AIDA 2024

Second International Workshop

AIDA (AI-driven Agriculture):

Opportunities and Challenges

@ IEEE BigData 2024

December 15-18, 2024, Washington DC, USA 

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Combined workshops: AIPREF+AIDA+CH&BD online meeting link  

urly.it/312_yh

 

Meeting ID: 392 903 593 631

Passcode: ov6zC3Bk 

Please find the schedule in the bottom of the page

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DESCRIPTION

Agriculture is essential for every nation's economic sector and is surely one of the industries that involves more risks. In addition, worldwide the urbanization is continuing and the need for food is increasing as the global population is growing on a daily basis. In this scenario, the farmers' traditional techniques are unable to meet the demand and urgent is the needs of efficient novel automation/reasoning approaches and tools supporting agriculture. 

Big data (BD) and Artificial Intelligence (AI) sinergically support the agriculture domain. More specifically, AI uses a vast amount of data to learn from and enhance decision-making in the and Big data analytics uses AI for better data analysis. AI and BD have emerged as the most significant technological advancements and are revolutionizing the agriculture industry as they use processes and technologies to combine and analyse massive datasets to provide insights and useful patterns. The final goal is help farmers to optimize crop yields and reduce waste by analyzing data from sensors, drones, and other sources to identify patterns and make predictions: AI can be used to predict weather patterns, soil moisture levels, and pest infestations, and to optimize irrigation and fertilizer use.

Grand View Research valued the global AI in agriculture market size at USD 600.5 million in 2020 and it is expected to grow of 25.7% from 2021 to 2028. Precision agriculture, which uses AI and other technologies to optimize crop yields and reduce waste, is expected to be a key driver of the AI in agriculture market with a market size expected to reach USD 12.9 billion by 2027. AI and other digital technologies have the potential to transform smallholder agriculture by improving productivity, reducing costs, increasing resilience to climate change and ensuring compliance with food safety regulations.

AIDA provides an interdisciplinary venue for the community to promote collaborations and exchanges ideas, practices and advances specific to artificial intelligence in agriculture. The goal is to bring people in the field cross-cutting information management, artificial intelligence and agriculture informatics to discuss innovative data management and analytics technologies highlighting end-to-end applications, systems, and methods to address problems in agriculture. The workshop solicits empirical, experimental, methodological, and theoretical research reporting original and unpublished results on topics in the realm of AI in agriculture, food and bio-system engineering and related areas, such as the need for data standardization and privacy concerns along with applications to real situations. The conference will take place in online format. 

Workshop type: ONLINE 

TOPICS


PAPER SUBMISSION

The workshop invites full-length paper submissions that report ongoing or finished research (up to 10 pages), or short papers of early stage work (up to 6 pages). Papers should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines using Letter page format (8.5 x 11).

Template are avaiable at: IEEE conference template

All accepted papers will be included in the Workshop Proceedings published by the IEEE Computer Society Press, and made available at the Conference. Proceedings will be  included in the IEEE digital library indexed by Google Scholar and Scopus.


Authors can submit their papers at this link: Paper Submission

The workshop organizers are negotiating a special journal issue. Further details will be provided in the next future.

    

      IMPORTANT DATES


     PROGRAM CHAIRS


PROGRAM COMMITTEE MEMBERS 


      INFO

      eugenio.vocaturo@cnr.it

      e.zumpano@dimes.unical.it

      vijaypalsingh.dhaka@jaipur.manipal.edu

      geetachhikara@gmail.com


SCHEDULE


8:40:  Workshop Introduction

Session 1: AIPREF(AIPowered Renewable Energy Forecasting: Techniques and Challenges), Chair: Luciano Caroprese

08:45-9:00 

S30201,  Effective Net Load Forecasting in Solar-Integrated Homes with Battery Systems, Stefano Cabiddu, Manuela Sanguinetti, Alessandro De Falco, Giulia Manca, and Maurizio Atzori,

09:00-9:15 

S30202, Comparing Artificial  Intelligence Techniques for Predicting Energy Consumption and Renewable Energy Production, Behzad Pirouz and Francesca Guerriero,

9:15 – 9:30

S30203, On the use of Machine Learning to Discover Novel Donor-Acceptor Pairs For Organic Photovoltaic Devices, Khoukha KHOUSSA, Patrick LEVEQUE, and Larbi Boubchir,

9:30 – 9:45

S30204, DEVELOPMENT OF A MACHINE LEARNING ALGORITHM TO FORECAST PV PLANT PRODUCTION, Nicola Sorrentino, Daniele Menniti, Giovanni Brusco, and Giovanni Schinelli,

9:45 – 10:00

S30205, PRECEDE: Climate and Energy Forecasts to Support Energy Communities with Deep Learning models, Francesco Dattola, Pasquale Iaquinta, Miriam Iusi, Deborah Federico, Raffaele Greco, Marco Talerico, Valentina Coscarella, Luca Legato, Ivana Pellegrino, Sonia Bergamaschi, Mirko Orsini, Riccardo Martoglia, Andrea Livaldi, Abeer Jelali, Simone Sbregia, Tommaso Ruga, Ester Zumpano, Luciano Caroprese, Camilla Lops, Sergio Montelpare, Mariano Pierantozzi, and Maira Aracne


10:00-10:30, Coffee Break


 

Session 2: AIDA (AI-Driven Agriculture: opportunities and challenges), Chair: Eugenio Vocaturo

10:30 – 10:45

S32201, A study on phenotype prediction using an artificial intelligence-based data augmentation approach, Jiho Choi, Sung-Woo Byun, Najeong Chae, Ji Hoon Lim, Taehoon Lim, Hye In Lee, and Hwa Seon Shin,

10:45 – 11:00

S32202, Forest fire prevention: Application of mathematical models for the realization of an IoT based monitoring system., Carmelo Scuro, Giuseppe Alì, Pierpaolo Antonio Fusaro, and Salvatore Nisticò, 

11:00 – 11:15

S32203, Boosting Agricultural Diagnostics: Cassava Disease Detection with Transfer Learning and Explainable AI, Danilo Maurmo, Marco Gagliardi, Tommaso Ruga, Ester Zumpano, and Eugenio Vocaturo,

11:15 – 11:30

S32205, Towards Agent-based Disease Spread Modeling Combining Knowledge-driven Simulation and Machine Learning, Maurice Günder, Facundo Ramón Ispizua Yamati, Anne-Katrin Mahlein, and Christian Bauckhage,

11:15 – 11:30

S32206, An AI-Driven Architecture for Precision Agriculture: IoT, Machine Learning, and Digital Twin Integration for Sustainable Crop Protection, Gianni Costa, Agostino Forestiero, Riccardo Ortale, Antonio Francesco Gentile, Davide Macri, Bruno Bernardi, and Emanuele Cerruto,

 

Session 3: CH&BD (AI-Driven Agriculture: opportunities and challenges), Chair: Tommaso Ruga

11:30 – 11:45

S33201, AI Image-based systems for enhancing the cultural tourism experience, Fiorella Folino, Tommaso Ruga, Ester Zumpano, Danilo Maurmo, Maria Francesca Foresta, and Eugenio Vocaturo, 

11:45 – 12:00

BigD603, Foundation Models for Big Data, Kranthi Godavarthi, JAYANNA HALLUR, and SUJAN DAS,


Closing Remarks