Smart Logistic System for Enhancing the Farmer-Customer Corridor in Smart Agriculture Sector Using Artificial Intelligence

Ramírez-Asís, Edwin; Bhanot, Astha; Jagota, Vishal; Chandra, B.; Hossain, Shamim; Pant, Kumud; Almashaqbeh, Hashem Ali

doi:10.1155/2022/7486974

Cited by 9 publications

(6 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It also benefits from channel extension and innovative marketing models, particularly for agricultural products that emphasize freshness (Ma and Zhang, 2022). Simultaneously, it ensures the safety of digital transactions through real-time tracking and sharing mechanisms, thus ensuring strict control over the quality and safety of agricultural products (Zhu et al, 2021;Ramirez-Asis et al, 2022). 2.2.2 Cross-industry development: linkage and synergy effect.…”

Section: 2mentioning

confidence: 99%

“…Furthermore, smart agriculture employs digital intelligence algorithms to provide personalized recommendations for consumers (Huo, 2021). Moreover, the e-commerce model transforms traditional offline transactions into a digital ecosystem, increasing profits with higher turnover rates (Zhu et al ., 2021; Ramirez-Asis et al ., 2022). It also benefits from channel extension and innovative marketing models, particularly for agricultural products that emphasize freshness (Ma and Zhang, 2022).…”

Section: Theoretical Background and Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

New growth in cross-border E-business: evidence from gray forecasting to cross-border E-business in China

Huo,

Ouyang,

Tang

et al. 2024

JIDE

View full text Add to dashboard Cite

PurposeThis paper delves into cross-border E-business, unraveling its intricate dynamics and forecasting its future trajectory.Design/methodology/approachThis paper projects the prospective market size of cross-border E-business in China for the year 2023 using the GM (1,1) gray forecasting model. Furthermore, to enhance the analysis, the paper attempts to simulate and forecast the size of China’s cross-border E-business sector using the GM (1,3) gray model. This extended model considers not only the historical trends of cross-border E-business but also the growth patterns of GDP and the digital economy.FindingsThe forecast indicates a market size of 18,760 to 18,934 billion RMB in 2023, aligning with the consistent growth observed in previous years. This suggests a sustained positive trajectory for cross-border E-business.Originality/valueCross-border e-commerce critically shapes China’s global integration and traditional industry development. The research in this paper provides insights beyond statistical trends, contributing to a nuanced understanding of the pivotal role played by cross-border e-commerce in shaping China’s economic future.

show abstract

Section: 2mentioning

confidence: 99%

Section: Theoretical Background and Literature Reviewmentioning

confidence: 99%

New growth in cross-border E-business: evidence from gray forecasting to cross-border E-business in China

Huo,

Ouyang,

Tang

et al. 2024

JIDE

View full text Add to dashboard Cite

show abstract

“…Te modifcation moved manufacturers from conventional farming (CA) to precision farming (PA). PA is introduced to adapt the tractors and machines with GPS sensors for knowledge management [38][39][40]. In the process of crop irrigation for UAV based on least squares, regression's loss function is the MSE.…”

Section: Iot-assisted Smart Farming Framework (Iot-sff)mentioning

confidence: 99%

Unmanned Aerial Vehicle and Geospatial Analysis in Smart Irrigation and Crop Monitoring on IoT Platform

Zhao

Wang

Pham

2023

Mobile Information Systems

View full text Add to dashboard Cite

The geospatial analysis provides high potential for modeling, understanding, and visualizing artificial and natural ecosystems, utilizing big data analytics and the Internet of things as a pervasive sensing infrastructure. Precision agriculture, weed control, fertilizer distribution, and field management benefit from unmanned ariel vehicles (UAVs). Reduced production costs and improved crop quality are some of the benefits of using this method. Smart farming denotes geographical data utilization to identify field variability, guarantee optimal inputs, and enhance a farm’s output. Hence, in this paper, an IoT-assisted Smart Farming Framework (IoT-SFF) with big data analytics has been proposed using geospatial analysis. The use of wireless sensors in IoT devices and communication methods in agricultural applications is thoroughly examined. IoT sensors are available for particular agriculture applications, such as crop status, soil preparation, insect, pest detection, and irrigation scheduled. It is now possible to view our regions in various ways and make accurate agrotechnological decisions, thanks to a computer-generated geographic information system (GIS) for crop irrigation and monitoring. Analytical and monitoring processes that yield timely and accurate decision-making add value to big data, which is a key component for intelligently managing and operating farms. Still, it is constrained by both technical and socioeconomic variables. The simulation findings show that the proposed IoT-SFF model improves the crop yield ratio by 92.4%, prediction ratio by 97.7%, accuracy ratio by 94.5%, the average error by 38.3%, and low-cost rate by 34.4%.

show abstract

“…However, the unstable conditions within a greenhouse can negatively impact plant growth and ultimately reduce crop production. This issue can be mitigated using Artificial Intelligence (AI) tools that regulate the greenhouse variables [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Long Short-Term Memory Recurrent Neural Network and Extreme Gradient Boosting Algorithms Applied in a Greenhouse’s Internal Temperature Prediction

Esparza-Gómez,

Luque-Vega,

Guerrero-Osuna

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

One of the main challenges agricultural greenhouses face is accurately predicting environmental conditions to ensure optimal crop growth. However, the current prediction methods have limitations in handling large volumes of dynamic and nonlinear temporal data, which makes it difficult to make accurate early predictions. This paper aims to forecast a greenhouse’s internal temperature up to one hour in advance using supervised learning tools like Extreme Gradient Boosting (XGBoost) and Recurrent Neural Networks combined with Long-Short Term Memory (LSTM-RNN). The study uses the many-to-one configuration, with a sequence of three input elements and one output element. Significant improvements in the R2, RMSE, MAE, and MAPE metrics are observed by considering various combinations. In addition, Bayesian optimization is employed to find the best hyperparameters for each algorithm. The research uses a database of internal data such as temperature, humidity, and dew point and external data such as temperature, humidity, and solar radiation, splitting the data into the year’s four seasons and performing eight experiments according to the two algorithms and each season. The LSTM-RNN model produces the best results for the metrics in summer, achieving an R2 = 0.9994, RMSE = 0.2698, MAE = 0.1449, and MAPE = 0.0041, meeting the acceptability criterion of ±2 °C hysteresis.

show abstract

Smart Logistic System for Enhancing the Farmer-Customer Corridor in Smart Agriculture Sector Using Artificial Intelligence

Cited by 9 publications

References 31 publications

New growth in cross-border E-business: evidence from gray forecasting to cross-border E-business in China

New growth in cross-border E-business: evidence from gray forecasting to cross-border E-business in China

Unmanned Aerial Vehicle and Geospatial Analysis in Smart Irrigation and Crop Monitoring on IoT Platform

Long Short-Term Memory Recurrent Neural Network and Extreme Gradient Boosting Algorithms Applied in a Greenhouse’s Internal Temperature Prediction

Contact Info

Product

Resources

About