IoT Based Agricultural Business Model for Estimating Crop Health Management to Reduce Farmer Distress Using SVM and Machine Learning

2021 ◽  
pp. 165-183
Author(s):  
Ishita Banerjee ◽  
P. Madhumathy
Keyword(s):  
Machine Learning ◽  
Business Model ◽  
Health Management ◽  
Agricultural Business ◽  
Crop Health

Beyond Cloud: A Fused Optic and SAR Based Solution to Monitor Crop Health

2021 ◽  
Author(s):  
Brianna Pagán ◽  
Adekunle Ajayi ◽  
Mamadou Krouma ◽  
Jyotsna Budideti ◽  
Omar Tafsi
Keyword(s):  
Machine Learning ◽  
Satellite Imagery ◽  
Cloud Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Learning Approaches ◽  
Area Index ◽  
Spatial And Temporal Scales ◽  
Field Level ◽  
Crop Health

<p>The value of satellite imagery to monitor crop health in near-real time continues to exponentially grow as more missions are launched making data available at higher spatial and temporal scales. Yet cloud cover remains an issue for utilizing vegetation indexes (VIs) solely based on optic imagery, especially in certain regions and climates. Previous research has proven the ability to reconstruct VIs like the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) by leveraging synthetic aperture radar (SAR) datasets, which are not inhibited by cloud cover. Publicly available data from SAR missions like Sentinel-1 at relatively decent spatial resolutions present the opportunity for more affordable options for agriculture users to integrate satellite imagery in their day to day operations. Previous research has successfully reconstructed optic VIs (i.e. from Sentinel-2) with SAR data (i.e. from Sentinel-1) leveraging various machine learning approaches for a limited number of crop types. However, these efforts normally train on individual pixels rather than leveraging information at a field level. </p><p>Here we present Beyond Cloud, a product which is the first to leverage computer vision and machine learning approaches in order to provide fused optic and SAR based crop health information. Field level learning is especially well-suited for inherently noisy SAR datasets. Several use cases are presented over agriculture fields located throughout the United Kingdom, France and Belgium, where cloud cover limits optic based solutions to as little as 2-3 images per growing season. Preliminary efforts for additional features to the product including automated crop and soil type detection are also discussed. Beyond Cloud can be accessed via a simple API which makes integration of the results easy for existing dashboards and smart-ag tools. Overall, these efforts promote the accessibility of satellite imagery for real agriculture end users.</p><p> </p>

scholarly journals 1078 Machine Learning to Predict PAP Adherence and Compliance in Tele-Health Management

SLEEP ◽  
2018 ◽  
Vol 41 (suppl_1) ◽  
pp. A400-A401 ◽  
Author(s):  
M Araujo ◽  
L Kazaglis ◽  
R Bhojwani ◽  
C Iber ◽  
S Khadanga ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Management

scholarly journals Adoption of e-commerce by the agri-food sector in China: the case of Minyu e-commerce company

2020 ◽  
Vol 23 (1) ◽  
pp. 157-171
Author(s):  
Xiaoshan Yang ◽  
Xiaowei Chen ◽  
Yan Jiang ◽  
Fu Jia
Keyword(s):  
Developing Countries ◽  
Business Model ◽  
Agricultural Products ◽  
Third Party ◽  
Food Sector ◽  
Postgraduate Students ◽  
Trading Activities ◽  
Agricultural Business ◽  
Online Shops ◽  
Gain Access

In recent years, increasing numbers of smallholders in developing countries such as China have begun to sell agricultural products directly to consumers via online shops using a third-party trade platform. It is increasingly clear that e-commerce has become a new and effective way to help smallholders gain access to the market. The investigation of agricultural e-commerce practices has a significant role in helping to understand the development of the agri-food sector in China. This teaching case provides an example of adopting e-commerce in the interaction and trading activities between participants in the food sector through a typical agricultural products e-commerce company in China, Minyu E-commerce. Particularly, the case analyzes the business model evolution through the ecosystem life cycle at the company. This case can be used to teach graduate/postgraduate students in agricultural business, MBA and executive programmes about the agri-food e-commerce business model.

scholarly journals Review and prospection for food and agricultural business model innovations in emerging economies

2020 ◽  
Vol 23 (5) ◽  
pp. 661-666
Author(s):  
Fu-Sheng Tsai ◽  
Cheng-Hung Tsai ◽  
Chi-Wei Liu ◽  
Chia-Hsun Lin ◽  
Chih-Hsiang Chang
Keyword(s):  
Sustainable Development ◽  
Business Model ◽  
Emerging Economies ◽  
Business Models ◽  
Special Issue ◽  
Research Practices ◽  
Food And Agriculture ◽  
Agricultural Business ◽  
Interactive Dynamic

Even for industries that are traditionally being perceived as ‘traditional,’ such as the food and agriculture ones, business models and its innovations are critical for the industries’ sustainable development. Nine interesting articles in this special issue are reviewed with sincere prospections that might push the research and practical frontiers further. Suggestions in cross-level investigations, international and diverse contexts and research practices, as well as the interactive, dynamic, and evolutionary intersections between the technological and managerial sub-systems of food and agribusiness model innovations are discussed.

Investigation of Influential Factors of Predicting Individuals' Use and Non-use of Fitness and Diet Apps on Smartphones: Application of the Machine Learning Algorithm (XGBoost)

2021 ◽  
Vol 45 (1) ◽  
pp. 111-124
Author(s):  
Jaehee Cho ◽  
Sehwan Kim ◽  
Gwangjin Jeong ◽  
Chonghye Kim ◽  
Ja-Kyoung Seo
Keyword(s):  
Machine Learning ◽  
Social Support ◽  
Social Influence ◽  
Health Management ◽  
Learning Algorithm ◽  
Media Use ◽  
Influential Factors ◽  
Machine Learning Algorithm ◽  
Accuracy Score ◽  
Diverse Groups

Objectives: In this study, we aimed to find the influential factors in determining individuals' use and non-use of fitness and diet apps on smartphones. To this end, we focused on diverse groups of predictors that would significantly affect people's use and non-use of these apps. Methods: Overall, we considered 105 factors as potential predictors and included them in further analyses using a machine learning algorithm, XGBoost. The main reason for selecting this particular algorithm was that it had been known as one of the most accurate and popular algorithms for predicting consumer behaviors. Results: We found the accuracy score of those factors for predicting people's use and non-use of fitness and diet apps was approximately 71.3%. In particular, the most influential predictors were mainly related to social influence, media use, overeating, social support, health management, and attitudes toward exercise. Conclusion: These findings contribute to helping scholars and practitioners to develop more practical strategies of the implementation of fitness and diet apps.

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