Rice monitoring using ENVISAT-ASAR data: preliminary results of a case study in the Mekong River Delta, Vietnam

Vietnam is one of the world’s largest rice exporting countries, and the fertile Mekong River Delta at the southern tip of Vietnam accounts for more than half of the country’s rice production. Unfortunately, a large part of rice crop growing time coincides with a rainy season, resulting in a limited number of cloud-free optical remote sensing images for rice monitoring. Synthetic aperture radar (SAR) data allows for observations independent of weather conditions and solar illumination, and is potentially well suited for rice crop monitoring. The aim of the study was to apply new generation Envisat ASAR data with dual polarization (HH and VV) to rice cropping system mapping and monitoring in An Giang province, Mekong River Delta. Several sample areas were established on the ground, where selected rice parameters (e.g. rice height and biomass) are periodically being measured over a period of 12 months. A correlation analysis of rice parameters and radar imagery values is then being conducted to determine the significance and magnitude of the relationships. This paper describes a review of the previous research studies on rice monitoring using SAR data, the context of this on-going study, and some preliminary results that provide insights on how ASAR imagery could be useful for rice crop monitoring. More work is being done to develop algorithms for mapping and monitoring rice cropping systems, and to validate a rice yield prediction model for one year cycle using time-series SAR imagery

Policy Implications for Green Banking Development in Vietnam

Purpose:  The article studies the factors affecting the development of green banking in Vietnam. Based on the research results, the authors proposed policy implications that contribute to the development of green banking in Vietnam.   Theoretical framework: The article studies the factors affecting the development of green banking in Vietnam. Based on the research results, the authors proposed policy implications that contribute to the development of green banking in Vietnam.   Design/methodology/approach: To survey the level of understanding of bank managers on issues related to green banking, green banking development, and the level of knowledge of officers and employees about green banking development areas, The authors used qualitative and quantitative methods. Quantitative research was carried out from January to February 2023. The authors used structural equation model analysis to test the fit of models with SPSS 20.0 and Amos.   Findings: Quantitative and qualitative indicators have been introduced to measure the development of green banking and the economy. The factor of banking technology has the most substantial impact on the development of green banking and the economy. This is a new research paper finding in the context of the banking industry's digital transformation.   Research, Practical & Social implications: The study also shows that green banking development depends on financial capacity, supporting policies, legal framework, and state regulations. In addition, the study has included the factors of green banking development affecting the green economy from different approaches and analytical methods to evaluate.   Originality/value: Developing green banks not only brings profits but also helps banks increase their prestige, reputation, and value. In addition, the development of green banking associated with the green economy is not only a form but will become the core value of the bank, recognized and appreciated in the trend of integration and sustainable development

Green Banking Development: A Case Study of Vietnam

Purpose: The objectives determine factors affecting green banking development in Vietnam. The authors proposed policy implications that contributed to the green banking development in Vietnam.   Theoretical framework: For long-term sustainable economic development, many countries worldwide have chosen to develop a green economy, including the theory of green banking.   Design/methodology/approach:  The research method of the paper is a combination of qualitative and quantitative research methods. Qualitative research was conducted with a group discussion technique, checked the scales used, and consulted with banking managers on the research issue, thereby building the scales included in the research model and setting up and completing the questionnaire. Quantitative research was carried out from January to February 2023. Processing data by statistical methods, analyzing EFA and CFA, using linear structural model analysis (SEM) to test the fit of models and hypotheses with SPSS 20.0 software and Amos.   Findings: The article showed that banking technology substantially impacts green banking development among eight factors.   Research, Practical & Social implications: The study has inherited and supplemented the scale in the model and a new set of scales used to evaluate the development of green banks, systematized, increased, and developed more basic theoretical issues about banking green.   Originality/value: The paper's originality and value help researchers, managers, and policymakers for Vietnamese commercial banks, in particular, and the banking industry, in general, to apply to contribute to the development of green banking and the green economy in the future

Effects of changing cultural practices on C-band SAR backscatter using Envisat ASAR data in the Mekong River Delta

International audienceChanges in rice cultivation systems have been observed in the Mekong River Delta, Vietnam. Among the changes in cultural practices, the change from transplanting to direct sowing, the use of water-saving technology, and the use of high production method could have impacts on radar remote sensing methods previously developed for rice monitoring. Using Envisat (Environmental Satellite) ASAR (Advanced Synthetic Aperture Radar) data over the province of An Giang, this study showed that the radar backscattering behaviour is much different from that of the reported traditional rice. At the early stage of the season, direct sowing on fields with rough and wet soil surface provides very high backscatter values for HH (Horizontal transmit - Horizontal receive polarisation) and VV (Vertical transmit - Vertical receive polarisation) data, as a contrast compared to the very low backscatter of fields covered with water before emergence. The temporal increase of the backscatter is therefore not observed clearly over direct sowing fields. Hence, the use of the intensity temporal change as a rice classifier proposed previously may not apply. Due to the drainage that occurs during the season, HH, VV and HH/VV are not strongly related to biomass, in contrast with past results. However, HH/VV ratio could be used to derive the rice/non-rice classification algorithm for all conditions of rice fields in the test province. The mapping results using the HH/VV polarization ratio at a single date in the middle period of the rice season were assessed using statistical data at different districts in the province, where very high accuracy was found. The method can be applied to other regions, provided that the synthetic aperture radar data are acquired during the peak period of the rice season, and that few training fields provide adjusted threshold values used in the method

Large-scale Vietnamese point-of-interest classification using weak labeling

Point-of-Interests (POIs) represent geographic location by different categories (e.g., touristic places, amenities, or shops) and play a prominent role in several location-based applications. However, the majority of POIs category labels are crowd-sourced by the community, thus often of low quality. In this paper, we introduce the first annotated dataset for the POIs categorical classification task in Vietnamese. A total of 750,000 POIs are collected from WeMap, a Vietnamese digital map. Large-scale hand-labeling is inherently time-consuming and labor-intensive, thus we have proposed a new approach using weak labeling. As a result, our dataset covers 15 categories with 275,000 weak-labeled POIs for training, and 30,000 gold-standard POIs for testing, making it the largest compared to the existing Vietnamese POIs dataset. We empirically conduct POI categorical classification experiments using a strong baseline (BERT-based fine-tuning) on our dataset and find that our approach shows high efficiency and is applicable on a large scale. The proposed baseline gives an F1 score of 90% on the test dataset, and significantly improves the accuracy of WeMap POI data by a margin of 37% (from 56 to 93%)

Rice seed varietal purity inspection using hyperspectral imaging

When distributing rice seed to farmers, suppliers strive to ensure that all seeds delivered belong to the species that was ordered and that the batch is not contaminated by unhealthy seeds or seeds of a different species. A conventional method to inspect the varietal purity of rice seeds is based on manually selecting random samples of rice seed from a batch and evaluating the physical grain properties through a process of human visual inspection. This is a tedious, laborious, time consuming and extremely inefficient task where only a very small subset of the entire batch of the rice seed can be examined. There is, therefore, a need to automate this process to make it repeatable and more efficient while allowing a larger sample of rice seeds from any batch to be analysed. This paper presents an automatic rice seed inspection method which combines hyperspectral imaging and tools from machine learning to automatically detect seeds which are erroneously contained within a batch when they actually belong to a completely different species. Image data from Near-infrared (NIR) and Visible Light (VIS) hyperspectral cameras are acquired for six common rice seed varieties. Two different classifiers are applied to the data: a Support Vector Machine (SVM) and a Random Forest (RF), where each consists of six one-versus-rest binary classifiers. The results show that combining spectral and shape-based features derived from the rice seeds results in an increase in the precision (PPV) of the multi-label classification to 84% compared with 74% when only visual features are used

Automatic cattle location tracking using image processing

Behavioural scientists track animal behaviour patterns through the construction of ethograms which detail the activities of cattle over time. To achieve this, scientists currently view video footage from multiple cameras located in and around a pen, which houses the animals, to extract their location and determine their activity. This is a time consuming, laborious task, which could be automated. In this paper we extend the well-known Real-Time Compressive Tracking algorithm to automatically determine the location of dairy and beef cows from multiple video cameras in the pen. Several optimisations are introduced to improve algorithm accuracy. An automatic approach for updating the bounding box which discourages the algorithm from learning the background is presented. We also dynamically weight the location estimates from multiple cameras using boosting to avoid errors introduced by occlusion and by the tracked animal moving in and out of the field of view

Le portail g-INFO pour surveiller la grippe Influenza A

Le portail g-INFO pour surveiller la grippe Influenza

Spatial and spectral features utilization on a hyperspectral imaging system for rice seed varietal purity inspection

A conventional method to inspect the varietal purity of rice seeds is based on evaluating human visual inspection where a random sample is drawn from a batch. This is a tedious, laborious, time consuming and extremely inefficient task. This paper presents an automatic rice seed inspection method using Hyperspectral imaging and machine learning, to automatically detect unwanted seeds from other varieties which may be contained in a batch. Hyperspectral image data from Near-infrared (NIR) and Visible cameras are acquired for six common rice seed varieties. The results of applying two classifiers are presented, a Support Vector Machine (SVM) and a Random Forest (RF), where each consists of six one-versus-rest binary classifiers. The results show that combining spectral and shape- based features derived from the rice seeds, increase precision of the multi-label classification to 84% compared 74% when only visual features are used