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Research Articles

Vol. 12 No. sp3 (2025): Advances in Plant Health Improvement for Sustainable Agriculture

Predictive analysis, data visualization and artificial intelligence tools for agricultural research and communication

DOI
https://doi.org/10.14719/pst.8276
Submitted
14 March 2025
Published
03-06-2025

Abstract

Data Science offers powerful tools that predict and promise to enhance crop yields, optimize the farm resource utilization. Artificial Intelligence (AI) can perform tasks that typically require human intelligence, which encompasses a wide range of applications in agriculture starting from seed selection to robotic harvest. Synergy between data science and AI will grow strong to drive technological advancements and shape the future of agriculture sector. This paper presents application of few data science and AI tools in agriculture. Trends, patterns and variations in cost, benefit and returns from crop like Paddy, Groundnut, Sugarcane, Cotton and Maize across different states from 2011 to 2020 were analysed through data visualization. An experimental attempt was also made through web scrapping to understand the United States consumer preference towards the brands (Spice Train, Tellicherry), quantity (11 and 14 oz) and price (15-25 USD package) of pepper. Predictive modeling had shown that the export of pepper may fluctuate over the years while export of basmati rice, cashew nuts and tea will gradually increase for the next few years. High-definition images of tomatoes of different shape, colour, size were used to train the algorithm and tested for identification and classification of tomatoes and images of FAW infested maize were trained and tested for deduction accuracy, which was 82.5 %. Prediction accuracy will increase when the algorithm is trained with large number of images. AI avatars are widely used in social communication for various purposes like short communications, storytelling and documentation, which will be also used effectively for agricultural research communication and learning purposes.

References

  1. 1. Mohsen Soori, Behrooz Arezoo, Roza Dastres. Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cognitive Robotics. 2023; (3) 54–70. https://doi.org/10.1016/j.cogr.2023.04.001
  2. 2. Subeesh A, Mehta C.R. Automation and digitization of agriculture using artificial intelligence and internet of things. Artificial Intelligence in Agriculture. 2021; (5) 278–291. https://doi.org/10.1016/j.aiia.2021.11.004
  3. 3. Chow MS, Prahadeeswaran M, Karthick V, Sumathi CS, Patil SG. Adaptive routing in agricultural supply chains: Harnessing Q-learning for optimal decision-making in dynamic environments. Plant Science To-day.2024;11(sp4):01-08. https://doi.org/10.14719/pst.5426
  4. 4. Kavitha J, Shabnam Kumari, Manivannan K, Amit Kumar Tyagi. Role of data visualization and big data analytics in smart agriculture. in book: Infrastructure possibilities and human-centered approaches with industry 5.0; IGI GLOBAL. 2024. https://doi.org/10.4018/979-8-3693-5266-3.ch007
  5. 5. Elbasi E, Zaki C, Topcu AE, Abdelbaki W, Zreikat AI, Cina E, Shdefat A, Saker L. Crop prediction model using machine learning algorithms. Applied Sciences. 2023; 13(16):9288. https://doi.org/10.3390/app13169288
  6. 6. Che’Ya NN, Mohidem NA, Roslin NA, Saberioon M, Tarmidi MZ, Arif Shah J, et al. Mobile computing for pest and disease management using spectral signature analysis: a review. Agronomy. 2022; 12(4):967. https://doi.org/10.3390/agronomy12040967
  7. 7. Joanne Yu, Astrid Dickinger, Kevin Kam Fung So, Roman. Egger artificial intelligence-generated virtual influencer: Examining the effects of emotional display on user engagement. Journal of Retailing and Consumer Services. 2024; (76) 103560. https://doi.org/10.1016/j.jretconser.2023.103560
  8. 8. Štofejová L, Kráľ Š, Fedorko R, Bačík R, Tomášová M. Sustainability and consumer behavior in electronic commerce. Sustainability. 2023; 15(22):15902. https://doi.org/10.3390/su152215902
  9. 9. Deshmane V, Musale P, Joshi P, Chinta V, Gokak K, Dalbhanjan I. Web scraping for E-commerce website. International Journal for Innovative Engineering & Management Research, 202413(4). https://doi.org/10.48047/IJIEMR/V13/ISSUE 04/24
  10. 10. Elbasi E, Zaki C, Topcu AE, Abdelbaki W, Zreikat AI, Cina E, et al. Crop prediction model using machine learning algorithms. Applied Sciences. 2023; 13(16):9288. https://doi.org/10.3390/app13169288
  11. 11. Kumar R, Channi HK, Banga HK. Data analytics in agriculture: Predictive models and real‐time decision‐making. Smart agritech: Robotics, AI, and internet of things (IoT) in agriculture. 2024:169-200. https://doi.org/10.1002/9781394302994.ch7
  12. 12. Aishwarya B, Vadivel R. Python based image processing and machine learning for plant disease detection. International Journal of Computer Sciences and Engineering. 2020; 10(6); 27-31. https://doi.org/10.26438/ijcse/v10i6.2731
  13. 13. Chen J, Kang J, Xu M, Xiong Z, Niyato D, Chen C, et al. Multiagent deep reinforcement learning for dynamic avatar migration in AIoT-enabled vehicular metaverses with trajectory prediction. IEEE Internet of Things Journal. 2023;11(1):70-83. https://doi.org/10.1109/JIOT.2023.3296075

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