Data Collection and Analysis Empowered with AI for Robotized Olive Oil Precision Farming

Abstract

ABSTRACT

This study examines robotized precision farming in olive oil production by integrating spatial data analytics and artificial intelligence (AI). A comprehensive analytical framework converts heterogeneous orchard data into actionable management information to address resource scarcity, climate variability, and sustainable intensification. The approach combines soil organic matter (OM), Normalized Difference Vegetation Index (NDVI), yield measurements, and geographic coordinates using spatial clustering, variogram analysis, Random Forest regression, and multivariate spatial analysis. Although AI-driven olive classification using RGB and VIS-NIR imaging can identify cultivars and defects, most existing systems focus on post-harvest quality assessment. This paper instead targets spatial variability and site-specific orchard control, extending AI to pre-harvest and field-wide decision support. Key findings show that: (1) spatial clustering effectively defined three management zones despite poor OM prediction from sensor variables (R² ≈ 0), with zone-based management approximately 90% effective even when using single-metric decisions; (2) linear regression revealed an unexpectedly weak correlation between OM and olive yield (r = 0.072), redirecting optimization toward other controllable variables and challenging traditional agronomic assumptions; and (3) robotized agricultural solutions with AI-enhanced spatial analytics provide a powerful closed-loop model for precision olive orchard management. The work advances Agriculture 5.0 by augmenting vision-based olive quality-assessment tools, enabling smart, autonomous, and sustainable Mediterranean olive oil production.

REFERENCES

[1] M. Eissa, "Precision agriculture using artificial intelligence and robotics," Journal of Research in Agriculture and Food Sciences, vol. 1, no. 1, p. 35, 2024.

[2] M. T. Linaza, J. Posada, J. Bund, P. Eisert, A. Pagani, I. G. Olaizola, M. Quartulli, J. Döllner, A. Barriguinha, T. Moysiadis, and L. Lucat, "Data-driven artificial intelligence applications for sustainable precision agriculture," Agronomy, vol. 11, no. 6, p. 1227, 2021.

[3] A. McBratney, B. Whelan, T. Ancev, and J. Bouma, "Future directions of precision agriculture," Precision Agriculture, vol. 6, no. 1, pp. 7–23, 2005.

[4] Q. Zhang and F. J. Pierce, Agricultural Automation: Fundamentals and Practices. Boca Raton, FL, USA: CRC Press, 2002.

[5] R. Ben Ayed, M. Hanana, S. Ercisli, R. Karunakaran, A. Rebai, and F. Moreau, "Integration of innovative technologies in the agri-food sector: The fundamentals and practical case of DNA-based traceability of olives from fruit to oil," Plants, vol. 11, no. 9, p. 1230, 2022.

[6] O. S. Arvaniti, E. Rodias, A. Terpou, N. Afratis, G. Athanasiou, and T. Zahariadis, "Bactrocera oleae control and smart farming technologies for olive orchards in the context of optimal olive oil quality: A review," Agronomy, vol. 14, no. 11, p. 2586, 2024.

[7] S. Abdul Ameer, M. A. Alkhafaji, Z. Jaffer, and M. Al-Farouni, "Empowering farmers with IoT, UAVs, and deep learning in smart agriculture," in E3S Web of Conferences, ICECS'24, vol. 491, p. 04007. EDP Sciences, 2024.

[8] E. Elbasi, N. Mostafa, C. Zaki, Z. AlArnaout, A. E. Topcu, and L. Saker, "Optimizing agricultural data analysis techniques through AI-powered decision-making processes," Applied Sciences, vol. 14, no. 17, p. 8018, 2024.

[9] V. Balaska, Z. Adamidou, Z. Vryzas, and A. Gasteratos, "Sustainable crop protection via robotics and artificial intelligence solutions," Machines, vol. 11, no. 8, p. 774, 2023.

[10] I. Beloev, D. Kinaneva, G. Georgiev, G. Hristov, and P. Zahariev, "Artificial intelligence-driven autonomous robot for precision agriculture," Acta Technologica Agriculturae, vol. 24, no. 1, pp. 48–54, 2021.

[11] R. Manganiello, L. Moscovini, L. Ortenzi, S. Figorilli, F. Pallottino, C. Costa, S. Vasta, S. Violino, and F. Antonucci, "Artificial intelligence approaches for real-time table olive cultivars quality assessment," European Food Research and Technology, vol. 252, no. 67, 2026.

[12] D. Aguilera Puerto, O. Cáceres Moreno, D. M. Martinez Gila, J. Gomez Ortega, and J. Gamez Garcia, "Online system for the identification and classification of olive fruits for the olive oil production process," Journal of Food Measurement and Characterization, vol. 13, pp. 716–727, 2019.

[13] F. Fuentes-Penailillo, K. Gutter, R. Vega, and G. Carrasco Silva, "Transformative technologies in digital agriculture: Leveraging internet of things, remote sensing, and artificial intelligence for smart crop management," Journal of Sensor and Actuator Networks, vol. 13, no. 4, p. 39, 2024.

[14] G. A. Senthil, S. U. Suganthi, L. Prinslin, R. Selvi, and R. Prabha, "Generative AI in agri: Sustainability in smart precision farming yield prediction mapping system based on GIS using deep learning and GPS," Procedia Computer Science, vol. 252, pp. 365–380, 2025.

[15] M. M. R. Akash, "Cardiotoxicity prediction models in cancer patients using artificial intelligence and genomics," International Journal of Drug Delivery Technology, vol. 16, pp. 60–73.

[16] P. Kaur, J. Stoltzfus, and V. Yellapu, "Descriptive statistics," International Journal of Academic Medicine, vol. 4, no. 1, pp. 60–63, 2018.

[17] P. Sedgwick, "Endgames statistical question: Pearson's correlation coefficient," BMJ, vol. 345, p. e4483, 2012.

[18] S. K. Behera, A. K. Shukla, R. Kumar, R. Mishra, V. Shukla, H. Yadav, and S. P. Datta, "Geostatistics-based understanding of temporal changes in spatial distribution of soil fertility parameters in an intensively cultivated area of north India," Journal of Soil Science and Plant Nutrition, vol. 25, pp. 4125–4143, 2025.

[19] F. He and F. Cai, "Feature selection and comparison of logistic regression and random forest for stability assessment of landslide dams," Landslides, 2025.

[20] D. J. Mulla, "Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps," Biosystems Engineering, vol. 114, no. 4, pp. 358–371, 2013.

[21] Y. Li, Z. Shi, C. Wu, and F. Li, "Management zone delineation for precision agriculture based on spatial clustering analysis," Computers and Electronics in Agriculture, vol. 123, pp. 17–26, 2016.

[22] S. Iivonen, O. Niemeläinen, M. Kokkinen, and L. Jauhainen, "Yield variation and yield potential of organic arable crops in Finland derived from statistical data," Agricultural and Food Science, vol. 33, pp. 212–222, 2024.

[23] M. Tafazzul, S. Anjum, A. A. Awan, A. Sajjad, S. K. Laghari, A. Baloch, Z. A. Bazai, K. Shahzad, M. A. Awan, M. A. Shafique, and S. Naeem, "Mapping spatial variability of ecological footprints in olive (Olea europaea L.) fruits, oil, and soils through elemental profiling," Journal of Animal and Plant Sciences, vol. 36, no. 2, 2026.

[24] I. Khan, V. Yadav, A. Kainthola et al., "Bivariate statistical approaches for spatial susceptibility zonation," Environmental Earth Sciences, 2024.

[25] J. Long, T. Nelson, and M. A. Wulder, "Regionalization of landscape pattern indices using multivariate cluster analysis," Landscape Ecology, vol. 25, no. 6, pp. 847–864, 2010.

[26] J. B. Arogundade and T. K. Njoku, "Maximizing crop yields through AI-driven precision agriculture and machine learning," International Research Journal of Modernization in Engineering Technology and Science, vol. 6, no. 10, pp. 1022–1030, 2024.

[27] J. E. Fernández, F. Moreno, I. F. Girón, and O. M. Blázquez, "Modelling olive orchard evapotranspiration and productivity under different water supply strategies," Plant Biosystems, vol. 142, no. 2, pp. 356–364, 2008.

[28] A. Khan, F. Amin, and U. Imtiaz, "Explainable AI-based ransomware early detection using file-system behavioral features and random forest," Global Research Journal of Natural Science and Technology, vol. 3, no. 4, 2025, doi: 10.53762/grjnst.03.04.39.

[29] A. Khan, F. Amin, and U. Imtiaz, "SecurePulse-GRID: Trust-aware predictive load balancing and rapid cyber containment in distributed smart microgrids," The Asian Bulletin of Big Data Management, vol. 6, no. 1, pp. 697–708, 2026, doi: 10.62019/smxj7m37.

[30] A. Dash, B. S. Samantray, K. H. K. Reddy, F. Amin, and S. K. Mishra, "A secure framework for smart waste management using IoT, machine learning, and blockchain," in Proc. 2026 1st Int. Conf. Emerging Trends in Advancements and Applications of Computational Intelligence Techniques (ETAACT), IEEE, 2026, pp. 1–7, doi: 10.1109/ETAACT69135.2026.11541497.

[31] R. H. Ahmed, M. Sultana, M. Imran, N. Ahmad, and M. D. Rasheed, "A decentralised multi-agent reinforcement learning framework for fault-tolerant swarm coordination under partial observability," in Proc. 2026 2nd Int. Symp. AI-Driven Engineering Systems (ISADES), IEEE, 2026, pp. 1–8.

[32] M. D. Rasheed, M. Akram, M. Imran, R. H. Ahmed, and A. Rauf, "Towards human-centric smart manufacturing: A digital twin enabled affective ergonomic framework for adaptive human robot collaboration," International Journal of Advances in Signal and Image Sciences, pp. 1544–1555, 2025.

[33] M. Imran, M. S. Khan, N. Ahmad, and A. M. Delshad, "AI-based lightweight security framework for IoT networks against botnet attacks," International Journal of Contemporary Issues in Social Sciences, vol. 2, no. 4, pp. 1746–1757, Nov. 2023.

[34] M. I. K. Jabed, M. Imran, A. A. Khan, M. Mehedi, A. Islam, and R. Pervez, "Explainable machine learning framework for early heart disease detection using SMOTE and SHAP," Vascular and Endovascular Review, vol. 9, no. 1, pp. 316–324, 2026.

[35] M. I. K. Jabet, M. Imran, C. A. Gomes, P. S. Ponduru, S. Mandal, and S. Hassan, "Deep learning and explainable AI framework for predicting lung cancer severity," Journal of Computer Science and Technology Studies, vol. 7, no. 12, pp. 573–598, 2025.

[36] M. D. Rasheed, W. Khan, M. Imran, N. Ahmad, Y. Khan, M. Akram, and M. Sultana, "Leveraging artificial intelligence for advance data networking and cybersecurity," Spectrum of Engineering Sciences, vol. 4, no. 3, pp. 286–298, 2026.

[37] M. I. K. Jabet, M. R. M. Sirazy, S. Mandal, S. A. Akter, A. Hassan, and H. Esa, "Developing AI-based financial forecasting and cybersecurity systems for the US digital economy," Frontiers in Computer Science and Artificial Intelligence, vol. 5, no. 5, pp. 30–38, 2026.

[38] M. I. K. Jabet, M. A. Manzoor, F. M. Tofa, and M. H. Khan, "Interpretable ensemble learning approach for breast cancer diagnosis using SHAP-based explainable AI," Journal of Computer Science and Technology Studies, vol. 8, no. 8, pp. 244–255, 2026.

[39] M. I. K. Jabet, M. P. Ahmed, F. M. Tofa, M. F. Islam, C. A. Gomes, and R. M. Sirazy, "Federated intrusion detection for Internet of Medical Things networks: Differential privacy, non-IID robustness, and cross-device generalization," Journal of Computer Science and Technology Studies, vol. 8, no. 8, pp. 303–315, 2026.

[40] M. Tafazzul, S. Anjum, A. A. Awan, A. Sajjad, S. K. Laghari, A. Baloch, Z. A. Bazai, K. Shahzad, M. A. Awan, M. A. Shafique, and S. Naeem, "Mapping spatial variability of ecological footprints in olive (Olea europaea L.) fruits, oil, and soils through elemental profiling," Journal of Animal and Plant Sciences, vol. 36, no. 2, 2026.