Article
Agronomy
Shlomo Sarig, Eli Shlevin, Arkadi Zilberman, Idan Richker, Mordechay Dudai, Shlomo Nezer, Jiftah Ben-Asher
Summary: Canopy nitrogen status is strongly related to canopy chlorophyll content and green color strength. A study utilized RGB camera photographs to develop a tool for assessing plant leaf nitrogen content. The research successfully monitored and calculated nitrogen levels using image processing algorithms and laboratory tests. Additionally, determining nitrogen application based on smartphone photographs proved to be a useful, cost-effective method for growers with smartphones.
Article
Agriculture, Multidisciplinary
F. Morari, V. Zanella, S. Gobbo, M. Bindi, L. Sartori, M. Pasqui, G. Mosca, R. Ferrise
Summary: A novel approach is proposed to manage site-specific nitrogen fertilization in durum wheat by integrating proximal sensing, seasonal weather forecasts, and crop modeling. This approach successfully reduces the amount of nitrogen supplied, improves crop nitrogen use efficiency, and decreases spatial variability in yield and protein content. Further improvements in model performance are necessary.
PRECISION AGRICULTURE
(2021)
Article
Agriculture, Multidisciplinary
Varinderpal-Singh, Kunal, Rajinder Kaur, Mehtab-Singh, Mohkam-Singh, Harpreet-Singh, Bijay-Singh
Summary: The study established prediction models for grain yield and nitrogen uptake using NDVI measurements with the GreenSeeker sensor in different cultivar groups of basmati rice. Sensing the crop at the panicle initiation stage provided accurate predictions for grain yield and N uptake potential. This demonstrates that in-season NDVI data can be used to predict yield and N uptake potential in basmati rice.
PRECISION AGRICULTURE
(2022)
Article
Plant Sciences
Jagdeep-Singh, Varinderpal-Singh
Summary: Canopy reflectance measurements using active optical sensors have the potential to improve in-season nitrogen management in cereals. This study developed and validated a site-specific need-based nitrogen management strategy using a GreenSeeker optical sensor in spring maize. Results showed that sensor measurements at the V9 growth stage provided better corrective nitrogen rates, leading to improved nitrogen use efficiency and grain yield.
JOURNAL OF PLANT NUTRITION
(2022)
Article
Agronomy
Marco Fiorentini, Stefano Zenobi, Roberto Orsini
Summary: The study demonstrates how different soil management and nitrogen fertilization levels can affect the nutritional status and yield of durum wheat, with near infrared band-based vegetation indices being an effective tool for monitoring nutritional status.
Article
Agronomy
S. Gobbo, M. De Antoni Migliorati, R. Ferrise, F. Morari, L. Furlan, L. Sartori
Summary: Nitrogen fertilization in corn is often based on uniform rates and yield goals without considering the spatial and temporal variability of yield potential. This study presents two site-specific N fertilization approaches, integrating crop simulation models, seasonal forecasts and proximal sensing, which led to higher yields, N efficiency and gross margin in 2019 but not in 2020. The inconsistency in 2020 was due to the underestimation of N leaching events caused by major rainfall events not present in historical or seasonal forecast datasets.
EUROPEAN JOURNAL OF AGRONOMY
(2023)
Article
Chemistry, Analytical
Jiri Mezera, Vojtech Lukas, Igor Horniacek, Vladimir Smutny, Jakub Elbl
Summary: The paper focuses on selecting a suitable system for monitoring winter wheat crops and effectively applying nitrogen fertilizers based on their conditions. Through a four-year field experiment using the ISARIA on-the-go system and satellite remote sensing with Sentinel-2 multispectral images, the study compared the vegetation indices obtained from both systems and found positive correlations between them. The correlations were of medium to strong strength (r = 0.51-0.89). The results suggest that both technologies can effectively capture the trends in vegetation development. Moreover, the study analyzed the impact of climatic conditions on vegetation indices, which showed significant variations between different years. The winter wheat yield also differed among the years, with the highest yield in 2017 (7.83 t/ha) and the lowest in 2020 (6.96 t/ha), while there was no statistically significant difference between 2018 (7.27 t/ha) and 2019 (7.44 t/ha).
Article
Engineering, Electrical & Electronic
Yiling Liu, Yanqiong Wang, Xi Yang, Chao-Yang Gong, Yuan Gong
Summary: This review article summarizes the recent advances in optofluidic lasers (OFLs) and their applications in biochemical analysis. OFLs achieve high performance in bio-chemical sensing due to the strong light-matter interaction in the laser cavity. The physical mechanisms, structure, and materials used in OFLs to achieve high sensitivity, disposability, fast response, and high throughput are discussed, as well as the inter-relationship between these performance indicators. The effects of new materials and a comparison with other optical biochemical sensors are also presented, along with the prospects of OFLs.
JOURNAL OF LIGHTWAVE TECHNOLOGY
(2023)
Article
Chemistry, Analytical
Jose Miguel Lopez-Higuera
Summary: This paper presents a doctrinal conception of sensing using Light (SuL) that can encompass any sensing approach using Light Sciences and Technologies. It quickly introduces key requirements of a sensing system and offers examples of detecting diverse measurands using different principles, techniques, and technologies in various sector applications.
Article
Soil Science
Varinderpal-Singh, Kunal, Mehtab-Singh, Bijay-Singh
Summary: This study conducted yield prediction models using optical sensing tools for basmati rice and validated the effectiveness of spectral indices. The study found that leaf color charts are an economical tool that can be used by small farmers in developing countries.
Article
Engineering, Electrical & Electronic
Shen Liu, Peijing Chen, Junxian Luo, Yanping Chen, Bonan Liu, Hang Xiao, Wenqi Yan, Wei Ding, Zhiyong Bai, Jun He, Yiping Wang
Summary: Monitoring electric current is crucial for the reliable operation of power systems and electronic equipment. This study presents an optomechanical cavity-based resonator for current sensing, which shows high sensitivity, short response time, low power consumption, and a compact structure. It is expected to have applications in high precision current and magnetic field sensing.
JOURNAL OF LIGHTWAVE TECHNOLOGY
(2022)
Article
Chemistry, Analytical
LiangLiang Liu, Serhiy Korposh, David Gomez, Ricardo Correia, Barrie R. Hayes-Gill, Stephen P. Morgan
Summary: This work presents a functionalized optical fibre probe with 'cotton-shaped' gold-silica nanostructures for monitoring relative humidity (RH). The sensor utilizes the localized surface plasmon resonance (LSPR) of self-assembled nanostructures and demonstrates a high sensitivity to RH after optimization. The plasmonic hybridization mode sensor exhibited excellent linearity, reversibility, and fast response and recovery times.
SENSORS AND ACTUATORS B-CHEMICAL
(2022)
Article
Optics
Huagang Lin, Yuxin Xing, Xiaolu Chen, Shuo Zhang, Erik Forsberg, Sailing He
Summary: A novel tactile sensor for two-dimensional force location measurements is presented, which is based on polymer-based planar waveguide chirped Bragg gratings (PPCBGs) fabricated on a PMMA substrate. The sensor measures the location and magnitude of an applied force by observing the change of the wavelength of a dip in the measured spectrum and a change in the reflectivity intensity. Experimental results show submillimeter spatial resolution and a sensitivity of 947.02 pm/mm for applied forces in the range of 1-4 N.
Article
Engineering, Electrical & Electronic
Matej Njegovec, Vedran Budinski, Boris Macuh, Denis Donlagic
Summary: Corrosion-induced optical fiber microbending is demonstrated as an efficient method for designing sensors that can detect and locate corrosion events on metal surfaces. The proposed sensors have been successfully applied to both bare metal surfaces and surfaces covered with corrosion-protective coatings, and they possess the simplicity in design and manufacturing, reliable corrosion detection, and accurate corrosion localization.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Review
Chemistry, Inorganic & Nuclear
Shuyu Qian, Ziping Wang, Zhongxiang Zuo, Xiaomeng Wang, Qing Wang, Xun Yuan
Summary: Metal nanoclusters (MNCs) show promising potential for diverse sensing applications, but there is an urgent need for a systematic summary on their physicochemical properties, synthetic strategies, and sensing mechanisms to facilitate their broader utilization in various fields.
COORDINATION CHEMISTRY REVIEWS
(2022)
Article
Agronomy
Tatiana Fernanda Canata, Mauricio Martello, Leonardo Felipe Maldaner, Jadir de Souza Moreira, Jose Paulo Molin
Summary: The adoption of LiDAR technology for spatial analysis of sugarcane fields revealed high spatial variability in plant height, demonstrating the potential of 3D sensing data for crop assessment and production level indication.
Article
Agronomy
Leonardo Felipe Maldaner, Tatiana Fernanda Canata, Jose Paulo Molin
Summary: By installing a hydraulic oil pressure sensor in the chopper of a sugarcane harvester, this study aimed to evaluate the accuracy of sugarcane mass prediction. The results suggest that increasing the data collection frequency by the harvester can improve the spatial variability detection of sugarcane yield at the field level without the need for empirical models or sensor calibration.
Article
Agriculture, Multidisciplinary
F. R. da S. Pereira, J. P. de Lima, R. G. Freitas, A. A. Dos Reis, L. R. do Amaral, G. K. D. A. Figueiredo, R. A. C. Lamparelli, P. S. G. Magalhaes
Summary: This study evaluates the spatial distribution of nitrogen in pasture fields using unmanned aerial vehicle and satellite data, and finds that remote sensing techniques are a reliable approach for nitrogen monitoring in commercial pasture fields. The combination of UAV and satellite data improves the prediction accuracy of nitrogen parameters, with UAV multispectral data resulting in the best accuracies.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Soil Science
Tiago R. Tavares, Abdul M. Mouazen, Lidiane C. Nunes, Felipe R. dos Santos, Fabio L. Melquiades, Thainara R. da Silva, Francisco J. Krug, Jose P. Molin
Summary: The study compared different modelling methods of LIBS data and found the iSPA-PLS method to be the most effective for predicting key fertility attributes in Brazilian tropical soils, providing an efficient and accurate modelling approach. The research discovered the potential value of LIBS technique for predicting fertility attributes in tropical soils, but further investigation is needed to reduce sample preparation procedures.
SOIL & TILLAGE RESEARCH
(2022)
Article
Multidisciplinary Sciences
Rodrigo G. Trevisan, Nicolas F. Martin, Simon Fonteyne, Nele Verhulst, Hugo A. Dorado Betancourt, Daniel Jimenez, Andrea Gardeazabal
Summary: This article discusses the study of maize management decisions in smallholder farming in tropical regions using a dataset collected from CIMMYT's knowledge hub in Chiapas, Mexico. Analyzing data from 4585 fields over a period of 7 years, the dataset can help explain and predict the spatial and temporal variability of maize planting decisions in Chiapas.
Article
Agriculture, Multidisciplinary
Helizani Couto Bazame, Jose Paulo Molin, Daniel Althoff, Mauricio Martello, Lucas De Paula Corredo
Summary: This study implemented a computer vision algorithm to quantify the number of coffee fruits and create yield maps. The results showed that this method effectively explained the factors influencing yield variations and had the advantages of low cost and independence from specific coffee harvester brands.
PRECISION AGRICULTURE
(2022)
Article
Agronomy
Mauricio Martello, Jose Paulo Molin, Helizani Couto Bazame, Tiago Rodrigues Tavares, Leonardo Felipe Maldaner
Summary: This study evaluated the potential of active optical sensors (AOS) to map the spatial and temporal variability of coffee crop yields and provided guidelines for data acquisition. The results showed that different faces of the same coffee plant have different correlations with yield. Vegetation indices measured at the beginning of the coffee cycle have a positive correlation with the yield of that year, but the correlation becomes negative after the start of the rainy season. Additionally, the vegetation index acquired at a specific time has an inverted relationship with the yield of that year and the following (or previous) year due to the biennial nature of coffee production.
Article
Computer Science, Information Systems
Francisco R. da S. Pereira, Aliny A. Dos Reis, Rodrigo G. Freitas, Stanley R. de M. Oliveira, Lucas R. do Amaral, Gleyce K. D. A. Figueiredo, Joao F. G. Antunes, Rubens A. C. Lamparelli, Edemar Moro, Paulo S. G. Magalhaes
Summary: The recent advances in UAV-based remote sensing systems have expanded its applications in agriculture. However, missing parts in UAV orthomosaics due to flight restrictions are common issues in agricultural monitoring. This study proposes a methodological framework using PS and S2 data along with the RF algorithm to impute missing parts in UAV orthomosaics. The framework achieves highly accurate results (RMSE = 6.77-17.33%), leveraging optical satellite imagery to impute up to 50% of missing parts.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2023)
Review
Agronomy
Mauricio Roberto Cherubin, Junior Melo Damian, Tiago Rodrigues Tavares, Rodrigo Goncalves Trevisan, Andre Freitas Colaco, Mateus Tonini Eitelwein, Mauricio Martello, Ricardo Yassushi Inamasu, Osmar Henrique de Castro Pias, Jose Paulo Molin
Summary: Precision agriculture in Brazil has shown significant growth in the past 25 years, with an increasing number and quality of publications, research group interactions, and international collaborations. Soil and plant management are the main focus areas, but research has expanded to include the use of sensors, remote sensing technologies, and decision support tools. A large portion of Brazilian precision agriculture research involves evaluating and adapting imported technologies, but there is potential for future research in digitally based decision support systems, on-farm experimentation, and machine learning approaches.
Article
Agricultural Engineering
Joao V. M. Nicoletti, Marcello R. A. Franchi, Anamari V. de A. Motomiya, Wagner R. Motomiya, Jose P. Molin
Summary: This study assessed the performance of three soil samplers in different management systems. Significant differences were observed in the quality of samples for certain attributes. The hydraulic sampler demonstrated the highest operational efficiency.
REVISTA BRASILEIRA DE ENGENHARIA AGRICOLA E AMBIENTAL
(2022)
Article
Agricultural Engineering
Ricardo Canal Filho, Jose Paulo Molin, Marcelo Chan Fu Wei, Eudocio Rafael Otavio da Silva
Summary: Spatio-temporal local calibrations are required for accurate soil attribute prediction using online NIR spectra ML models.
Article
Agriculture, Multidisciplinary
Helizani Couto Bazame, Jose Paulo Molin, Daniel Althoff, Mauricio Martello
Summary: This study proposes a computer vision system based on deep learning algorithms to detect and classify the maturation stage of coffee fruits. The YOLOv4 and YOLOv3 models showed promise in guiding coffee farmers' decision-making processes.
Article
Computer Science, Artificial Intelligence
Marcelo Chan Fu Wei, Ricardo Canal Filho, Tiago Rodrigues Tavares, Jose Paulo Molin, Afranio Marcio Correa Vieira
Summary: This study evaluated the predictive performance of two dimensionality reduction statistical models (PCR and lasso) for modeling soil spectral data without pretreatment techniques. The results showed that PCR and lasso achieved good performance in predicting soil attributes using raw spectral data. The comparison with literature results that employed pretreatment techniques indicated similar performance. However, there was no consensus on the best calibration approach, which seemed to be attribute and area specific.
Article
Agriculture, Multidisciplinary
Leonardo Felipe Maldaner, Jose Paulo Molin, Mark Spekken
Summary: The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, in order to improve the accuracy and spatial variability characterization of interpolated data. The results showed that the developed filter process, which included global, anisotropic, and anisotropic local analysis, effectively decreased the nugget effect and significantly improved the spatial variability within the data sets. The methodology was tested using raw data sets of corn yield, soil electrical conductivity, and sensor vegetation index, and it successfully reduced RMSE and excluded local outliers.
