IoT-Enabled Machine Learning-Based Smart and Sustainable Agriculture DOI
Vivek Patel, Swati Gautam, Vijayshri Chaurasia

et al.

Advances in environmental engineering and green technologies book series, Journal Year: 2024, Volume and Issue: unknown, P. 176 - 200

Published: May 6, 2024

In this chapter, an elaborated description of machine learning (ML)-based IoT system for smart and sustainable agriculture in modern perspective is presented. Idea future to advanced ML-IoT development emphasized, a CNN LightGBM-based crop recommendation suggested. Internet things (IoT) emerging technology dedicated platform connect the remote systems each other. Recently, widely adopted environmental data acquisition. The sensors collected from devices analyzed using ML techniques detection further action taken improvement farming. solution assists farmers deciding which state be as per analysis sensor such temperature, light intensity, humidity, ultraviolet range, soil moisture boost goals. A comprehensive discussion given present situation, applications, opportunities study, constraints, issues.

Language: Английский

Rapid estimation of soil Mn content by machine learning and soil spectra in large-scale DOI Creative Commons
Min Zhou, Tao Hu, Mengting Wu

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 81, P. 102615 - 102615

Published: April 28, 2024

Language: Английский

Citations

18
Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction DOI Creative Commons

Nguyen Van Thieu,

Ngoc Hung Nguyen, Mohsen Sherif

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: June 12, 2024

Accurate river streamflow prediction is pivotal for effective resource planning and flood risk management. Traditional forecasting models encounter challenges such as nonlinearity, stochastic behavior, convergence reliability. To overcome these, we introduce novel hybrid that combine extreme learning machines (ELM) with cutting-edge mathematical inspired metaheuristic optimization algorithms, including Pareto-like sequential sampling (PSS), weighted mean of vectors (INFO), the Runge-Kutta optimizer (RUN). Our comparative assessment includes 20 across eight categories, using data from Aswan High Dam on Nile River. findings highlight superior performance mathematically based models, which demonstrate enhanced predictive accuracy, robust convergence, sustained stability. Specifically, PSS-ELM model achieves a root square error 2.0667, Pearson's correlation index (R) 0.9374, Nash-Sutcliffe efficiency (NSE) 0.8642. Additionally, INFO-ELM RUN-ELM exhibit absolute percentage errors 15.21% 15.28% respectively, 1.2145 1.2105, high Kling-Gupta efficiencies values 0.9113 0.9124, respectively. These suggest adoption our proposed significantly enhances water management strategies reduces any risks.

Language: Английский

Citations

7
Research on Atlantic surface pCO2 reconstruction based on machine learning DOI Creative Commons
Jiaming Liu, Jie Wang,

Xun Wang

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103094 - 103094

Published: March 1, 2025

Language: Английский

Citations

0
Trajectory-based fish event classification through pre-training with diffusion models DOI Creative Commons
Noemi Canovi,

Benjamin A. Ellis,

Tonje Knutsen Sørdalen

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 82, P. 102733 - 102733

Published: July 28, 2024

This study contributes to advancing the field of automatic fish event recognition in natural underwater videos, addressing current gap studying interaction and competition, including predator-prey relationships mating behaviors. We used corkwing wrasse (Symphodus melops) as a model, marine species commercial importance that reproduces sea-weed nests built cared for by single male. These attract wide range visitors are focal point behavior such spawning, chasing, maintenance. propose deep learning methodology analyze movement trajectories nesting male classify associated events observed their habitat. Our approach leverages unsupervised pre-training based on diffusion models, leading improved feature learning. Additionally, we introduce dataset comprising 16,937 across 12 classes, making it largest terms class diversity. results demonstrate superior performance our method compared several architectures. The code proposed can be found at https://github.com/NoeCanovi/Fish_Behaviors_Generative_Models.

Language: Английский

Citations

2
A method for durian precise fertilization based on improved radial basis neural network algorithm DOI Creative Commons
Ruipeng Tang,

Wei Sun,

Jianxun Tang

et al.

Frontiers in Plant Science, Journal Year: 2024, Volume and Issue: 15

Published: June 5, 2024

Introduction Durian is one of the tropical fruits that requires soil nutrients in its cultivation. It important to understand relationship between content critical nutrients, such as nitrogen (N), phosphorus (P), and potassium (K) durian yield. How optimize fertilization plan also planting. Methods Thus, this study proposes an Improved Radial Basis Neural Network Algorithm (IM-RBNNA) precision fertilization. uses gray wolf algorithm weights thresholds RBNNA algorithm, which can improve prediction accuracy for nutrient with collects historical yield data build IM-RBNNA model compare other similar algorithms. Results The results show better than three algorithms average relative error, absolute coefficient determination predicted true values N, K, P fertilizer contents. predicts yield, closer value. Discussion shows accurately predict benefited farmers make agronomic plans management strategies. resources efficiently, reduces environmental negative impacts. ensures tree obtain appropriate amount maximize growth potential, reduce production costs, increase yields.

Language: Английский

Citations

1
A novel model for mapping soil organic matter: Integrating temporal and spatial characteristics DOI Creative Commons
Xinle Zhang, Guowei Zhang, Sheng‐Qi Zhang

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: unknown, P. 102923 - 102923

Published: Nov. 1, 2024

Language: Английский

Citations

1
IoT-Enabled Machine Learning-Based Smart and Sustainable Agriculture DOI
Vivek Patel, Swati Gautam, Vijayshri Chaurasia

et al.

Advances in environmental engineering and green technologies book series, Journal Year: 2024, Volume and Issue: unknown, P. 176 - 200

Published: May 6, 2024

In this chapter, an elaborated description of machine learning (ML)-based IoT system for smart and sustainable agriculture in modern perspective is presented. Idea future to advanced ML-IoT development emphasized, a CNN LightGBM-based crop recommendation suggested. Internet things (IoT) emerging technology dedicated platform connect the remote systems each other. Recently, widely adopted environmental data acquisition. The sensors collected from devices analyzed using ML techniques detection further action taken improvement farming. solution assists farmers deciding which state be as per analysis sensor such temperature, light intensity, humidity, ultraviolet range, soil moisture boost goals. A comprehensive discussion given present situation, applications, opportunities study, constraints, issues.

Language: Английский

Citations

0