Automatic Crop Recommendation System Using LightGBM and Decision Tree Machine Learning Models DOI

Ravi Kumar Banoth,

R. V.

Journal of Machine and Computing, Journal Year: 2025, Volume and Issue: unknown, P. 343 - 355

Published: Jan. 3, 2025

An Automatic Crop Recommendation System is a system that makes use of data analysis and algorithms to recommend crops are suitable proper about soil quality, climate, local factors. Such eases the decision-making process for farmers. The necessity efficient agricultural techniques growing rapidly, it impossible without application modern technology would promote quality ideal crop selection list production. This paper introduces new concept System, integrating LightGBM Decision Tree algorithms. research uses strengths LightGBM, type gradient boosting framework, Tree, conventional machine learning model, form powerful mixed ensemble approach. These approaches combined exploit their complementary strengths, leading more accurate dependable advisory system. effectiveness proposed algorithm’s approach verified through experimental results has following accuracies, recalls, F-1 scores. proven very successful; an accuracy 98.64% possible appropriate crops.

Language: Английский

Towards sustainable agriculture: Harnessing AI for global food security DOI Creative Commons
Dhananjay K. Pandey, Richa Mishra

Artificial Intelligence in Agriculture, Journal Year: 2024, Volume and Issue: 12, P. 72 - 84

Published: April 30, 2024

The issue of food security continues to be a prominent global concern, affecting significant number individuals who experience the adverse effects hunger and malnutrition. finding solution this intricate necessitates implementation novel paradigm-shifting methodologies in agriculture sector. In recent times, domain artificial intelligence (AI) has emerged as potent tool capable instigating profound influence on sectors. AI technologies provide advantages by optimizing crop cultivation practices, enabling use predictive modelling precision techniques, aiding efficient monitoring disease identification. Additionally, potential optimize supply chain operations, storage management, transportation systems, quality assurance processes. It also tackles problem loss waste through post-harvest reduction, analytics, smart inventory management. This study highlights that how utilizing power AI, we could transform way produce, distribute, manage food, ultimately creating more secure sustainable future for all.

Language: Английский

Citations

42
Enhancing precision agriculture: A comprehensive review of machine learning and AI vision applications in all-terrain vehicle for farm automation DOI Creative Commons
Mrutyunjay Padhiary,

Debapam Saha,

Raushan Kumar

et al.

Smart Agricultural Technology, Journal Year: 2024, Volume and Issue: 8, P. 100483 - 100483

Published: June 4, 2024

The automation of all-terrain vehicles (ATVs) through the integration advanced technologies such as machine learning (ML) and artificial intelligence (AI) vision has significantly changed precision agriculture. This paper aims to analyse develop trends provide comprehensive knowledge current state ATV-based agriculture future possibilities ML AI. A bibliometric analysis was conducted network diagram with keywords taken from previous publications in domain. review comprehensively analyses potential transforming farming operations tasks deployment vehicles. research extensively how methods have influenced several aspects agricultural activities, planting, harvesting, spraying, weeding, crop monitoring, others. AI systems are being researched for their ability enhance precise prompt decision-making ATV-driven automation. These been thoroughly tested show they can improve yield, reducing overall investment, make more efficient. Examples include learning-based seeding accuracy, AI-enabled health use accurate pesticide application. assessment examines challenges data privacy problems scalability constraints, along advancements prospects field. will assist researchers practitioners making well-informed judgments regarding practices that efficient, sustainable, technologically robust.

Language: Английский

Citations

40
Generative artificial intelligence of things systems, multisensory immersive extended reality technologies, and algorithmic big data simulation and modelling tools in digital twin industrial metaverse DOI Creative Commons
Tomáš Klieštik, Pavol Kráľ, Martin Bugaj

et al.

Equilibrium Quarterly Journal of Economics and Economic Policy, Journal Year: 2024, Volume and Issue: 19(2), P. 429 - 461

Published: June 30, 2024

Research background: Multi-modal synthetic data fusion and analysis, simulation modelling technologies, virtual environmental location sensors shape the industrial metaverse. Visual digital twins, smart manufacturing sensory mining techniques, 3D twin predictive maintenance tools, big mobile analytics, cloud-connected spatial computing devices further immersive spaces, decentralized worlds, reality Purpose of article: We aim to show that extended cognitive systems, computer vision-based production neuro-engineering interoperability improve artificial intelligence-based metaverse hyper-immersive simulated environments. Geolocation tracking image processing computational robot motion algorithms, technologies economic business management environments Methods: Quality tools: AMSTAR, BIBOT, CASP, Catchii, R package Shiny app citationchaser, DistillerSR, JBI SUMARI, Litstream, Nested Knowledge, Rayyan, Systematic Review Accelerator. Search period: April 2024. terms: “digital metaverse” + “artificial Intelligence Things systems”, “multisensory technologies”, “algorithmic tools”. Selected sources: 114 out 336. Published research inspected: 2022–2024. PRISMA was reporting quality assessment tool. Dimensions VOSviewer were deployed as visualization tools. Findings & value added: Simulated augmented multi-sensory explainable decision support cloud-based robotic cooperation ambient intelligence deep learning-based analytics tools are instrumental in The necessitates connected enterprise reality-embedded twins.

Language: Английский

Citations

30
Integrated Pest Management: An Update on the Sustainability Approach to Crop Protection DOI Creative Commons
Wentao Zhou, Yashwanth Arcot,

Raul F. Medina

et al.

ACS Omega, Journal Year: 2024, Volume and Issue: 9(40), P. 41130 - 41147

Published: Sept. 28, 2024

Integrated Pest Management (IPM) emerged as a pest control framework promoting sustainable intensification of agriculture, by adopting combined strategy to reduce reliance on chemical pesticides while improving crop productivity and ecosystem health. This critical review synthesizes the most recent advances in IPM research practice, mostly focusing studies published within past five years. The Review discusses key components IPM, including cultural practices, biological control, genetic targeted pesticide application, with particular emphasis significant advancements made delivery systems. Recent findings highlight growing importance conservation which involves management agricultural landscapes promote natural enemy populations. Furthermore, discovery novel biopesticides, microbial agents plant-derived compounds, has expanded arsenal tools available for eco-friendly management. Substantial progress recently also been development systems, such nanoemulsions controlled-release formulations, can minimize environmental impact maintaining their efficacy. analyzes environmental, economic, social dimensions adoption, showcasing its potential biodiversity ensure food safety. Case from various agroecological contexts demonstrate successful implementation programs, highlighting participatory approaches effective knowledge exchange among stakeholders. identifies main challenges opportunities widespread adoption need transdisciplinary research, capacity building, policy support. In conclusion, this essential role achieving it seeks optimize production minimizing adverse impacts enhancing resilience systems global climate change loss.

Language: Английский

Citations

26
Integration of Technology in Agricultural Practices towards Agricultural Sustainability: A Case Study of Greece DOI Open Access
Dimitrios Kalfas, Stavros Kalogiannidis, Olympia Papaevangelou

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(7), P. 2664 - 2664

Published: March 24, 2024

Agricultural technology integration has become a key strategy for attaining agricultural sustainability. This study examined the of in practices towards sustainability, using Greece as case study. Data were collected questionnaire from 240 farmers and agriculturalists Greece. The results showed significant positive effect on with p-values indicating strong statistical relevance (types used: p = 0.003; factors influencing adoption: 0.001; benefits integration: 0.021). These highlight effects that cutting-edge like artificial intelligence, Internet Things (IoT), precision agriculture have improving resource efficiency, lowering environmental effects, raising yields. Our findings cast doubt conventional dependence intensive, resource-depleting farming techniques point to move toward more technologically advanced, sustainable approaches. research advances conversation by showcasing how well may improve sustainability Greek agriculture. emphasizes significance infrastructure investment, supporting legislation, farmer education order facilitate adoption technology.

Language: Английский

Citations

20
Integrating digital technologies in agriculture for climate change adaptation and mitigation: State of the art and future perspectives DOI
Carlos Parra-López, Saker Ben Abdallah, Guillermo Garcia‐Garcia

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 226, P. 109412 - 109412

Published: Sept. 7, 2024

Language: Английский

Citations

19
AI and Related Technologies in the Fields of Smart Agriculture: A Review DOI Creative Commons

Fotis Assimakopoulos,

Costas Vassilakis, Dionisis Margaris

et al.

Information, Journal Year: 2025, Volume and Issue: 16(2), P. 100 - 100

Published: Feb. 2, 2025

The integration of cutting-edge technologies—such as the Internet Things (IoT), artificial intelligence (AI), machine learning (ML), and various emerging technologies—is revolutionizing agricultural practices, enhancing productivity, sustainability, efficiency. objective this study is to review literature regarding development evolution AI well other technologies in fields Agriculture they are developed transformed by integrating above technologies. areas examined open field smart farming, vertical indoor zero waste agriculture, precision livestock greenhouses, regenerative agriculture. This paper links current research, technological innovations, case studies present a comprehensive these being context for benefit farmers consumers general. By exploring practical applications future perspectives, work aims provide valuable insights address global food security challenges, minimize environmental impacts, support sustainable goals through application new

Language: Английский

Citations

3
A comprehensive analysis of the advances in Indian Digital Agricultural architecture DOI Creative Commons
Acharya Balkrishna, Rakshit Pathak, Sandeep Kumar

et al.

Smart Agricultural Technology, Journal Year: 2023, Volume and Issue: 5, P. 100318 - 100318

Published: Sept. 7, 2023

ICT-based interventions such as smart farming and precision agriculture are helping to improve the output of traditional agricultural systems drive them toward sustainability. Data-driven technologies like remote sensing, sensors, IoT-based devices constructed over AI/ML algorithms have become a fundamental aspect that assists farmers with critical decision-making. This revolution is strengthening in terms farm management by improving crop yield, pest control, soil health, etc. real-time. We thoroughly reviewed digital adoption insights into Indian sector presented comprehensive account major ICT initiatives undertaken followed redundancy analysis well its influence on sector. Unfortunately, while being significant agrarian country, India's solutions still infancy, apparent from close look at important FMIS key components recognized used internationally. found 28 active globally, produced list 29 local (Indian) applications spread across 23 different sub-domains. Sadly, majority among these were not unique replicated similar features, besides just few be crop-specific applications. The article approach presenting tale penetration will helpful further Agri-stack vision India.

Language: Английский

Citations

26
Coconut (Cocos nucifera) tree disease dataset: A dataset for disease detection and classification for machine learning applications DOI Creative Commons
Sandip Thite, Yogesh Suryawanshi, Kailas Patil

et al.

Data in Brief, Journal Year: 2023, Volume and Issue: 51, P. 109690 - 109690

Published: Oct. 15, 2023

The ``Coconut (

Language: Английский

Citations

23
Privacy-Centric AI and IoT Solutions for Smart Rural Farm Monitoring and Control DOI Creative Commons
Mosiur Rahaman, Chun‐Yuan Lin, Princy Pappachan

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(13), P. 4157 - 4157

Published: June 26, 2024

The integration of artificial intelligence (AI) and the Internet Things (IoT) in agriculture has significantly transformed rural farming. However, adoption these technologies also introduced privacy security concerns, particularly unauthorized breaches cyber-attacks on data collected from IoT devices sensitive information. present study addresses concerns by developing a comprehensive framework that provides practical, privacy-centric AI solutions for monitoring smart farms. This is performed designing includes three-phase protocol secures exchange between User, Sensor Layer, Central Server. In proposed protocol, Server responsible establishing secure communication channel verifying legitimacy User securing using rigorous cryptographic techniques. validated Automated Validation Security Protocols Applications (AVISPA) tool. formal analysis confirms robustness its suitability real-time applications IoT-enabled farms, demonstrating resistance against various attacks enhanced performance metrics, including computation time 0.04 s 11 messages detailed search where 119 nodes were visited at depth 12 plies mere 0.28 s.

Language: Английский

Citations

14