Alam, S. A. (2024). Innovating 'AI-Kitchen Garden' for vegetable and fruit production for Canadian and US markets. Int. J. Agric. Innov. Cutting Edge Res. 2: 01-17.
Alazzai, W. K., Obaid, M. K., Abood, B. S. Z. and Alzubaidi, L. H. (2024). Smart agriculture solutions: Harnessing AI and IoT for crop management. E3S Web Conf. 477: doi:10.1051/e3sconf/202447700057.
Aldossary, M., Alharbi, H. A. and Hassan, C. A. U. (2024). Internet of Things (IoT)-enabled machine learning models for efficient monitoring of smart agriculture. IEEE Access 12: doi:10.1109/ACCESS.2024.3404651.
Bhimavarapu, P. K., Reddy, K. R. and Reddy, P. S. (2023). A Review on IoT Applications in Smart Agriculture. J. Clean. Prod. 2023: doi:10.3233/ATDE221332.
Bua, C., Adami, D. and Giordano, S. (2024). GymHydro: An innovative modular small-scale smart agriculture system for hydroponic greenhouses. Electronics 13: doi:10.3390/ electronics13071366.
Chen, W. H. and You, F. (2024). Decarbonization through smart energy management: Climate control in building-integrated rooftop greenhouses for urban agriculture across various climate conditions. J. Clean. Prod. 458: doi:10.1016/j.jclepro.2024.142544.
Choudhary, V., Guha, P., Pau, G. and Mishra, S. (2025). An overview of smart agriculture using internet of things (IoT) and web services. Environ. Sustain. Ind. 26: doi:10.1016 /j.indic.2025.100607.
Darabkh, K. A. and Al-Akhras, M. (2025). Towards optimized IoT sensor networks for smart cities: Centrality-aware position-based occlusion-driven and role dynamics solutions for clustering and routing. IEEE Internet Things J. 12: 30282-301. doi:10.1109/JIOT. 2025.3570612.
Debnath, S., Paul, M. and Debnath, T. (2023). Applications of LiDAR in agriculture and future research directions. J. Imaging 9: doi:10.3390/jimaging9030057.
Deep, G. and Verma, J. (2024). Deep learning models for fine scale climate change prediction: Enhancing spatial and temporal resolution using AI. In: Tripathi, G., Shakya, A., Kanga, S., Singh, S. K. and Rai, P. K. (eds.). Big data, artificial intelligence, and data analytics in climate change research. Singapore: Springer. pp: 81-100. doi:10.1007/978-981-97-1685-2_5
Eyre, R., Lindsay, J., Laamrani, A. and Berg, A. (2021). Within-field yield prediction in cereal crops using LiDAR-derived topographic attributes with geographically weighted regression models. Remote Sens. 13: doi:10.3390/rs13204152.
Gade, S. A., Deshmukh, P. B., Amodkar, T. A., Dhawale, M. R. and Jathar, Y. V. (2024). Smart crop advisor system using IoT and machine learning. Int. J. Emerg. Technol. Innov. Res. 11: 484-89.
Gkagkas, G., Vergados, D. J., Michalas, A. and Dossis, M. (2024). The advantage of the 5G network for enhancing the Internet of Things and the evolution of the 6G network. Sensors 24: doi:10.3390/s24082455.
Hoque, A. and Padhiary, M. (2024). Automation and AI in precision agriculture: Innovations for enhanced crop management and sustainability. Asian J. Res. Comput. Sci. 17: 95-109.
Illandara, T. S., De Silva, H. L. H., Madurawala, K. S. H., Dayasena, B. R. D., Srimath, U. and Arachchillage, S. S. (2020). Smart intelligent advisory agent for farming community. In: Proc. Int. Conf. Adv. Comput. (ICAC). IEEE. pp. 392-97.
Jabbari, A., Humayed, A., Reegu, F. A., Uddin, M., Gulzar, Y. and Majid, M. (2023). Smart farming revolution: Farmer's perception and adoption of smart IoT technologies. Sustainability 15: doi:10.3390/su151914541.
Kabato, W., Getnet, G. T., Sinore, T., Nemeth, A. and Molnár, Z. (2025). Towards climate-smart agriculture: Strategies for sustainable agricultural production, food security, and greenhouse gas reduction. Agronomy 15: doi:10.3390/agronomy15030565.
Kayadibi, I. (2025). An IoT-driven framework based on sensor technology for smart greenhouses and precision agriculture. Int. J. Smart Sens. Intel. Syst. 18: doi:10.2478/ijssis-2025-0005.
Kumar, V., Sharma, K. V., Kedam, N., Patel, A., Kate, T. R. and Rathnayake, U. (2024). A comprehensive review on smart and sustainable agriculture using IoT technologies. Smart Agric. Technol. 8: doi:10.1016/j.atech.2024.100487.
Lytridis, C. and Pachidis, T. (2024). Recent advances in agricultural robots for automated weeding. Agri. Eng. 6: 3279–96. doi.org/10.3390/.
Maraveas, C. (2023). Incorporating artificial intelligence technology in smart greenhouses: Current state of the art. Appl. Sci. 13: doi:10.3390/app13010014.
Narayanan, M., Sharma, A. and Ilampooranan, I. (2025). Precision agriculture and water management in India: Artificial intelligence for climate action. In: Jadhav, D. A., Khaple, S., Wable, P. S. and Chendake, A. D. (eds.). Integr. Land Water Resour. Manag. Sustain. Agric. Vol. 1. Smart Agric., Vol. 4. Singapore: Springer. pp: 17-33.
Ngozi, E. C. and Ezeagwu, O. (2019). Applications of Artificial Intelligence in Agriculture: A Review. Eng. Technol. Appl. Sci. Res. 9: 4377-83. oi:10.48084/etasr.2756.
Rahu, M. A., Karim, S., Ali, S. M., Jatoi, G. M. and Sohu, N. D. (2024). Integration of wireless sensor networks, Internet of Things, artificial intelligence, and deep learning in smart agriculture: A comprehensive survey. J. Innov. Intell. Comput. Emerg. Technol. 1: 8-22.
Rajak, P., Ganguly, A., Adhikary, S. and Bhattacharya, S. (2023). Internet of things and smart sensors in agriculture: Scopes and challenges. J. Agric. Food Res. 14: 100776-86.
Rajbonshi, M. P., Mitra, S. and Bhattacharyya, P. (2024). Agro-technologies for greenhouse gases mitigation in flooded rice fields for promoting climate-smart agriculture. Environ. Poll. 350: 123973-82.
Satoła, W. and Satoła, M. (2024). The role of artificial intelligence in precision agriculture: A review. Comput. Electron. Agric. 226: 109386-99.
Selvam, D. C. and Devarajan, Y. (2025). Investigation of emerging technologies in agriculture: An in-depth look at smart farming, nano-agriculture, AI and big data. J. Biosyst. Eng. 50: doi:10.1007/s42853-025-00258-z.
Shahab, H., Naeem, M., Iqbal, M., Aqeel, M. and Ullah, S. S. (2025). IoT-driven smart agricultural technology for real-time soil and crop optimization. Smart Agric. Technol. 10: 100847-57.
Shamshiri, R. R., Hameed, I. A., Thorp, K. R., Balasundram, S. K., Shafian, S., Fatemieh, M., Sultan, M., Mahns, B. and Samiei, S. (2021). Greenhouse automation using wireless sensors and IoT instruments integrated with artificial intelligence. In: Shamshiri, R. R. (ed.). Next-Generation Greenhouses for Food Security. IntechOpen. pp: 237-60.
Sharma, K. and Shivandu, S. K. (2024). Integrating artificial intelligence and Internet of Things (IoT) for enhanced crop monitoring and management in precision agriculture. Sens. Int. 5: 100292-301.
Sharma, M., Kumar, C. J. and Bhattacharyya, D. K. (2024). Machine/deep learning techniques for disease and nutrient deficiency disorder diagnosis in rice crops: A systematic review. Biosyst. Eng. 244: 77-92. doi.org/10.1016/j. biosystemseng.2024.05.014.
Singla, A., Kumar, S. and Kumar, A. (2024). Smart agriculture: A comprehensive review of IoT and AI applications. J. Clean. Prod. 392: 135872-90.
Talaviya, T., Shah, D., Patel, N., Yagnik, H. and Shah, M. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artif. Intell. Agric. 4: 58-73.
Taseer, A. and Han, X. (2024). Advancements in variable rate spraying for precise spray requirements in precision agriculture using unmanned aerial spraying systems: A review. Comput. Electron. Agric. 219: 108841-50.
Trentin, C., Ampatzidis, Y., Lacerda, C. and Shiratsuchi, L. (2024). Tree crop yield estimation and prediction using remote sensing and machine learning: A systematic review. Smart Agric. Technol. 9: 100556-69.
Turgut, Ö., Kok, İ. and Ozdemir, S. (2024). AgroXAI: Explainable AI-driven crop recommendation system for Agriculture 4.0. IEEE International Conference on Big Data (IEEE BigData). Pp.10. doi:10.48550/arXiv.2412.16196.
Wang, S. (2024). Intelligent agricultural greenhouse control system based on Internet of Things and machine learning. arXiv. doi:10.48550/arXiv.2402.09488.
Zheng, H., Ma, W. and He, Q. (2024). Climate-smart agricultural practices for enhanced farm productivity, income, resilience, and greenhouse gas mitigation: A comprehensive review. Mitig. Adapt. Strateg. Glob. Change 29: 28-45.
Alazzai, W. K., Obaid, M. K., Abood, B. S. Z. and Alzubaidi, L. H. (2024). Smart agriculture solutions: Harnessing AI and IoT for crop management. E3S Web Conf. 477: doi:10.1051/e3sconf/202447700057.
Aldossary, M., Alharbi, H. A. and Hassan, C. A. U. (2024). Internet of Things (IoT)-enabled machine learning models for efficient monitoring of smart agriculture. IEEE Access 12: doi:10.1109/ACCESS.2024.3404651.
Bhimavarapu, P. K., Reddy, K. R. and Reddy, P. S. (2023). A Review on IoT Applications in Smart Agriculture. J. Clean. Prod. 2023: doi:10.3233/ATDE221332.
Bua, C., Adami, D. and Giordano, S. (2024). GymHydro: An innovative modular small-scale smart agriculture system for hydroponic greenhouses. Electronics 13: doi:10.3390/ electronics13071366.
Chen, W. H. and You, F. (2024). Decarbonization through smart energy management: Climate control in building-integrated rooftop greenhouses for urban agriculture across various climate conditions. J. Clean. Prod. 458: doi:10.1016/j.jclepro.2024.142544.
Choudhary, V., Guha, P., Pau, G. and Mishra, S. (2025). An overview of smart agriculture using internet of things (IoT) and web services. Environ. Sustain. Ind. 26: doi:10.1016 /j.indic.2025.100607.
Darabkh, K. A. and Al-Akhras, M. (2025). Towards optimized IoT sensor networks for smart cities: Centrality-aware position-based occlusion-driven and role dynamics solutions for clustering and routing. IEEE Internet Things J. 12: 30282-301. doi:10.1109/JIOT. 2025.3570612.
Debnath, S., Paul, M. and Debnath, T. (2023). Applications of LiDAR in agriculture and future research directions. J. Imaging 9: doi:10.3390/jimaging9030057.
Deep, G. and Verma, J. (2024). Deep learning models for fine scale climate change prediction: Enhancing spatial and temporal resolution using AI. In: Tripathi, G., Shakya, A., Kanga, S., Singh, S. K. and Rai, P. K. (eds.). Big data, artificial intelligence, and data analytics in climate change research. Singapore: Springer. pp: 81-100. doi:10.1007/978-981-97-1685-2_5
Eyre, R., Lindsay, J., Laamrani, A. and Berg, A. (2021). Within-field yield prediction in cereal crops using LiDAR-derived topographic attributes with geographically weighted regression models. Remote Sens. 13: doi:10.3390/rs13204152.
Gade, S. A., Deshmukh, P. B., Amodkar, T. A., Dhawale, M. R. and Jathar, Y. V. (2024). Smart crop advisor system using IoT and machine learning. Int. J. Emerg. Technol. Innov. Res. 11: 484-89.
Gkagkas, G., Vergados, D. J., Michalas, A. and Dossis, M. (2024). The advantage of the 5G network for enhancing the Internet of Things and the evolution of the 6G network. Sensors 24: doi:10.3390/s24082455.
Hoque, A. and Padhiary, M. (2024). Automation and AI in precision agriculture: Innovations for enhanced crop management and sustainability. Asian J. Res. Comput. Sci. 17: 95-109.
Illandara, T. S., De Silva, H. L. H., Madurawala, K. S. H., Dayasena, B. R. D., Srimath, U. and Arachchillage, S. S. (2020). Smart intelligent advisory agent for farming community. In: Proc. Int. Conf. Adv. Comput. (ICAC). IEEE. pp. 392-97.
Jabbari, A., Humayed, A., Reegu, F. A., Uddin, M., Gulzar, Y. and Majid, M. (2023). Smart farming revolution: Farmer's perception and adoption of smart IoT technologies. Sustainability 15: doi:10.3390/su151914541.
Kabato, W., Getnet, G. T., Sinore, T., Nemeth, A. and Molnár, Z. (2025). Towards climate-smart agriculture: Strategies for sustainable agricultural production, food security, and greenhouse gas reduction. Agronomy 15: doi:10.3390/agronomy15030565.
Kayadibi, I. (2025). An IoT-driven framework based on sensor technology for smart greenhouses and precision agriculture. Int. J. Smart Sens. Intel. Syst. 18: doi:10.2478/ijssis-2025-0005.
Kumar, V., Sharma, K. V., Kedam, N., Patel, A., Kate, T. R. and Rathnayake, U. (2024). A comprehensive review on smart and sustainable agriculture using IoT technologies. Smart Agric. Technol. 8: doi:10.1016/j.atech.2024.100487.
Lytridis, C. and Pachidis, T. (2024). Recent advances in agricultural robots for automated weeding. Agri. Eng. 6: 3279–96. doi.org/10.3390/.
Maraveas, C. (2023). Incorporating artificial intelligence technology in smart greenhouses: Current state of the art. Appl. Sci. 13: doi:10.3390/app13010014.
Narayanan, M., Sharma, A. and Ilampooranan, I. (2025). Precision agriculture and water management in India: Artificial intelligence for climate action. In: Jadhav, D. A., Khaple, S., Wable, P. S. and Chendake, A. D. (eds.). Integr. Land Water Resour. Manag. Sustain. Agric. Vol. 1. Smart Agric., Vol. 4. Singapore: Springer. pp: 17-33.
Ngozi, E. C. and Ezeagwu, O. (2019). Applications of Artificial Intelligence in Agriculture: A Review. Eng. Technol. Appl. Sci. Res. 9: 4377-83. oi:10.48084/etasr.2756.
Rahu, M. A., Karim, S., Ali, S. M., Jatoi, G. M. and Sohu, N. D. (2024). Integration of wireless sensor networks, Internet of Things, artificial intelligence, and deep learning in smart agriculture: A comprehensive survey. J. Innov. Intell. Comput. Emerg. Technol. 1: 8-22.
Rajak, P., Ganguly, A., Adhikary, S. and Bhattacharya, S. (2023). Internet of things and smart sensors in agriculture: Scopes and challenges. J. Agric. Food Res. 14: 100776-86.
Rajbonshi, M. P., Mitra, S. and Bhattacharyya, P. (2024). Agro-technologies for greenhouse gases mitigation in flooded rice fields for promoting climate-smart agriculture. Environ. Poll. 350: 123973-82.
Satoła, W. and Satoła, M. (2024). The role of artificial intelligence in precision agriculture: A review. Comput. Electron. Agric. 226: 109386-99.
Selvam, D. C. and Devarajan, Y. (2025). Investigation of emerging technologies in agriculture: An in-depth look at smart farming, nano-agriculture, AI and big data. J. Biosyst. Eng. 50: doi:10.1007/s42853-025-00258-z.
Shahab, H., Naeem, M., Iqbal, M., Aqeel, M. and Ullah, S. S. (2025). IoT-driven smart agricultural technology for real-time soil and crop optimization. Smart Agric. Technol. 10: 100847-57.
Shamshiri, R. R., Hameed, I. A., Thorp, K. R., Balasundram, S. K., Shafian, S., Fatemieh, M., Sultan, M., Mahns, B. and Samiei, S. (2021). Greenhouse automation using wireless sensors and IoT instruments integrated with artificial intelligence. In: Shamshiri, R. R. (ed.). Next-Generation Greenhouses for Food Security. IntechOpen. pp: 237-60.
Sharma, K. and Shivandu, S. K. (2024). Integrating artificial intelligence and Internet of Things (IoT) for enhanced crop monitoring and management in precision agriculture. Sens. Int. 5: 100292-301.
Sharma, M., Kumar, C. J. and Bhattacharyya, D. K. (2024). Machine/deep learning techniques for disease and nutrient deficiency disorder diagnosis in rice crops: A systematic review. Biosyst. Eng. 244: 77-92. doi.org/10.1016/j. biosystemseng.2024.05.014.
Singla, A., Kumar, S. and Kumar, A. (2024). Smart agriculture: A comprehensive review of IoT and AI applications. J. Clean. Prod. 392: 135872-90.
Talaviya, T., Shah, D., Patel, N., Yagnik, H. and Shah, M. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artif. Intell. Agric. 4: 58-73.
Taseer, A. and Han, X. (2024). Advancements in variable rate spraying for precise spray requirements in precision agriculture using unmanned aerial spraying systems: A review. Comput. Electron. Agric. 219: 108841-50.
Trentin, C., Ampatzidis, Y., Lacerda, C. and Shiratsuchi, L. (2024). Tree crop yield estimation and prediction using remote sensing and machine learning: A systematic review. Smart Agric. Technol. 9: 100556-69.
Turgut, Ö., Kok, İ. and Ozdemir, S. (2024). AgroXAI: Explainable AI-driven crop recommendation system for Agriculture 4.0. IEEE International Conference on Big Data (IEEE BigData). Pp.10. doi:10.48550/arXiv.2412.16196.
Wang, S. (2024). Intelligent agricultural greenhouse control system based on Internet of Things and machine learning. arXiv. doi:10.48550/arXiv.2402.09488.
Zheng, H., Ma, W. and He, Q. (2024). Climate-smart agricultural practices for enhanced farm productivity, income, resilience, and greenhouse gas mitigation: A comprehensive review. Mitig. Adapt. Strateg. Glob. Change 29: 28-45.
