Article
Multidisciplinary Sciences
Chih-Jung Chen, Yung-Jhe Yan, Chi-Cho Huang, Jen-Tzung Chien, Chang-Ting Chu, Je-Wei Jang, Tzung-Cheng Chen, Shiou-Gwo Lin, Ruei-Siang Shih, Mang Ou-Yang
Summary: Sweetness is a crucial indicator for measuring the quality of wax apples. Traditional methods of measuring sweetness are labor-intensive and wasteful, prompting the need for non-destructive techniques. Deep learning methods using hyperspectral imaging have been shown to accurately predict sweetness, especially when utilizing the full band range between 400 and 1700 nm with a specialized CNN model.
SCIENTIFIC REPORTS
(2022)
Article
Agronomy
Mariana Chiozza, Kyle A. Parmley, Race H. Higgins, Asheesh K. Singh, Fernando E. Miguez
Summary: The study aimed to rank the prediction accuracy among different crop variables using hyperspectral bands captured at different timepoints during the growing season. Results showed that LAI can be best predicted using reflectance information, and suggest that hyperspectral bands are necessary but not sufficient to improve the prediction of other crop variables such as biomass, seed yield, and seed composition traits.
FIELD CROPS RESEARCH
(2021)
Article
Plant Sciences
Giovanni Melandri, Kelly R. Thorp, Corey Broeckling, Alison L. Thompson, Lori Hinze, Duke Pauli
Summary: Studying the leaf metabolome of cotton plants under water deficit and heat stress revealed membrane lipid remodeling as the main mechanism of adaptation to drought, impacting fiber traits. Hyperspectral reflectance data accurately estimated and predicted various leaf metabolites, offering a rapid and non-destructive method for summarizing the plant physiological status.
FRONTIERS IN PLANT SCIENCE
(2021)
Article
Agronomy
Bosoon Park, Tae-Sung Shin, Jeong-Seok Cho, Jeong-Ho Lim, Ki-Jae Park
Summary: Firmness of blueberries is closely related to microstructural characteristics such as cell shape, cell wall, intercellular spaces, and cell wall color. A deep learning model based on spatial and spectral features of blueberry cells shows potential for classifying blueberry firmness.
Article
Agronomy
Christopher Y. S. Wong, Matthew E. Gilbert, Marshall A. Pierce, Travis A. Parker, Antonia Palkovic, Paul Gepts, Troy S. Magney, Thomas N. Buckley
Summary: Proximal remote sensing is a powerful tool for high-throughput phenotyping of plants in assessing stress response. In this study, ground and tower-based hyperspectral remote sensing data were used to evaluate the drought response of different bean genotypes. The results showed that hyperspectral data can predict physiological traits and rank genotypic drought responses.
Article
Green & Sustainable Science & Technology
Luis Guilherme Teixeira Crusiol, Liang Sun, Zheng Sun, Ruiqing Chen, Yongfeng Wu, Juncheng Ma, Chenxi Song
Summary: This research aims to evaluate the relationship between maize leaf water content (LWC) and ground-based and UAV-based hyperspectral data. The study finds that ground-based hyperspectral data outperforms UAV-based data for LWC monitoring, and HVIs and PLSR models are more suitable for LWC monitoring. The complementary use of ground-based and UAV-based hyperspectral data has the potential for maize LWC monitoring.
Article
Automation & Control Systems
Xin Huang, Li Xia
Summary: Wavelength selection is crucial in near infrared spectral analysis and can enhance prediction performance and interpretability of the model. This study proposes a novel algorithm called iterative distance correlation combined with PLS regression (IDC-PLS) that incorporates the advantages of distance correlation and PLS. The method involves an iterative procedure based on distance correlation to screen wavelength interval variables and the construction of PLS models using all possible wavelength intervals. The results demonstrate that IDC-PLS can improve prediction performance and efficiently select strongly correlated wavelength intervals.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2023)
Article
Environmental Sciences
Onuwa Okwuashi, Christopher E. Ndehedehe, Dupe Nihinlola Olayinka
Summary: This research explores the novel application of Tensor Partial Least Squares (TPLS) for hyperspectral image classification. The results show that TPLS performed better than unfolded PLS, but fell short of traditional classifiers.
GEOCARTO INTERNATIONAL
(2022)
Article
Environmental Sciences
Liang Wan, Weijun Zhou, Yong He, Thomas Cherico Wanger, Haiyan Cen
Summary: In this study, we propose a new transfer learning method called TCA-SVR to transfer leaf nitrogen concentration (LNC) assessment models across different plant species. By analyzing five remote sensing datasets, we find that combining visible, near infrared, and shortwave infrared reflectance achieves the optimal LNC assessment across all datasets. Compared to the established PLSR model, TCA-SVR greatly improves the transferability of the LNC assessment model and further improves performance through model updating.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Agricultural Engineering
Samantha Rubo, Jana Zinkernagel
Summary: Optimizing irrigation and nitrogen management is crucial for vegetable production. This study used hyperspectral reflectance to determine the water and nitrogen supply status of crops. Statistical methods were applied to estimate the plant's status, and the basis for differentiating nitrogen and water management using spectral data has been established.
BIOSYSTEMS ENGINEERING
(2022)
Article
Agriculture, Multidisciplinary
Sandra Marin-San Roman, Juan Fernandez-Novales, Cristina Cebrian-Tarancon, Rosario Sanchez-Gomez, Maria Paz Diago, Teresa Garde-Cerdan
Summary: This study investigated the use of hyperspectral imaging (HSI) to estimate the aromatic composition of Tempranillo Blanco grape berries. The results showed that HSI in the VIS + SW-NIR range could be a good tool for noninvasive and rapid estimation of the aromatic composition of the berries. This provides important information for determining the harvest date of the grapes.
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY
(2023)
Article
Optics
Shubho Mohajan, Yingchao Huang, Nicholas F. Beier, Miles Dyck, Frank Hegmann, Abdul Bais, Amina Hussein
Summary: This study investigates the influence of laser wavelength on the measurement of carbon in agricultural soils using laser-induced breakdown spectroscopy (LIBS). It was found that a 532 nm laser wavelength achieved more accurate prediction of carbon content compared to a 1064 nm wavelength. Additionally, the limit of detection was lower for the 532 nm wavelength compared to the 1064 nm wavelength.
Article
Chemistry, Analytical
Sulaymon Eshkabilov, John Stenger, Elizabeth N. Knutson, Erdem Kucuktopcu, Halis Simsek, Chiwon W. Lee
Summary: The influence of nitrogen, phosphorus, and potassium compounds on the growth dynamics of hydroponically grown lettuce was studied, and optimal wavebands were found for estimating the nutrient levels. The results showed a high correlation between hyperspectral imaging data and laboratory-measured data.
Article
Plant Sciences
Han Zhang, Qiling Hou, Bin Luo, Keling Tu, Changping Zhao, Qun Sun
Summary: Chemical hybridization and genic male sterility systems are the main methods of hybrid wheat production. However, ensuring seed purity has been a challenge. This study successfully established a non-destructive classification method for hybrid and female parent seeds using transmittance hyperspectral imaging technology combined with a machine learning algorithm. This provides a reference for rapid seed purity detection in the hybrid wheat production process.
FRONTIERS IN PLANT SCIENCE
(2022)
Article
Food Science & Technology
Zhaoqi Zheng, Zimin An, Xinyu Liu, Jinghui Chen, Yonghong Wang
Summary: This study employed explicit dynamic simulation and near-infrared hyperspectral reflectance imaging to investigate the bruising of blueberries. The different grades of bruises were distinguished and the effects of factors such as ripeness, loading speed, and loading location on the bruising rate were determined. The results provide a theoretical basis for the accurate identification and classification of blueberry bruise damage.
Article
Engineering, Chemical
Aichen Wang, Renfu Lu, Lijuan Xie
JOURNAL OF FOOD ENGINEERING
(2017)
Article
Agriculture, Multidisciplinary
Fernando A. Mendoza, Karen A. Cichy, Christy Sprague, Amanda Goffnett, Renfu Lu, James D. Kelly
JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE
(2018)
Article
Agronomy
Yuping Huang, Renfu Lu, Kunjie Chen
POSTHARVEST BIOLOGY AND TECHNOLOGY
(2017)
Article
Agriculture, Multidisciplinary
Yuzhen Lu, Renfu Lu
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2018)
Article
Engineering, Chemical
Yuzhen Lu, Renfu Lu
JOURNAL OF FOOD ENGINEERING
(2018)
Article
Engineering, Chemical
Yuping Huang, Renfu Lu, Kunjie Chen
JOURNAL OF FOOD ENGINEERING
(2018)
Article
Optics
Dong Hu, Renfu Lu, Yibin Ying
JOURNAL OF QUANTITATIVE SPECTROSCOPY & RADIATIVE TRANSFER
(2018)
Article
Chemistry, Applied
Gabriel A. Leiva-Valenzuela, Marcela Quilaqueo, Maria Salome Mariotti-Celis, Karis Letelier, Danilo Estay, Franco Pedreschi
Article
Computer Science, Artificial Intelligence
Pengyu Chu, Zhaojian Li, Kyle Lammers, Renfu Lu, Xiaoming Liu
Summary: Researchers have developed a novel deep learning-based apple detection framework called Suppression Mask R-CNN, which achieves high detection accuracy and efficiency in complex orchard environments. By collecting a comprehensive apple orchard dataset using a color camera under different lighting conditions, the framework is able to achieve a detection time of 0.25 seconds per frame and an F1 score of 0.905 on a standard desktop computer, outperforming state-of-the-art models.
PATTERN RECOGNITION LETTERS
(2021)
Article
Automation & Control Systems
Kaixiang Zhang, Kyle Lammers, Pengyu Chu, Zhaojian Li, Renfu Lu
Summary: This study presents a robotic apple harvesting prototype with mechatronic design and motion control. The prototype utilizes deep learning for fruit detection and localization, incorporates a pneumatic/motor actuation mechanism for dexterous movements, and features a vacuum-based end-effector for apple detachment. Additionally, a nonlinear control scheme is developed for accurate and agile motion control, demonstrated through field experiments to showcase the robot's performance in apple harvesting.
Review
Agricultural Engineering
Z. Zhang, A. K. Pothula, R. Lu
APPLIED ENGINEERING IN AGRICULTURE
(2018)
Review
Agricultural Engineering
Y. Lu, R. Lu
TRANSACTIONS OF THE ASABE
(2017)
Article
Agricultural Engineering
Z. Zhang, A. K. Pothula, R. Lu
TRANSACTIONS OF THE ASABE
(2017)
Proceedings Paper
Food Science & Technology
Dong Hu, Renfu Lu, Yibin Ying
SENSING FOR AGRICULTURE AND FOOD QUALITY AND SAFETY IX
(2017)
Article
Agricultural Engineering
Y. Lu, R. Lu
TRANSACTIONS OF THE ASABE
(2017)