Application of LiDAR Sensors for Crop and Working Environment Recognition in Agriculture: A Review DOI Creative Commons
Md Rejaul Karim, Md Nasim Reza, Hongbin Jin

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(24), P. 4623 - 4623

Published: Dec. 10, 2024

LiDAR sensors have great potential for enabling crop recognition (e.g., plant height, canopy area, spacing, and intra-row spacing measurements) the of agricultural working environments field boundaries, ridges, obstacles) using machinery. The objective this study was to review use in crops environments. This also highlights sensor testing procedures, focusing on critical parameters, industry standards, accuracy benchmarks; it evaluates specifications various commercially available with applications feature characterization importance mounting technology machinery effective Different studies shown promising results an airborne LiDAR, such as coefficient determination (R2) root-mean-square error (RMSE) values 0.97 0.05 m wheat, 0.88 5.2 cm sugar beet, 0.50 12 potato height estimation, respectively. A relative 11.83% observed between manual measurements, highest distribution correlation at 0.675 average 5.14% during soybean estimation LiDAR. An object detection 100% found identification three scanning methods: center cluster, lowest point, stem–ground intersection. effectively detect obstacles, which is necessary precision agriculture autonomous navigation. Future directions emphasize need continuous advancements technology, along integration complementary systems algorithms, machine learning, improve performance applications. strategic framework implementing includes recommendations precise testing, solutions current limitations, guidance integrating other technologies enhance digital agriculture.

Language: Английский

3D printing applications in smart farming and food processing DOI Creative Commons
Mrutyunjay Padhiary,

Javed Akhtar Barbhuiya,

Dipak Roy

et al.

Smart Agricultural Technology, Journal Year: 2024, Volume and Issue: 9, P. 100553 - 100553

Published: Aug. 28, 2024

Language: Английский

Citations

24
Status of Farm Automation, Advances, Trends, and Scope in India DOI Open Access
Mrutyunjay Padhiary

International Journal of Science and Research (IJSR), Journal Year: 2024, Volume and Issue: 13(7), P. 737 - 745

Published: July 5, 2024

Farm automation, in particular, has brought about tremendous changes agriculture as a result of the rapid growth technology.This study investigates farm automation's current state, recent developments, new trends, and potential applications India. The illustrates effects automation on agricultural productivity sustainability by looking at integration modern technologies like robotics, precision farming, Internet Things. A consideration governmental regulations, technical advancements, contribution academic institutions to movement are all included study. findings indicate that India increased crop yields 25-40% levels lower northeastern areas greater northwestern regions. also identifies important shift toward environmentally friendly solutions expanding significance digital technology agriculture. Although great potential, discusses technological, societal, economic obstacles prevent it from being widely used. Lastly, offers strategic insights policy recommendations accelerate adoption, with ultimate goal supporting India's industry.

Language: Английский

Citations

9
Real-Time Data Processing in Agricultural Robotics DOI

Azmirul Hoque,

Mrutyunjay Padhiary, G. Krishna Prasad

et al.

Advances in environmental engineering and green technologies book series, Journal Year: 2025, Volume and Issue: unknown, P. 431 - 468

Published: Jan. 3, 2025

This chapter emphasizes the integration of IoT and computer vision technology improving precision farming also highlights crucial role that real-time data processing plays in farm robots. According to research studies, enhances efficiency operations. The spraying can be even more accurate by up 20% operating costs reduced 12%. In addition discussing topics like accuracy cybersecurity, this still addressed benefits for crop monitoring autonomous form instantaneous feedback. further explains some future areas under AI, climate-smart behaviors, emergent technology. Some takeaway points are there is so much potential greatly increase agricultural output sustainability through these advancements. Apart from that, it includes requirements continuous innovation adaptations technologies ensure they meet today's agriculture needs.

Language: Английский

Citations

1
A Review on Advancing Agricultural Efficiency through Geographic Information Systems, Remote Sensing, and Automated Systems DOI Creative Commons
Mrutyunjay Padhiary, Payaswini Saikia, Pankaj Roy

et al.

Cureus Journal of Engineering., Journal Year: 2025, Volume and Issue: unknown

Published: March 6, 2025

Language: Английский

Citations

1
Collaborative Marketing Strategies in Agriculture for Global Reach and Local Impact DOI
Mrutyunjay Padhiary,

Prodipto Roy

Advances in marketing, customer relationship management, and e-services book series, Journal Year: 2024, Volume and Issue: unknown, P. 219 - 252

Published: Oct. 25, 2024

This chapter discusses the importance of cooperative marketing strategies in agriculture, focusing particularly on value embracing diverse viewpoints and harnessing global opportunities for local farms. study addresses relationships between collaboration, diversity, globalization agricultural marketing, with a focus how these might enhance market entrance, encourage inclusivity, promote sustainable development. Various case studies current practice collaborative agriculture industry are discussed greater depth. The also gives much to teamwork advantages, opportunity identification, possible obstacles, valuable advisory improve understanding potential effects farming communities, economic advancement, food systems. findings this may better engagement among stakeholders inclusive

Language: Английский

Citations

5
Post-Harvest Technologies and Automation: Al-Driven Innovations in Food Processing and Supply Chains DOI Open Access
Biswa Ranjan Das,

Azmirul Hoque,

Subhra Saikat Roy

et al.

International Journal of Scientific Research in Science and Technology, Journal Year: 2025, Volume and Issue: 12(1), P. 183 - 205

Published: Jan. 26, 2025

The rapid advancements in artificial intelligence (AI) and automation are transforming post-harvest technologies, offering innovative solutions to improve food quality, safety, supply chain efficiency. This paper reviews the role of AI-driven innovations processing logistics, with a focus on automation, predictive analytics, quality control. AI such as machine learning, computer vision, IoT integration, optimizing processes like sorting, grading, packaging, microbial detection, reducing waste extending shelf life. Moreover, AI-powered robotics smart warehouses streamlining transportation inventory management, enhancing operational integration demand forecasting optimization is further improving traceability, minimizing disruptions, environmental impact. Despite promising potential, challenges data system cost barriers, regulatory concerns remain. future technologies presents opportunities for continued innovation, deep IoT, global scalability, pathways sustainable systems. concludes by discussing impact sector its potential drive more efficient, resilient, chains worldwide.

Language: Английский

Citations

0
Precision Agriculture and AI-Driven Resource Optimization for Sustainable Land and Resource Management DOI
Mrutyunjay Padhiary,

Azmirul Hoque,

G. Krishna Prasad

et al.

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 197 - 232

Published: Feb. 18, 2025

Amidst the escalating global challenges of climate change, limited resources, and population growth, adoption sustainable land resource management has become imperative to ensure food security environmental conservation. Precision agriculture enhances process efficiency, reduces impact, improves agricultural productivity through integration artificial intelligence technologies, including machine learning, deep computer vision. Key findings indicate a reduction 10–20% in input costs an increase 15–25% crop yields efficient utilisation. Furthermore, precision irrigation systems can achieve water savings up 50%, while targeted pesticide treatments reduce chemical usage by 30–40%. This chapter examines economic benefits, highlighting 20% CO2 emissions. Recent advancements underscore potential AI foster agriculture, promoting conservation viability.

Language: Английский

Citations

0
Simulation Software in the Design and AI-Driven Automation of All-Terrain Farm Vehicles and Implements for Precision Agriculture DOI Creative Commons
Mrutyunjay Padhiary,

Prodipto Roy,

Kundan Kumar

et al.

Published: April 1, 2025

Precision agriculture depends on the automation and mechanization of agricultural equipment vehicles in a variety terrains, which increases productivity sustainability. This review presents comparative analysis significant simulation software used designing developing automated systems, emphasizing their methodologies significance advancing farm technology. Artificial intelligence (AI) machine learning (ML) methods are modeled, optimized, integrated using key technologies such as MATLAB/Simulink, SolidWorks, ANSYS, AirSim, Gazebo. The results demonstrate how these improve automation's real-time decision-making, predictive maintenance, system accuracy. Case studies illustrate practical application simulating all-terrain specialized implements. best tools for autonomous navigation AirSim Gazebo, although MATLAB/Simulink is particularly adept at system-level AI modeling. study takes new approach to improving design, control, environmental interactions by combining many modeling tools. makes it easier make systems that last longer work better. It suggested future investigate relationship between automation, AI, greater detail propel precision forward.

Language: Английский

Citations

0
Machine Learning for Precision Agriculture and Crop Yield Optimization DOI

Prodipto Roy,

Mrutyunjay Padhiary,

Azmirul Hoque

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 189 - 234

Published: March 28, 2025

The swift advancement of machine learning (ML) has altered several industries, including agriculture, by providing innovative ways addressing complex challenges related to modern farming. This chapter discusses the use ML in precision emphasizing its capacity maximize crop output and improve agricultural practices. It studies supervised, unsupervised, reinforcement, deep methodologies evaluate extensive datasets derived from remote sensing technologies, soil sensors, climate data, equipment. Principal applications include predictive modeling for yield estimation, pest disease identification, health assessment, irrigation optimization, fertilization. also examines problems limits implementation data quality farmer acceptance.

Language: Английский

Citations

0
Field to Cloud DOI
Mrutyunjay Padhiary

Advances in systems analysis, software engineering, and high performance computing book series, Journal Year: 2025, Volume and Issue: unknown, P. 391 - 430

Published: April 30, 2025

The integration of Internet Things (IoT) into the agricultural sector exhibits potential for modernising conventional farming and tackling various obstacles faced by community. This chapter focuses on transformative capacity IoT in sector, stressing significant insights discoveries obtained from several case studies. enables farmers to empower efficient operations risk reduction monitoring analysing data real-time. Real world applications show IoT's precise agriculture solutions, which contribute sustainable farm mechanisation, enhanced livestock management, food safety, blockchain technology, supply chain visibility. Collaboration among stakeholders, giving priorities research innovation, encouraging technology adoption are essential progress. By adopting incorporating inputs, industry can initiate an approach towards automation, thereby guaranteeing a prosperous future communities global scale.

Language: Английский

Citations

0