فناوری هوش‌ مصنوعی در کشاورزی؛ چشم‌انداز، کاربردها و چالش‌ها

Arvind, G, Athira, V. G, Haripriya, H, Rani, R. A, & Aravind, S. (2017, April). Automated irrigation with advanced seed germination and pest control. In 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR) (pp. 64-67). IEEE. Awasthi, Y. (2020). Press “a” for artificial intelligence in agriculture: A review. JOIV: International Journal on Informatics Visualization, 4(3), 112-116. Azadnia, R., Jahanbakhshi, A., Rashidi, S., & Bazyar, P. (2022). Developing an automated monitoring system for fast and accurate prediction of soil texture using an image-based deep learning network and machine vision system. Measurement, 190, 110669. Ben Ayed, R., & Hanana, M. (2021). Artificial intelligence to improve the food and agriculture sector. Journal of Food Quality, 2021, 1-7. Beloev, I., Kinaneva, D., Georgiev, G., Hristov, G., & Zahariev, P. (2021). Artificial intelligence-driven autonomous robot for precision agriculture. Acta Technologica Agriculturae, 24(1), 48-54. Bhagat, P. R., Naz, F., & Magda, R. (2022). Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis. PloS one, 17(6), 0268989. Chavan, T. R., & Nandedkar, A. V. (2018). AgroAVNET for crops and weeds classification: A step forward in automatic farming. Computers and electronics in agriculture, 154, 361-372. Chen, H., Chen, A., Xu, L., Xie, H., Qiao, H., Lin, Q., & Cai, K. (2020). A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources. Agricultural Water Management, 240, 106303. Choudhary, S., Gaurav, V., Singh, A., & Agarwal, S. (2019). Autonomous crop irrigation system using artificial intelligence. Int. J. Eng. Adv. Technol, 8(5), 46-51. Cook, P., & O'Neill, F. (2020). Artificial intelligence in agribusiness is growing in emerging markets. Cubric, M. (2020). Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study. Technology in Society, 62, 101257. Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24-42. Dharmaraj, V., & Vijayanand, C. (2018). Artificial intelligence (AI) in agriculture. International Journal of Current Microbiology and Applied Sciences, 7(12), 2122-2128. Elbeltagi, A., Kushwaha, N. L., Rajput, J., Vishwakarma, D. K., Kulimushi, L. C., Kumar, M., ... & Abd-Elaty, I. (2022). Modelling daily reference evapotranspiration based on stacking hybridization of ANN with meta-heuristic algorithms under diverse agro-climatic conditions. Stochastic Environmental Research and Risk Assessment, 36(10), 3311-3334. Eli-Chukwu, N. C. (2019). Applications of artificial intelligence in agriculture: A review. Engineering, Technology & Applied Science Research, 9(4). Fallah, M., & Ghanbari Parmehr, E. (2023). Detection of Chilo Suppressalis using Smartphone Images and Deep Learning. Journal of Agricultural Machinery, 13(2), 195-211. Fusari, C. M., Kooke, R., Lauxmann, M. A., Annunziata, M. G., Enke, B., Hoehne, M., ... & Keurentjes, J. J. (2017). Genome-wide association mapping reveals that specific and pleiotropic regulatory mechanisms fine-tune central metabolism and growth in Arabidopsis. The Plant Cell, 29(10), 2349-2373. Gentili, A., Compagnucci, F., Gallegati, M., & Valentini, E. (2020). Are machines stealing our jobs? Cambridge Journal of Regions, Economy and Society, 13(1), 153-173. Gerhards, R., & Christensen, S. (2003). Real‐time weed detection, decision making and patch spraying in maize, sugarbeet, winter wheat and winter barley. Weed research, 43(6), 385-392. Harfouche, A. L., Jacobson, D. A., Kainer, D., Romero, J. C., Harfouche, A. H., Mugnozza, G. S.,... & Altman, A. (2019). Accelerating climate resilient plant breeding by applying next-generation artificial intelligence. Trends in biotechnology, 37(11), 1217-1235. Hesami, M., & Jones, A. M. P. (2020). Application of artificial intelligence models and optimization algorithms in plant cell and tissue culture. Applied Microbiology and Biotechnology, 104, 9449-9485. Holzinger, A., Keiblinger, K., Holub, P., Zatloukal, K., & Müller, H. (2023). AI for life: Trends in artificial intelligence for biotechnology. New Biotechnology, 74, 16-24. Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2022). Artificial intelligence applications for industry 4.0: A literature-based study. Journal of Industrial Integration and Management, 7(01), 83-111. Javaid, M., Haleem, A., Khan, I. H., & Suman, R. (2023). Understanding the potential applications of Artificial Intelligence in Agriculture Sector. Advanced Agrochem, 2(1), 15-30. Jha, K., Doshi, A., Patel, P., & Shah, M. (2019). A comprehensive review on automation in agriculture using artificial intelligence. Artificial Intelligence in Agriculture, 2, 1-12. Jung, J., Maeda, M., Chang, A., Bhandari, M., Ashapure, A., & Landivar-Bowles, J. (2021). The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems. Current Opinion in Biotechnology, 70, 15-22. Kakani, V., Nguyen, V. H., Kumar, B. P., Kim, H., & Pasupuleti, V. R. (2020). A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 2, 100033. Kamyshova, G., Osipov, A., Gataullin, S., Korchagin, S., Ignar, S., Gataullin, T., ... & Suvorov, S. (2022). Artificial neural networks and computer vision’s-based phytoindication systems for variable rate irrigation improving. IEEE Access, 10, 8577-8589. Khan, M. H. U., Wang, S., Wang, J., Ahmar, S., Saeed, S., Khan, S. U., ... & Feng, X. (2022). Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding. International Journal of Molecular Sciences, 23(19), 11156. Khoshnevisan, B., Rafiee, S., Pan, J., Zhang, Y., & Liu, H. (2020). A multi-criteria evolutionary-based algorithm as a regional scale decision support system to optimize nitrogen consumption rate; A case study in North China plain. Journal of Cleaner Production, 256, 120213. Kim, K. H., Kim, M. G., Yoon, P. R., Bang, J. H., Myoung, W. H., Choi, J. Y., & Choi, G. H. (2022). Application of CCTV image and semantic segmentation model for water level estimation of irrigation channel. Journal of The Korean Society of Agricultural Engineers, 64(3), 63-73. Krisnawijaya, N. N. K., Tekinerdogan, B., Catal, C., & van der Tol, R. (2022). Data analytics platforms for agricultural systems: A systematic literature review. Computers and Electronics in Agriculture, 195, 106813. Kumbi, A. A., & Birje, M. N. (2022). Deep CNN based sunflower atom optimization method for optimal water control in IoT. Wireless Personal Communications, 1-26. Kumar, R., Yadav, S., Kumar, M., Kumar, J., & Kumar, M. (2020). Artificial intelligence: new technology to improve Indian agriculture. International Journal of Chemical Studies, 8(2), 2999-3005. Kushwaha, N. L, Elbeltagi, A, Mehan, S, Malik, A, & Yousuf, A. (2022). Comparative study on morphometric analysis and RUSLE-based approaches for micro-watershed prioritization using remote sensing and GIS. Arabian Journal of Geosciences, 15(7), 564. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. Liao, M., & Yao, Y. (2021). Applications of artificial intelligence‐based modeling for bioenergy systems: A review. GCB Bioenergy, 13(5), 774-802. Liu, S. Y. (2020). Artificial intelligence (AI) in agriculture. IT Professional, 22(3), 14-15. Lokers, R., Knapen, R., Janssen, S., van Randen, Y., & Jansen, J. (2016). Analysis of Big Data technologies for use in agro-environmental science. Environmental modelling & software, 84, 494-504. Mor, S., Madan, S., & Prasad, K. D. (2021). Artificial intelligence and carbon footprints: Roadmap for Indian agriculture. Strategic Change, 30(3), 269-280. Mohr, S., & Kühl, R. (2021). Acceptance of artificial intelligence in German agriculture: an application of the technology acceptance model and the theory of planned behavior. Precision Agriculture, 22(6), 1816-1844. Muraya, M. M., Chu, J., Zhao, Y., Junker, A., Klukas, C., Reif, J. C., & Altmann, T. (2017). Genetic variation of growth dynamics in maize (Zea mays L.) revealed through automated non‐invasive phenotyping. The Plant Journal, 89(2), 366-380. Nabwire, S., Suh, H. K., Kim, M. S., Baek, I., & Cho, B. K. (2021). Application of artificial intelligence in phenomics. Sensors, 21(13), 4363. Niazian, M., & Niedbała, G. (2020). Machine learning for plant breeding and biotechnology. Agriculture, 10(10), 436. Oliveira, R. C. D., & Silva, R. D. D. S. E. (2023). Artificial Intelligence in Agriculture: Benefits, Challenges, and Trends. Applied Sciences, 13(13), 7405. Omondiagbe, O. P., Lilburne, L., Licorish, S., & MacDonell, S. (2022). Soil Texture Prediction with Automated Deep Convolutional Neural Networks and Population Based Learning. Social Science Research Network, 40(03), 387. Patrício, D. I., & Rieder, R. (2018). Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and electronics in agriculture, 153, 69-81. Pérez-Jaramillo, J. E., Carrión, V. J., de Hollander, M., & Raaijmakers, J. M. (2018). The wild side of plant microbiomes. Microbiome, 6, 1-6. Rai, K. K. (2022). Integrating speed breeding with artificial intelligence for developing climate-smart crops. Molecular Biology Reports, 49(12), 11385-11402. Ritharson, P. I., Raimond, K., Mary, X. A., Eunice, R. J., & Andrew, J. (2023). DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes. Artificial Intelligence in Agriculture. Riese, F. M., & Keller, S. (2019). Soil texture classification with 1D convolutional neural networks based on hyperspectral data. arXiv preprint arXiv:1901.04846. Rodzalan, S. A., Yin, O. G., & Noor, N. N. M. (2020). A foresight study of artificial intelligence in the agriculture sector in Malaysia. J Crit Rev, 7, 1339-1346. Ryan, M. (2020). Agricultural big data analytics and the ethics of power. Journal of Agricultural and Environmental Ethics, 33, 49-69. Ryan, M., van der Burg, S., & Bogaardt, M. J. (2021). Identifying key ethical debates for autonomous robots in agri-food: a research agenda. AI and Ethics, 1-15. Schimmelpfennig, D., & Ebel, R. (2016). Sequential adoption and cost savings from precision agriculture. Journal of Agricultural and Resource Economics, 97-115. Shakoor, N., Lee, S., & Mockler, T. C. (2017). High throughput phenotyping to accelerate crop breeding and monitoring of diseases in the field. Current opinion in plant biology, 38, 184-192. Sharma, R. (2021, May). Artificial intelligence in agriculture: a review. In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 937-942). IEEE. Sood, A., Sharma, R. K., & Bhardwaj, A. K. (2022). Artificial intelligence research in agriculture: A review. Online Information Review, 46(6), 1054-1075. Sparrow, R., Howard, M., & Degeling, C. (2021). Managing the risks of artificial intelligence in agriculture. NJAS: Impact in Agricultural and Life Sciences, 93(1), 172-196. Syed-Ab-Rahman, S. F., Hesamian, M. H., & Prasad, M. (2022). Citrus disease detection and classification using end-to-end anchor-based deep learning model. Applied Intelligence, 52(1), 927-938. Tang, J., Arvor, D., Corpetti, T., & Tang, P. (2021). Mapping Center Pivot Irrigation Systems in the Southern Amazon from Sentinel-2 Images. Water 2021, 13, 298. Tetila, E. C., Machado, B. B., Astolfi, G., de Souza Belete, N. A., Amorim, W. P., Roel, A. R., & Pistori, H. (2020). Detection and classification of soybean pests using deep learning with UAV images. Computers and Electronics in Agriculture, 179, 105836. Tripodi, P., Nicastro, N., Pane, C., & Cammarano, D. (2022). Digital applications and artificial intelligence in agriculture toward next-generation plant phenotyping. Crop and Pasture Science. Udupi, V. R. (2019). Identification of soybean diseases using learning vector quantization neural network algorithm. Journal of Analysis and Computation (JAC), 1(1), 1-3. Vishwakarma, D. K., Pandey, K., Kaur, A., Kushwaha, N. L., Kumar, R., Ali, R.,... & Kuriqi, A. (2022). Methods to estimate evapotranspiration in humid and subtropical climate conditions. Agricultural Water Management, 261, 107378. Waleed, M., Um, T. W., Kamal, T., Khan, A., & Iqbal, A. (2020). Determining the precise work area of agriculture machinery using internet of things and artificial intelligence. Applied Sciences, 10(10), 3365. Weersink, A., Fraser, E., Pannell, D., Duncan, E., & Rotz, S. (2018). Opportunities and challenges for big data in agricultural and environmental analysis. Annual Review of Resource Economics, 10, 19-37. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review. Agricultural systems, 153, 69-80. Xu, Y., Zhang, X., Li, H., Zheng, H., Zhang, J, Olsen, M. S., ... & Qian, Q. (2022). Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. Molecular Plant, 15(11), 1664-1695. Yang, C. C., Prasher, S. O., Landry, J., & Ramaswamy, H. S. (2002). Development of neural networks for weed recognition in corn fields. Transactions of the ASAE, 45(3), 859. Yang, W., Guo, Z., Huang, C., Duan, L., Chen, G., Jiang, N., ... & Xiong, L. (2014). Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice. Nature communications, 5(1), 5087. Yu, Y., Xu, T., Shen, Z., Zhang, Y., & Wang, X. (2019). Compressive spectral imaging system for soil classification with three-dimensional convolutional neural network. Optics Express, 27(16), 23029-23048. Zhang, C., Yue, P., Di, L., & Wu, Z. (2018). Automatic identification of center pivot irrigation systems from landsat images using convolutional neural networks. Agriculture, 8(10), 147. Zhang, Y., Chu, J., Leng, L., & Miao, J. (2020). Mask-refined R-CNN: A network for refining object details in instance segmentation. Sensors, 20(4), 1010. Zhang, P., Guo, Z., Ullah, S., Melagraki, G., Afantitis, A., & Lynch, I. (2021). Nanotechnology and artificial intelligence to enable sustainable and precision agriculture. Nature Plants, 7(7), 864-876.