Uzaktan Algılama, Yapay Zeka ve Geleceğin Akıllı Tarım Teknolojisi Trendleri

Yıl 2023, Sayı: 52, 234 - 246, 15.12.2023

Muhammet Fatih Çakmakçı Ramazan Cakmakcı

Öz

Gelecek vadeden bir sektör olarak dijital tarım ve teknolojiler; verimliliği ve üretkenliği iyileştirmeye, biyolojik çeşitliliğin ve toprağın korunmasına, gıda güvenliğinin iyileştirilmesine, sağlık ve beslenmeye, iklim değişliği ile mücadeleye ve kıt kaynaklar üzerindeki baskının azaltılmasına yardımcı olabilir. Akıllı tarımda nesnelerin interneti (IoT), kablosuz sensör ağları (WSN), uzaktan algılama (RS), insansız hava araçları (İHA), büyük veri analitiği, makine öğrenmesi (ML), derin öğrenme (DL) ve yapay zeka (AI) kullanımı, tarım ve endüstrinin uzun ömürlü ve sürdürülebilir olması için kritik öneme sahiptir. AI ve ML tarımda öncelikle verim tahmini, yabancı ot, hastalık, azot ve su stresi tespiti, ürün kalite özelliklerinin tespiti ve sınıflandırılması, bitki türlerinin tanımlanması ve sınıflandırılması gibi bitki yönetimi alanlarında kullanılacağı gibi evapotranspirasyon ve sıcaklık tahmini, toprak kurumasının değerlendirilmesi, toprak sıcaklığı, toprak nemi, sulama zamanı, miktarı ve optimizasyonunun belirlenmesi, toprakta karbon ve azot tahmini gibi toprak ve su yönetiminde öneriler sunabilir. Bu derlemede, tarımı daha verimli hale getirme ve sürdürülebilirlik için WSN, IoT, AI ve ML gibi temel teknolojiler kullanılarak bilginin algılanması, izlenmesi, toplanması, analiz edilmesi ve bilgilerden anlamlı öngörüler çıkarılarak tarımsal faaliyetlerde uygulanabilirliği tartışılmıştır.

Anahtar Kelimeler

Yapay zeka (AI), Makine öğrenmesi (ML), Nesnelerin interneti (IoT), Tarımsal uzaktan algılama, Hassas tarım (PA)

Remote Sensing, Artificial Intelligence and Smart Agriculture Technology Trends of the Future

Öz

As a promising sector, digital agriculture and technologies can help improve efficiency, productivity, and food security, protect biodiversity and soil, while also helping to improve food security, nutrition and health, combat climate change and reduce pressure on scarce resources. The use of the internet of things (IoT), wireless sensor networks (WSN), remote sensing (RS), unmanned aerial vehicles (UAVs), big data analytics (BDA), machine learning (ML), deep learning (DL) and artificial intelligence (AI) in smart agriculture is critical for the long-term viability and sustainability of agriculture and industry. In agricultural terms, AI and ML can be used in crop management areas such as yield prediction, weed, disease, nitrogen, and water stress detection, detection and classification of crop quality characteristics, and classification of plant species, as well as suggestions and insights can provided on water management and soil management such as estimation of evapotranspiration and temperature, evaluation of soil drying, estimation of soil temperature and soil moisture, determination of irrigation time, amount and optimization, and prediction of soil carbon and total nitrogen. In this review, its applicability in agricultural activities such as sensing, monitoring, collecting, analysing, and extracting meaningful insights from information by using basic technologies such as WSN, IoT, AI and ML to make agriculture more efficient and sustainable is discussed.

Anahtar Kelimeler

Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), Agricultural remote sensing, Precision agriculture (PA)

Kaynakça

• Abioye, E.A., Abidin, M.S.Z., Mahmud, M.S.A., Buyamin, S., Ishak, M.H.I., Rahman, M.K. I.A., Otuoze, A.O., Onotu, P., & Ramli, M.S.A. (2020). A review on monitoring and advanced control strategies for precision irrigation. Computers and Electronics in Agriculture, 173, 105441.
• Ahirwar, S., Swarnkar, R., Bhukya, S., & Namwade, G. (2019). Application of drone in agriculture. International Journal of Current Microbiology and Applied Sciences, 8(1), 2500–2505.
• Ahmad, A., Ordoñez, J., Cartujo, P., & Martos, V. (2021). Remotely piloted aircraft (RPA) in agriculture: A pursuit of sustainability. Agronomy, 11(1), 7.
• Alreshidi, E. (2019). Smart sustainable agriculture (SSA) solution underpinned by internet of Things (IoT) and artificial intelligence (AI). International Journal of Advanced Computer Science and Applications, 10(5), 93-102.
• An, J., Li, W., Li, M., Cui, S., & Yue, H. (2019). Identification and classification of maize drought stress using deep convolutional neural network. Symmetry, 11(2), 256.
• Apolo-Apolo, O.E, Martínez-Guanter, J., Egea, G., Raja, P., & Pérez-Ruiz, M. (2020). Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV. European Journal of Agronomy, 115, 126030.
• Araújo, S.O., Peres, R. S., Barata, J., Lidon, F., & Ramalho, J.C. (2021). Characterising the agriculture 4.0 landscape-emerging trends, challenges and opportunities. Agronomy, 11 (4), 667.
• Aslantaş, R., & Olgun, M. (1999). İklim verilerinden faydalanarak çoruh vadisinde yetişen cevizlerde verim tahmini ve modellemesi. Türkiye III. Ulusal Bahçe Bitkileri Kongresi, Ankara, 305–309.
• Aslantaş, R., Karakurt, H., & Karakurt, Y. (2010). Bitkilerin düşük sıcaklıklara dayanımında hücresel ve moleküler mekanizmalar. Atatürk Üniversitesi Ziraat Fakültesi Dergisi, 41 (2), 157-167.
• Bali, N., & Singla, A. (2021). Deep learning based wheat crop yield prediction model in Punjab Region of North India. Applied Artificial Intelligence, 35(15), 1304–1328.
• Barrero O., & Perdomo S.A. (2018). RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields. Precision Agriculture, 19(5), 809–822.
• Berckmans, D. (2014). Precision livestock farming technologies for welfare management in intensive livestock systems. Revue Scientifique et Technique, 33, 189–196.
• Bhagat, P.R., Naz, F., & Magda, R. (2022). Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis. PLoS One, 17(6), e0268989.
• Bibri, S.E. (2018). The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability. Sustainable Cities and Society, 38, 230-253.
• Bischoff, V., Farias, K., Menzen, J.P., & Pessin, G. (2021). Technological support for detection and prediction of plant diseases: A systematic mapping study. Computers and Electronics in Agriculture, 181, 105922.
• Blagojević, M., Blagojević, M., & Ličina, V. (2016). Web-based intelligent system for predicting apricot yields using artificial neural networks. Scientia Horticulturae, 213, 125-131.
• Boursianis, A.D., Papadopoulou, M.S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas G., Karagiannidis, G., Wan, S., & Goudos, S.K. (2022). Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review. Internet of Things, 18, 100187.
• Bu, F., & Wang, X. (2019). A smart agriculture IoT system based on deep reinforcement learning. Future Generation Computer Systems, 99, 500–507.
• Castaldi, F., Pelosi, F., Pascucci, S., & Casa, R. (2017). Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize. Precision Agriculture, 18(1), 76–94.
• Chang, A., Jung, J., Maeda, M. M., & Landivar, J. (2017). Crop height monitoring with digital imagery from unmanned aerial system (UAS). Computers and Electronics in Agriculture, 141, 232 – 237.
• Chen, Q., Li, L., Chong, C., & Wang, X. (2022). AI‐enhanced soil management and smart farming. Soil Use and Management, 38(1), 7-13.
• Chen, Y., Lee, W. S., Gan, H., Peres, N., Fraisse, C., Zhang, Y., & He, Y. (2019). Strawberry yield prediction based on a deep neural network using high-resolution aerial orthoimages. Remote Sensing, 11(13), 1584.
• Cheng, H., Damerow, L., Sun, Y., & Blanke, M. (2017). Early yield prediction using image analysis of apple fruit and tree canopy features with neural networks. Journal of Imaging, 3(1), 6.
• Cruz, A., Ampatzidis, Y., Pierro, R., Materazzi, A., Panattoni, A., De Bellis, L., & Luvisi, A. (2019). Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence. Computers and Electronics in Agriculture, 157, 63–76.
• Çakmakçı, R., (2019). A Review of biological fertilizers current use, new approaches, and future perspectives. International Journal of Innovative Studies in Sciences and Engineering Technology (IJISSET), 5(7), 83-92.
• Çakmakçı, R., Salık, M.A., Çakmakçı, S. (2023). Assessment and principles of environmentally sustainable food and agriculture systems. Agriculture, 13, 1073.
• Das, J.V., Sharma, S., & Kaushik, A. (2019). Views of Irish farmers on smart farming technologies: An observational study. AgriEngineering, 1(2), 164–187.
• Dayıoğlu, M.A., & Türker, U. (2021). Digital transformation for sustainable future- agriculture 4.0: A review. Journal of Agricultural Science, 27(4), 373-399.
• DeChant, C., Wiesner-Hanks, T., Chen, S., Stewart, E.L., Yosinski, J., Gore, M.A., Nelson, R.J., & Lipson, H. (2017). Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology, 107, 1426–1432.
• Devi, G., Sowmiya, N., Yasoda, K., Muthulakshmi, K., & Balasubramanian, K. (2020). Review on application of drones for crop health monitoring and spraying pesticides and fertilizer. Journal of Critical Reviews, 7, 667–672.
• Dhanaraju, M., Chenniappan, P., Ramalingam, K., Pazhanivelan, S., & Kaliaperumal, R. (2022). Smart farming: Internet of things (IoT)-based sustainable agriculture. Agriculture, 12(10), 1745.
• Diwate, S., Nitnaware, V., & Argulwar, K. (2018). Design and development of application specific drone machine for seed sowing. International Research Journal of Engineering and Technology, 5(5), 4003–4007.
• Duncan, E., Glaros, A., Ross, D.Z., & Nost, E. (2021). New but for whom? Discourses of innovation in precision agriculture. Agriculture and Human Values, 38, 1181–1199.
• Duysak, H., Özkaya, U., Yiğit, E. (2020). Grain surface classification via machine learning methods. Avrupa Bilim ve Teknoloji Dergisi, Özel Sayı, 54-59.
• EC, (2020). European Commission, Farm to fork strategy: For a fair, healthy and environmentally-friendly food system. https://food.ec.europa.eu/system/files/2020.
• Elahi, E., Weijun, C., Zhang, H., & Nazeer, M. (2019). Agricultural intensification and damages to human health in relation to agrochemicals: Application of artificial intelligence. Land Use Policy, 83, 461–474.
• Escalante, H. J., Rodrguez-Snchez, S., Jimnez-Lizrraga, M., Morales-Reyes, A. Calleja, J. D. L., & Vazquez, R. (2019). Barley yield and fertilization analysis from UAV imagery: A deep learning approach. International Journal of Remote Sensing, 40(7), 2493–2516.
• Esgario, J.G. Krohling, R.A., & Ventura, J.A. (2020). Deep learning for classification and severity estimation of coffee leaf biotic stress. Computers and Electronics in Agriculture, 169, 105162.
• Faiçal, B.S., Freitas, H., Gomes, P.H., Mano, L.Y., Pessin, G., de Carvalho, A.C.P.L.F., Krishnamachari, B., & Ueyama, J. (2017). An adaptive approach for UAV-based pesticide spraying in dynamic environments, Computers and Electronics in Agriculture, 138, 210–223.
• Ferreira, B., Iten, M., & Silva, R.G. (2020). Monitoring sustainable development by means of earth observation data and machine learning: a review. Environmental Sciences Europe, 32,120.
• Fu, Z., Jiang, J., Gao, Y., Krienke, B., Wang, M., Zhong, K., Cao, Q., Tian, Y., Zhu, Y., Cao, W., & Liu, X. (2020). Wheat growth monitoring and yield estimation based on multi-rotor unmanned aerial vehicle. Remote Sensing, 12(3), 508.
• Fuentes, A., Yoon, S., Kim, S.C., & Park, D.S. (2017). A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors, 17, 2022.
• García, R., Aguilar, J., Toro, M., Pinto, A., & Rodríguez, P. (2020). A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture, 179, 105826.
• Gholipoor, M., & Nadali, F. (2019). Fruit yield prediction of pepper using artificial neural network. Scientia Horticulturae, 250, 249-253.
• Gholizadeh, M., Melesse, A., & Reddi, L. (2016). Spaceborne and airborne sensors in water quality assessment. International Journal of Remote Sensing, 37, 3143-3180.
• Goedde, L., Katz, J., Ménard, A., & Revellat, J. (2020). Agriculture’s connected future: How technology can yield new growth. McKinsey and Company, https://www.mckinsey.com /industries/agriculture.
• Gómez, C., White, J., & Wulder, M. (2016). Optical remotely sensed time series data for land cover classification: A review. Journal of Photogrammetry and Remote Sensing, 116, 55-72.
• Gómez, J.E., Marcillo, F.R., Triana, F.L., Gallo, V.T., Oviedo, B.W., & Hernández, V.L. (2017). IoT for environmental variables in urban areas. Procedia Computer Science, 109, 67-74.
• Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., & Cai, J. (2018). Recent advances in convolutional neural networks. Pattern Recognition, 77, 354–377.
• Hassan M.A., Yang, M., Rasheed A., Yang, G., Reynolds, M., Xia, X., Xiao, Y., & He, Z. (2019). A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Science, 282, 95–103.
• Hemming, S., de Zwart, F., Elings, A., Righini, I., & Petropoulou, A. (2019). Remote control of greenhouse vegetable production with artificial intelligence—greenhouse climate, irrigation, and crop production. Sensors, 19(8), 1807.
• Heuvelink, G.B.M., Angelini, M.E., Poggio, L., Bai, Z., Batjes, N.H., van den Bosch, R., Bossio, D., Estella, S., Lehmann, J., Olmedo, G.F., & Sanderman, J. (2021). Machine learning in space and time for modelling soil organic carbon change. European Journal of Soil Science, 72, 1607–1623.
• Hodgson, J.C., Mott, R., Baylis, S.M., Pham, T.T., Wotherspoon, S., Kilpatrick, A.D., Segaran, R.R., Reid, L., Terauds, A., & Koh, L.P. (2018). Drones count wildlife more accurately and precisely than humans. Methods in Ecology and Evolution, 9(5), 1160-1167.
• Horng, G.-J., Liu, M.-X., & Chen, C.-C. (2020). The smart image recognition mechanism for crop harvesting system in intelligent agriculture. IEEE Sensors Journal, 20, 2766-2781.
• Huang, H., Deng, J., Lan, Y., Yang, A., Deng, X., & Zhang, L. (2018). A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery. PLoS One, 13 (4), e0196302.
• Ivushkin, K., Bartholomeus, H., Bregt A.K., Pulatov, A., Franceschini, M.H:D., Kramer, H., van Loo, E.N., Roman, V.J., &
• Finkers, R. (2019). UAV based soil salinity assessment of cropland. Geoderma, 338, 502–512.
• Javaid, M., Haleem, A., Singh, R.P., & Suman, R. (2022). Enhancing smart farming through the applications of Agriculture 4.0 technologies. International Journal of Intelligent Networks, 3, 150–164.
• 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.
• Ji, J., Zhu, X., Ma, H., Wang, H., Jin, X., & Zhao, K. (2021). Apple fruit recognition based on a deep learning algorithm using an improved lightweight network. Applied Engineering in Agriculture, 37(1), 123-134.
• 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.
• Kale, S.S., & Patil, P.S. (2019). Data mining technology with fuzzy logic, neural networks and machine learning for agriculture. In V. Balas, N. Sharma, & A. Chakrabarti (Eds.), Data management, analytics and innovation (pp.79-87), Springer, Singapore.
• Kamilaris, A., & Prenafeta-Boldú, F.X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90, 2018.
• Kavianand, M., Nivas, V.M., Kiruthika R., & Lalitha, S. (2016). Smart drip irrigation system for sustainable agriculture. 2016 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR), Chennai, India, pp. 19–22.
• Keskin, H., Grunwald, S., & Harris, W.G. (2019). Digital mapping of soil carbon fractions with machine learning. Geoderma, 339, 40–58.
• Keswani, B., Mohapatra, A.G., Mohanty, A., Khanna, A., Rodrigues, J.J.P.C., Gupta, D., & de Albuquerque, V.H.C. (2019). Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms. Neural Computing and Applications, 31, 277–292.
• Khalil, R.A., Saeed, N., Masood, M., Fard, Y.M., Alouini, M.S., & Al-Naffouri, T.Y. (2021). Deep learning in the industrial internet of things: Potentials, challenges, and emerging applications. IEEE Internet of Things Journal, 8 (14), 11016–11040.
• Kılıç, Z. (2020). The importance of water and conscious use of water. International Journal of Hydrology, 4(5), 239-241.
• Klyushin, D., & Tymoshenko, A. (2021). Optimization of drip irrigation systems using artificial intelligence methods for sustainable agriculture and environment. In: AE. Hassanien, R. Bhatnagar, & A. Darwish (Eds.). Artificial intelligence for sustainable development: Theory, practice and future applications (pp. 3-17). Springer International Publishing.
• Kodali, R.K., & Sahu, A. (2016). An IoT based soil moisture monitoring on Losant platform. 2nd International Conference on Contemporary Computing and Informatics. IEEE, pp. 764–768.
• Liang, W., Zhang, H., Zhang, G., & Cao, H. (2019). Rice blast disease recognition using a deep convolutional neural network. Scientific Report, 9, 1–10.
• Lieder, S., & Schröter-Schlaack, C. (2021). Smart farming technologies in arable farming: towards a holistic assessment of opportunities and risks. Sustainability, 13, 6783.
• Lin, T.-L., Chang, H.-Y., & Chen, K.-H. (2020). The pest and disease identification in the growth of sweet peppers using faster R-CNN and mask R-CNN. Journal of Internet Technology, 21, 605–614.
• Lingwal, S., Bhatia, K.K., & Singh, M. (2022). A novel machine learning approach for rice yield estimation. Journal of Experimental Theoretical Artificial Intelligence, https://doi.org/10.1080/0952813X.2022.2062458
• Liu, B., Zhang, Y., He, D., & Li, Y. (2018). Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry, 10, 11.
• MacPherson, J., Voglhuber-Slavinsky, A., Olbrisch, M., Schöbel, P., Dönitz, E., Mouratiadou, I., & Helming, K. (2022). Future agricultural systems and the role of digitalization for achieving sustainability goals. A review. Agronomy for Sustainable Development, 42, 70.
• Maes, W.H., & Steppe, K. (2019). Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends in Plant Science, 24 (2), 152-164.
• Mahmood, H.S., Ahmad, M., Ahmad, T., Saeed, M.A., & Iqbal, M. (2013). Potentials and prospects of precision agriculture in Pakistan-a review. Pakistan Journal of Agricultural Research, 26(2), 151–167.
• Maimaitijiang, M., Sagan, V., Sidike, P., Hartling, S., Esposito, F., & Fritschi, F.B. (2020). Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sensing of Environment, 237,111599.
• Manogaran, G., & Lopez, D. (2018). Disease surveillance system for big climate data processing and dengue transmission. In Climate Change and Environmental Concerns: Breakthroughs in Research and Practice (pp. 427-446). IGI Global.
• Maraveas, C., Piromalis, D., Arvanitis, K.G., Bartzanas, T., & Loukatos, D. (2022). Applications of IoT for optimized greenhouse environment and resources management. Computers and Electronics in Agriculture, 198, 106993.
• Martos, V., Ahmad, A., Cartujo, P., & Ordoñez, J. (2021). Ensuring agricultural sustainability through remote sensing in the era of agriculture 5.0. Applied Sciences, 11, 5911.
• Masi, M., De Rosa, M., Vecchio, Y., Bartoli, L., & Adinolfi, F. (2022). The long way to innovation adoption: Insights from precision agriculture. Agricultural and Food Economics, 10, 27.
• Mateen, A., & Qingsheng, Z. (2019). Legion based weed extraction from UAV imagery. Pakistan Journal of Agricultural Sciences, 56(4), 1057–1064.
• McNicol, G., Bulmer, C., D’Amore, D., Sanborn, P., Saunders, S., Giesbrecht, I., Arriola, S. G., Bidlack, A., Butman, D., & Buma, B. (2019). Large, climate-sensitive soil carbon stocks mapped with pedology-informed machine learning in the North Pacific coastal temperate rainforest. Environmental Research Letters, 14(1), 14004.
• Megeto, G.A.S., da Silva, A.G., Bulgarelli, R.F., Bublitz, C.F., Valente, A.C., & da Costa, D.A.G. (2020). Artificial intelligence applications in the agriculture 4.0. Revista Ciência Agronômica, 51, Special Agriculture 4.0, e20207701.
• Mishra, P., Asaari, M.S.M., Herrero-Langreo, A., Lohumi, S., Diezma, B., & Scheunders, P. (2017). Close range hyperspectral imaging of plants: A review. Biosystems Engineering, 164, 49–67.
• Mohanraj, I., Ashokumar, K., & Naren, J. (2016). Field monitoring and automation using IoT in agriculture domain. Procedia Computer Science, 93, 931-939.
• Morellos, A., Pantazi, X.-E., Moshou, D., Alexandridis, T., Whetton, R., Tziotzios, G., Wiebensohn, J., Bill, R., & Mouazen, A.M. (2016). Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy. Biosystems Engineering, 152, 104–116.
• Muhammad, M.N., Wayayok, A., Shariff, A.R.M., Abdullah, A.F., & Husin, E.M. (2019). Droplet deposition density of organic liquid fertilizer at low altitude UAV aerial spraying in rice cultivation. Computers and Electronics in Agriculture, 167, 105045.
• Natu, A.S., & Kulkarni, S. (2016). Adoption and utilization of drones for advanced precision farming: A review. International Journal on Recent and Innovation Trends in Computing and Communication, 4, 563–565.
• Nevavuori, P., Narra, N., & Lipping, T. (2019). Crop yield prediction with deep convolutional neural networks. Computers and Electronics in Agriculture, 163, 104859.
• Ng, W., Minasny, B., Montazerolghaem, M., Padarian, J., Ferguson, R., Bailey, S., & McBratney, A.B. (2019). Convolutional neural network for simultaneous prediction of several soil properties using visible/near-infrared, mid-infrared, and their combined spectra. Geoderma, 352, 251–267.
• Nguyen, Q.T., Fouchereau, R., Frénod, E., Gerard, C., & Sincholle, V. (2020). Comparison of forecast models of production of dairy cows combining animal and diet parameters. Computers and Electronics in Agriculture, 170, 105258.
• Nie, X., Wang, L., Ding, H., & Xu, M. (2019). Strawberry verticillium wilt detection network based on multi-task learning and attention. IEEE Access, 7, 170003–170011.
• Norton, T., Chen, C., Larsen, M.L.V., & Berckmans, D. (2019). Review: precision livestock farming: building “digital representations” to bring the animals closer to the farmer. Animal, 3, 3009–3017.
• OECD, (2022). Digital innovations and the growing importance of agricultural data. OECD Publishing, Paris.
• Özgen, H., & Turan M. (2021). Sulama/ilaçlama robotu için nesne tanıma çalışmaları. Avrupa Bilim ve Teknoloji Dergisi, (Special Issue), 25-33.
• Öztürk, E., Çelik, Y., & Kırcı, P. (2021). Akıllı tarımda sensör uygulaması. Avrupa Bilim ve Teknoloji Dergisi, 28, 1279-1282.
• Padarian, J., Minasny, B., & McBratney, A.B. (2020). Machine learning and soil sciences: a review aided by machine learning tools. Soil, 6(1), 35–52.
• Park, S., Ryu, D., Fuentes, S., Chung, H., Hernández-Montes, E., & O’Connell, M. (2017). Adaptive estimation of crop water stress in nectarine and peach orchards using high-resolution imagery from an unmanned aerial vehicle (UAV). Remote Sensing, 9(8), 828.
• Partel, V., Kakarla, S.C, & Ampatzidis, Y. (2019). Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Computers and Electronics in Agriculture, 157, 339–350.
• 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.
• Paustian, M., & Theuvsen, L. (2017). Adoption of precision agriculture technologies by German crop farmers. Precision Agriculture, 18, 701–716.
• Pham, T.D., Yokoya, N., Nguyen, T.T.T., Le, N.N., Ha, N.T., Xia, J., Takeuchi, W., & Pham, T.D. (2021). Improvement of mangrove soil carbon stocks estimation in North Vietnam using Sentinel-2 data and machine learning approach. GIScience and Remote Sensing, 58(1), 68–87.
• Pincheira, M., Vecchio, M., Giaffreda, R., & Kanhere, S.S. (2021). Cost-effective IoT devices as trustworthy data sources for a blockchain-based water management system in precision agriculture. Computers and Electronics in Agriculture, 180, 105889.
• Pivoto, D., Waquil, P.D., Talamini, E., Finocchio, C.P.S., Corte, V.F.D., & de Vargas Mores, G. (2018). Scientific development of smart farming technologies and their application in Brazil. Information Processing in Agriculture, 5(1), 21-32.
• Qazi, S., Khawaja, B.A., & Farooq, Q.U. (2022). IoT-equipped and AI-enabled next generation smart agriculture: A critical review, current challenges and future trends. IEEE Access, 10, 21219-21235.
• Qureshi, T., Saeed, M., Ahsan, K., & Malik, A.A. (2022). Smart agriculture for sustainable food security using internet of Things (IoT). Wireless Communications and Mobile Computing, 2022, 9608394.
• Rani, A., Chaudhary, A., Sinha, N., Mohanty, M., & Chaudhary, R. (2019). Drone: The green technology for future agriculture. Harit Dhara, 2, 3–6.
• Ray, P. (2018). A survey on internet of things architectures. Journal of King Saud University - Computer and Information Sciences, 30(3), 291-319.
• Ren, Q., Zhang, R., Cai, W., Sun, X., & Cao, L. (2020). Application and development of new drones in agriculture. IOP Conference Series: Earth and Environmental Science, 440(5), 052041.
• Rose, D.C., & Chilvers, J. (2018). Agriculture 4.0: Broadening responsible innovation in an era of smart farming. Frontiers in Sustainable Food Systems, 2, 87.
• Roth, L., Aasen, H., Walter, A., & Liebisch, F. (2018). Extracting leaf area index using viewing geometry effects- A new perspective on high-resolution unmanned aerial system photography. ISPRS Journal of Photogrammetry and Remote Sensing, 141, 161-175.
• Ryan, M., Isakhanyan, G., Tekinerdogan, B. (2023). An interdisciplinary approach to artificial intelligence in agriculture. NJAS: Impact in Agricultural and Life Sciences, 95, 2168568.
• Sa, I., Chen Z., Popovi, M., Khanna, R., Liebisch, F., Nieto, J., & Siegwart, R. (2018). WeedNet: Dense semantic weed classification using multispectral images and mav for smart farming. IEEE Robotics and Automation Letters, 3 (1), 588–595.
• Saiz-Rubio, V., & Rovira-Más, F. (2020). From smart farming towards agriculture 5.0: A review on crop data management. Agronomy, 10(207), 1–21.
• Sanderman, J., Savage, K., & Dangal, S.R.S. (2019). Mid-infrared spectroscopy for prediction of soil health indicators in the United States. Soil Science Society of America Journal, 84, 251–261.
• Saranya, T., Deisy, C., Sridevi, S., & Anbananthen, K.S.M. (2023). A comparative study of deep learning and Internet of Things for precision agriculture. Engineering Applications of Artificial Intelligence, 122, 106034.
• Savitha, M., & UmaMaheshwari, O.P. (2018). Smart crop field irrigation in IOT architecture using sensors. International Journal of Advanced Research in Computer Science, 9(1), 302–306.
• Schillings, J., Bennett, R., & Rose, D.C. (2021). Exploring the potential of precision livestock farming technologies to help address farm animal welfare. Frontiers in Animal Science, 2, 639678.
• Shaikh, T.A., Rasool, T., & Lone, F.R. (2022). Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture, 198, 107119.
• Sharma, A., Jain, A., Gupta, P., Chowdary, V. (2021). Machine learning applications for precision agriculture: A comprehensive review. IEEE Access, 9, 4843-4873.
• Shekhar, Y., Dagur, E., Mishra, S., Tom, R.J., Veeramanikandan, M., & Sankaranarayanan, S. (2017). Intelligent IoT based automated irrigation system. International Journal of Applied Engineering Research, 12(18), 7306–7320.
• Shine, P., Murphy, M.D., Upton, J., & Scully, T. (2018). Machine-learning algorithms for predicting on-farm direct water and electricity consumption on pasture based dairy farms. Computers and Electronics in Agriculture, 150, 74–87.
• Sishodia, R.P., Ray, R.L., & Singh, S.K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12, 3136.
• Song, X.P., Li, H., Potapov, P., & Hansen, M.C. (2022). Annual 30 m soybean yield mapping in Brazil using long-term satellite observations, climate data and machine learning. Agricultural and Forest Meteorology, 326, 109186.
• Song, X.-P., Liang, Y.-J., Zhang, X.-Q., Qin, Z.-Q., Wei, J.-J., Li, Y.-R., & Wu, J.-M. (2020). Intrusion of fall armyworm (Spodoptera frugiperda) in sugarcane and its control by drone in China. Sugar Tech, 22, 734–737.
• Spachos, P., & Gregori, S. (2019). Integration of wireless sensor networks and smart UAVs for precision viticulture. IEEE Internet Computing, 23(3), 8-16.
• 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.
• Steele-Dunne, S.C., McNairn, H., Monsivais-Huertero, A., Judge, J., Liu, P.-W., & Papathanassiou, K. (2017). Radar remote sensing of agricultural canopies: A review. EEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10, 2249–2273.
• Subeesh, A., & Mehta, C.R. (2021). Automation and digitization of agriculture using artificial intelligence and internet of things. Artificial Intelligence in Agriculture, 5, 278-291.
• Suganya, E., Sountharrajan, S., Shandilya, S.K., & Karthiga, M. (2019). Chapter 5- IoT in agriculture investigation on plant diseases and nutrient level using image analysis techniques. In V.E. Balas, L.H. Son, S. Jha, M. Khari, R. Kumar (Eds.), Internet of Things in biomedical engineering (pp.117–130). Academic Press.
• Sujaritha, M., Annadurai, S., Satheeshkumar, J., Kowshik Sharan, S., & Mahesh, L. (2017). Weed detecting robot in sugarcane fields using fuzzy real time classifier. Computers and Electronics in Agriculture, 134, 160–171.
• Sukhadia, A., Upadhyay, K., Gundeti, M., Shah, S., & Shah, M. (2020). Optimization of smart traffic governance system using artificial intelligence. Augmented Human Research, 5, 13.
• Sun, Y., Yi, S., Hou, F., Luo, D., Hu, J., & Zhou, Z. (2020). Quantifying the dynamics of livestock distribution by unmanned aerial vehicles (UAVs): A case study of yak grazing at the household scale. Rangeland Ecology and Management, 73, 642–648.
• Talaviya, T., Shah, D., Patel, N., Yagnik, H., & Shah, M. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, 4, 58–73.
• Tao, W., Zhao, L., Wang, G., & Liang, R. (2021). Review of the internet of things communication technologies in smart agriculture and challenges. Computers and Electronics in Agriculture, 189, 106352.
• Tsai, D.M., & Huang, C.Y. (2014). A motion and image analysis method for automatic detection of estrus and mating behavior in cattle. Computers and Electronics in Agriculture, 104, 25–31.
• Vanegas, F., Bratanov, D., Powell, K., Weiss, J., & Gonzalez, F. (2018). A novel methodology for improving plant pest surveillance in vineyards and crops using UAV-based hyperspectral and spatial data. Sensors, 18(1), 260, 2018.
• Veroustraete, F. (2015). The rise of the drones in agriculture. Ecronicon, 2 (2), 1–3.
• Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018). Deep learning for computer vision: A brief review. Computational Intelligence and Neuroscience, 2018, 7068349.
• Weiss, M., Jacob, F., & Duveiller, G. (2020). Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment, 236,111402.
• Wolfert, S., & Isakhanyan, G. (2022). Sustainable agriculture by the Internet of Things – A practitioner’s approach to monitor sustainability progress. Computers and Electronics in Agriculture, 200, 107226.
• Yamamoto, K., Guo, W., Yoshioka, Y., & Ninomiya, S. (2014). On plant detection of intact tomato fruits using image analysis and machine learning methods. Sensors, 14(7), 12191-
• Zannou, J.G.N., & Houndji, V.R. (2019). Sorghum yield prediction using machine learning. 3rd International Conference on Bio-engineering for Smart Technologies, 24-26 April 2019, Paris, France.
• Zhang, C., & Kovacs, J.M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13(6), 693–712.
• Zhang, S. Chen, X., & Wang, S. (2014). Research on the monitoring system of wheat diseases, pests and weeds based on IoT. 9th International Conference on Computer Science Education, 22-24 August 2014, Vancouver, BC, Canada, pp. 981–985.
• Zhang, T., Su, J., Liu, C., & Chen, W.-H. (2019). Bayesian calibration of AquaCrop model for winter wheat by assimilating UAV multi-spectral images. Computers and Electronics in Agriculture, 167, 105052.
• Zhang, J., Karkee, M., Zhang, Q., Zhang, X., Yaqoob, M., Fu, L., & Wang, S. (2020). Multi-class object detection using faster R-CNN and estimation of shaking locations for automated shake-and-catch apple harvesting. Computers and Electronics in Agriculture, 173, 105384.
• Zheng, C., Abd-Elrahman, A., & Whitaker, V. (2021). Remote sensing and machine learning in crop phenotyping and management, with an emphasis on applications in strawberry farming. Remote Sensing, 13, 531.
• Zhou, X., Zheng, H., Xu, X., He, J., Ge, X., Yao, X., Cheng, T., Zhu, Y., Cao, W., & Tian, Y. (2017). Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 130, 246 – 255.
• Zhou, Y., Xia, Q, Zhang, Z., Quan, M., & Li, H. (2022). Artificial intelligence and machine learning for the green development of agriculture in the emerging manufacturing industry in the IoT platform. Acta Agriculturae Scandinavica, Section B-Soil & Plant Science, 72 (1), 284-299.
• Zhuang, X., Bi, M., Guo, J., Wu, S., & Zhang, T. (2018). Development of an early warning algorithm to detect sick broilers. Computers and Electronics in Agriculture, 144, 102–113.