Nyhet

Agricultural-Centric Computation

Third International Conference, ICA 2025, Guwahati, India, May 13–16, 2025, Revised Selected Papers

AvHanumant Singh Shekhawat,Mukesh Kumar Saini

Häftad, Engelska, 2026

Del i serien Communications in Computer and Information Science

850 kr

Beställningsvara. Skickas inom 11-20 vardagar. Fri frakt över 249 kr.

Beskrivning

This book constitutes the proceedings of the Third International Conference on Agricultural-Centric Computation, ICA 2025, held in Guwahati, India, during May 13–16, 2025.The 25 full papers and 3 short papers included in this volume were carefully reviewed and selected from 115 submissions. They were organized under the following thematic areas: AI & Machine Learning in Agriculture, IoT and Sensor Networks for Smart Farming, Robotics and Automation, Climate-Resilient and Sustainable Technologies, and Precision Agriculture and Data Analytics.

Produktinformation

Utgivningsdatum:2026-04-17
Mått:155 x 235 x undefined mm
Format:Häftad
Språk:Engelska
Serie:Communications in Computer and Information Science
Antal sidor:332
Förlag:Springer Nature Switzerland AG
ISBN:9783032170828

Utforska kategorier

Agronomi inom Naturvetenskap och teknik
Artificiell intelligens inom Data och IT

Innehållsförteckning

Assessing Multi-Mode Temporal PolSAR Data for Winter Wheat and Barley Discrimination Using Convolutional Neural Networks.- Early Disease Detection in Pearl Millet Using YOLO v11 Model for Improved Agricultural Monitoring.- AgriFed: A Federated Learning Approach for Scalable and Secure Crop Disease Detection.- Remote Sensing and Environmental parameter analysis of Rajpura, Punjab using GIS for Precision farming application.- Optimal Hyperspectral Band selection using Amended Lyrebird optimization algorithm.- Optimally Designed High-Efficiency Drone-Based Spray System for Precision Nutrient Delivery in Agricultural Farms.- Assessing applicability and adoption of agriculture-centric computation technologies in Punjab: A multi-district farmer survey.- A Model Based Comparative Study for Conventional and Drone Based Farming: Case Studies from India.- Enhancing Large Language Model Performance for Agricultural Domain Translation via Specialised Dictionaries and Embeddings.- A Detailed Perspective on Vertical Farming – An Innovative Agricultural Trend with AI/ML and Smart Robotics.- LeADS (Leaf Anomaly Detection System):Deep Learning Pipeline for Leaf Stress, Disease & Severity Estimation.- Enhancing Agricultural Intelligence: A Comparison of AI Models for Plant Disease Categorisation and Crop Yield Prediction.- Integration of Mechanical Systems and IoT for Smart and Climate-Resilient Irrigation in Agriculture.- Deep Learning-Based Framework for Early Detection and Classification of Mango Crop Diseases.- Residual Biomass Utilization: A Review on Newly Emerging Techniques for Mitigating Climate Change.- NIR spectroscopy based non-invasive assessment of tea quality.- Comparative Analysis of DSSAT and APSIM Crop Models for Wheat Yield Prediction in Chhattisgarh State of India.- An IoT Smart Irrigation System using Raspberry Pi and ThingSpeak: Design, Implementation, and Validation.- Quantum Reinforcement Learning Framework for Agricultural Seed Treatment Optimization and Yield Prediction.- Multimodal Fusion for Cow Behavior Prediction.- Real-Time DEM-Based Terrain and Step Farming Suitability Analysis via Geospatial Processing in VR.- Swarm Robotics for Agricultural Drones: A Transformative Approach to Smart Farming.- Sodium Ion Detection using Polymer-Based Sensor for Monitoring Soil Health in Agriculture.- LEAFNET Model for Early Crop Disease Detection: Integrating CNNs and Vision Approach in Precision Agriculture.- Energy Consumption Analysis for a Robotic Arm in Vertical Farming Process: An Experimental Validation.- AgriRover: A GPS-Guided Smart Rover for Environmental Monitoring.- Optimized Conv-LSTM Model for Analyzing Negative Affective State Vocalization in Dairy Cattle for an Edge Device.- Drone RCS Statistical Behaviour and its Implications for Agriculture Drones Air Traffic Management.