Full Description
This book constitutes revised selected papers from the First International Conference on Agriculture-Centric Computation, ICA 2023, held in Chandigarh, India, in May 2023.
The 18 papers were thoroughly reviewed and selected from the 52 submissions. They examine how computing disciplines such as big data analytics, artificial intelligence, machine learning, the Internet of Things (IoT), remote sensing, robotics, and drones can be applied to agriculture to address some of the biggest challenges facing the industry today, including climate change, food security, and environmental sustainability.
Contents
Fine Tuned Single Shot Detector for Finding Disease Patches in Leaves.- Empirical Analysis and Evaluation of Factors Influencing Adoption of AI-based Automation Solutions for Sustainable Agriculture.- FusedNet Model for Varietal Classification of Rice Seeds.- Fertilizer Recommendation using Ensemble Filter-based Feature Selection Approach.- Privacy-Preserving Pest Detection Using Personalized Federated Learning.- A review on applications of artificial intelligence for identifying soil nutrients.- IRPD: In-Field Radish Plant Dataset.- Fast Rotated Bounding Box Annotations for Object Detection.- IndianPotatoWeeds: An Image Dataset of Potato Crop to Address Weed Issues in Precision Agriculture.- Estimation Of Leaf Parameters in Punjab Region Through Multi-Spectral Drone Images using Deep Learning Models.- Application of near-infrared (NIR) hyperspectral imaging system for protein content prediction in chickpea flour.- Classification of crops based on band quality and redundancy from hyperspectral image.- Automated Agriculture News Collection, Analysis, and Recommendation.- Intelligent Chatbot Assistant in Agriculture Domain.- Machine Learning Methods for Crop Yield Prediction.- Real-time Plant Disease Detection: A Comparative Study.- Fruit Segregation using Deep Learning.- Investigation of the bulk and electronic properties of boron/nitrogen/indium doped armchair graphene nanoribbon for sensing plant VOC: A DFT study.
