Akıllı Tarım Teknolojileri ve Uygulamaları
Özet
Tarım binlerce yıldır medeniyeti ayakta tutan önemli bir insan faaliyeti olmuştur. Nüfustaki hızlı artış ve buna bağlı olarak gıda talebinin artmasıyla birlikte, tarım uygulamalarını bu talebi karşılayacak şekilde
optimize etmek giderek daha önemli hale gelmiştir. Son yıllarda, teknolojiyi tarım uygulamalarına entegre eden, çiftçilerin ürün verimini optimize etmesine, israfı azaltmasına ve verimliliği artırmasına olanak tanıyan ve “akıllı tarım” olarak bilinen yenilikçi bir tarım yaklaşımı ortaya çıkmıştır. Öte yandan akıllı tarımın temel bileşenlerinden yapay zekâ, makine öğrenimi, derin öğrenme, nesnelerin interneti (IoT), bulut bilişim gibi teknolojilerin gelişimi, tarımın verimliliğini artırmak, iklim değişikliği ile başa çıkmak ve kaynakları daha etkili bir şekilde kullanmak için önemli bir rol oynamakta ve bu teknolojilerin bir araya gelmesi, tarımın geleceğini şekillendirmektedir. Bu çalışma, tarım sektörünü geleceğe taşıyan bir dönüşümün parçası olarak karşımıza çıkan akıllı tarımın, tarımsal üretimdeki uygulamaları, iklim değişikliği ile mücadele, su kaynaklarının etkili kullanımı ve çiftçi kararlarını nasıl etkilediği detaylı bir şekilde ele alınmakta ve bilişim teknolojilerinin tarımsal sistemlere entegre edilmesinin tarım sektöründe yarattığı etkiyi incelemektedir.
Referanslar
Russell Stuart J and Norvig P, Artificial Intelligence: A Modern Approach. New Jersey: Prentice Hall; 2009.
McCarthy J, Minsky ML, Rochester N, et al., A proposal for the dartmouth summer research project on artificial intelligence. AI Magazine. 2006;27(4): 11-12. doi: https://doi.org/10.1609/aimag.v27i4.1904
Simon HA, Artificial intelligence: an empirical science. Artificial intelligence. 1995;77(1): 95-127.
Moschona DS. An Affective Service based on Multi-Modal Emotion Recognition, using EEG enabled Emotion Tracking and Speech Emotion Recognition. in 2020 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia). 2020. (pp. 1-3). doi: https://doi.org/10.1109/ICCE-Asia49877.2020.9277291.
Campbell M, Hoane Jr AJ, and Hsu F-h, Deep blue. Artificial intelligence. 2002;134(1-2): 57-83.
Robertson S, Azizpour H, Smith K, et al., Digital image analysis in breast pathology—from image processing techniques to artificial intelligence. Translational Research. 2018;194: 19-35.
Stoitsis J, Valavanis I, Mougiakakou SG, et al., Computer aided diagnosis based on medical image processing and artificial intelligence methods. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2006;569(2): 591-595.
Savadjiev P, Chong J, Dohan A, et al., Demystification of AI-driven medical image interpretation: past, present and future. European radiology. 2019;29: 1616-1624.
Karadöl H, Arslan S, and Gizlenci İ, Makine Görüsü Kullanarak Tarla Pülverizatöründe Bir Nokta Püskürtme Sisteminin Geliştirilmesi. Tarım Makinaları Bilimi Dergisi. 2018;14(1): 31-40.
Ma Y, Wang Z, Yang H, et al., Artificial intelligence applications in the development of autonomous vehicles: A survey. IEEE/CAA Journal of Automatica Sinica. 2020;7(2): 315-329.
Di X and Shi R, A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning. Transportation research part C: emerging technologies. 2021;125: 103008.
Fujita H, AI-based computer-aided diagnosis (AI-CAD): the latest review to read first. Radiological physics and technology. 2020;13(1): 6-19.
Jussupow E, Spohrer K, Heinzl A, et al., Augmenting medical diagnosis decisions? An investigation into physicians’ decision-making process with artificial intelligence. Information Systems Research. 2021;32(3): 713-735.
Mhlanga D, Artificial intelligence in the industry 4.0, and its impact on poverty, innovation, infrastructure development, and the sustainable development goals: Lessons from emerging economies? Sustainability. 2021;13(11): 5788.
Lin TCW, Artificial intelligence, finance, and the law. Fordham L. Rev. 2019;88: 531.
Shaikh TA, Rasool T, and Lone FR, Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture. 2022;198: 107119.
Yaslıoğlu E and Şimşek E, The Status ff Livestock Production in Terms ff Global Warming and Its Future Perspective, in Pioneer And Contemporary Studies in Agriculture, Forest and Water Issues. 2023. p. 69-82.
Said Mohamed E, Belal AA, Kotb Abd-Elmabod S, et al., Smart farming for improving agricultural management. The Egyptian Journal of Remote Sensing and Space Science. 2021;24(3, Part 2): 971-981. doi: https://doi.org/10.1016/j.ejrs.2021.08.007.
Lobell DB and Field CB, Global scale climate–crop yield relationships and the impacts of recent warming. Environmental research letters. 2007;2(1): 014002.
Muniasamy A. Machine Learning for Smart Farming: A Focus on Desert Agriculture. in 2020 International Conference on Computing and Information Technology (ICCIT-1441). 2020. (pp. 1-5). doi: 10.1109/ICCIT-144147971.2020.9213759.
Ahmed RA, Hemdan EED, El‐Shafai W, et al., Climate‐smart agriculture using intelligent techniques, blockchain and Internet of Things: Concepts, challenges, and opportunities. Transactions on Emerging Telecommunications Technologies. 2022;33(11): e4607.
Tao F, Yokozawa M, Liu J, et al., Climate–crop yield relationships at provincial scales in China and the impacts of recent climate trends. Climate Research. 2008;38(1): 83-94.
Dunnett A, Shirsath PB, Aggarwal PK, et al., Multi-objective land use allocation modelling for prioritizing climate-smart agricultural interventions. Ecological modelling. 2018;381: 23-35.
Tanriverdi C, Degirmenci H, and Sesveren S, Assessment of irrigation schemes in Turkey based on management types. African Journal of Biotechnology. 2011;10(11): 1997-2004.
Sesveren S and Karakaya FG, Kartalkaya sol sahil sulama birliği bazı performans göstergeleri, sulama problemleri ve çözüm önerileri. Journal of the Institute of Science and Technology. 2019;9(1): 76-84.
Perea RG, Water and energy demand forecasting in large-scale water distribution networks for irrigation using open data and machine learning algorithms. Computers and Electronics in Agriculture. 2021;188: 106327.
Simunek J, Sejna M, Van Genuchten MT, et al., HYDRUS-1D. Simulating the one-dimensional movement of water, heat, and multiple solutes in variably-saturated media, version. 1998;2.
Roy SK, Misra S, Raghuwanshi NS, et al., AgriSens: IoT-based dynamic irrigation scheduling system for water management of irrigated crops. IEEE Internet of Things Journal. 2020;8(6): 5023-5030.
Pardossi A, Incrocci L, Incrocci G, et al., Root zone sensors for irrigation management in intensive agriculture. Sensors. 2009;9(4): 2809-2835.
Machado MR, Júnior TR, Silva MR, et al. Smart water management system using the microcontroller ZR16S08 as IoT solution. in 2019 IEEE 10th Latin American Symposium on Circuits & Systems (LASCAS). IEEE. 2019. (pp. 169-172).
Kamienski C, Soininen J-P, Taumberger M, et al., Smart water management platform: IoT-based precision irrigation for agriculture. Sensors. 2019;19(2): 276.
Xie T, Huang Z, Chi Z, et al. Minimizing amortized cost of the on-demand irrigation system in smart farms. in Proceedings of the 3rd International Workshop on Cyber-Physical Systems for Smart Water Networks. 2017. (pp. 43-46).
Chen H, Chen A, Xu L, et al., A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources. Agricultural Water Management. 2020;240: 106303.
Subashini MM, Das S, Heble S, et al., Internet of things based wireless plant sensor for smart farming. Indonesian Journal of Electrical Engineering and Computer Science. 2018;10(2): 456-468.
Sureephong P, Wiangnak P, and Wicha S. The comparison of soil sensors for integrated creation of IOT-based Wetting front detector (WFD) with an efficient irrigation system to support precision farming. in 2017 International Conference on Digital Arts, Media and Technology (ICDAMT). IEEE. 2017. (pp. 132-135).
Guo Y, Zhang J, Yin C, et al., Plant disease identification based on deep learning algorithm in smart farming. 2020;2020: 1-11.
Khirade SD and Patil A. Plant disease detection using image processing. in 2015 International conference on computing communication control and automation. IEEE. 2015. (pp. 768-771).
Mehra T, Kumar V, and Gupta P. Maturity and disease detection in tomato using computer vision. in 2016 Fourth international conference on parallel, distributed and grid computing (PDGC). IEEE. 2016. (pp. 399-403).
Richard B, Qi A, and Fitt BD, Control of crop diseases through Integrated Crop Management to deliver climate‐smart farming systems for low‐and high‐input crop production. Plant Pathology. 2022;71(1): 187-206.
Potamitis I, Eliopoulos P, and Rigakis I, Automated remote insect surveillance at a global scale and the internet of things. Robotics. 2017;6(3): 19.
Rustia DJA and Lin T-T, An IoT-based wireless imaging and sensor node system for remote greenhouse pest monitoring. Chemical Engineering Transactions. 2017;58: 601-606.
Kapoor A, Bhat SI, Shidnal S, et al. Implementation of IoT (Internet of Things) and Image processing in smart agriculture. in 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS). IEEE. 2016. (pp. 21-26).
Foughali K, Fathallah K, and Frihida A, Using Cloud IOT for disease prevention in precision agriculture. Procedia computer science. 2018;130: 575-582.
Mohanraj I, Ashokumar K, and Naren J, Field monitoring and automation using IOT in agriculture domain. Procedia Computer Science. 2016;93: 931-939.
Ma D, Ding Q, Li Z, et al., Prototype of an aquacultural information system based on internet of things E-Nose. Intelligent Automation & Soft Computing. 2012;18(5): 569-579.
Boyacı S, Akyüz A, and Tanriverdi Ç, Comparison of heat requirements in greenhouses for Kirşehir and Kahramanmaraş provinces. Infrastruktura i Ekologia Terenów Wiejskich. 2022: 5-20.
Boyacı S, Abacı-Bayar AA, Başpınar A, et al., Kırşehir İlindeki Bazı Seralarda Yetiştirilen Bitkilerin Beslenme Durumlarının Toprak ve Yaprak Analizleri ile Değerlendirilmesi. Atatürk Üniversitesi Ziraat Fakültesi Dergisi. 2021;52(3): 273-287. doi: https://doi.org/10.17097/ataunizfd.887348.
Yaslıoğlu E and Durmuş S, Bursa ilinde yetiştiricilik yapılan seraların yapısal yönden değerlendirilmesi. Journal of Agricultural Faculty of Gaziosmanpaşa University (JAFAG). 2017;34(Ek Sayı): 164-171.
Boyacı S, Başpınar A, Atilgan A, et al., Determination of the Vertical Distribution Pattern of Indoor Climate Parameters in the Greenhouse Heated in the Winter Period. Rocznik Ochrona Środowiska. 2023;25: 105-115. doi: https://doi.org/10.54740/ros.2023.011.
Cayli A, Temperature and relative humidity spatial variability: An assessment of the environmental conditions inside greenhouses. Fresenius Environmental Bulletin. 2020;29(7): 4954-4962.
Lamprinos I and Charalambides M, Experimental assessment of ZigBee as the communication technology of a wireless sensor network for greenhouse monitoring. International Journal of Advanced Smart Sensor Network Systems. 2015;6.
García L, Parra L, Jimenez JM, et al., IoT-based smart irrigation systems: An overview on the recent trends on sensors and IoT systems for irrigation in precision agriculture. Sensors. 2020;20(4): 1042.
Chieochan O, Saokaew A, and Boonchieng E. IOT for smart farm: A case study of the Lingzhi mushroom farm at Maejo University. in 2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE. 2017. (pp. 1-6).
Pooja S, Uday D, Nagesh U, et al. Application of MQTT protocol for real time weather monitoring and precision farming. in 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT). IEEE. 2017. (pp. 1-6).
Jayaraman PP, Yavari A, Georgakopoulos D, et al., Internet of things platform for smart farming: Experiences and lessons learnt. Sensors. 2016;16(11): 1884.
Kalathas J, Bandekas D, Kosmidis A, et al., Seedbed based on IoT: A Case Study. Journal of Engineering Science & Technology Review. 2016;9(2).
Çaylı A, Akyüz A, Baytorun AN, et al., Control of Greenhouse Environmental Conditions with IOT Based Monitoring and Analysis System. Turkish Journal of Agriculture-Food Science and Technology. 2017;5(11): 1279-1289.
Banhazi T, Lehr H, Black J, et al. Precision livestock farming: Scientific concepts and commercial reality. in Proceedings of the XVth International Congress on Animal Hygiene: Animal Hygiene and Sustainable Livestock Production (ISAH 2011). University of Southern Queensland. 2011. (pp. 137-143).
Castrignanò A and Buttafuoco G, Chapter 3 - Data processing, in Agricultural Internet of Things and Decision Support for Precision Smart Farming, A. Castrignanò, et al., Editors. 2020, Academic Press. p. 139-182.doi: https://doi.org/10.1016/B978-0-12-818373-1.00003-2.
Naud O, Taylor J, Colizzi L, et al., Chapter 4 - Support to decision-making, in Agricultural Internet of Things and Decision Support for Precision Smart Farming, A. Castrignanò, et al., Editors. 2020, Academic Press. p. 183-224.doi: https://doi.org/10.1016/B978-0-12-818373-1.00004-4.
Sun Z, Zheng F, and Yin S, Perspectives of research and application of Big Data on smart agriculture. Journal of Agricultural Science and Technology (Beijing). 2013;15(6): 63-71.
Çaylı A, Makine Öğrenimi (ML) ve Tarımdaki Uygulamaları, in Biyosistem Mühendisliği III, A. Atılgan, et al., Editors. 2022, Akademisyen Yayınevi Ankara. p. 198.doi: 10.37609/akya.1414.
Cambra Baseca C, Sendra S, Lloret J, et al., A Smart Decision System for Digital Farming. Agronomy. 2019;9(5): 216.
Suakanto S, Engel VJL, Hutagalung M, et al. Sensor networks data acquisition and task management for decision support of smart farming. in 2016 International Conference on Information Technology Systems and Innovation (ICITSI). 2016. (pp. 1-5). doi: 10.1109/ICITSI.2016.7858233.
Debauche O, Mahmoudi S, Andriamandroso ALH, et al., Cloud services integration for farm animals’ behavior studies based on smartphones as activity sensors. Journal of Ambient Intelligence and Humanized Computing. 2019;10: 4651-4662.
Halachmi I and Guarino M, Precision livestock farming: a ‘per animal’approach using advanced monitoring technologies. Animal. 2016;10(9): 1482-1483.
Rowe E, Dawkins MS, and Gebhardt-Henrich SG, A systematic review of precision livestock farming in the poultry sector: Is technology focussed on improving bird welfare? Animals. 2019;9(9): 614.
Garcia R, Aguilar J, Toro M, et al., A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture. 2020;179: 105826.
di Virgilio A, Morales JM, Lambertucci SA, et al., Multi-dimensional Precision Livestock Farming: A potential toolbox for sustainable rangeland management. PeerJ. 2018;6: e4867.
Beukes P, McCarthy S, Wims C, et al., Regular estimates of herbage mass can improve profitability of pasture-based dairy systems. Animal Production Science. 2019;59(2): 359-367.
Lussem U, Schellberg J, and Bareth G, Monitoring forage mass with low-cost UAV data: case study at the Rengen grassland experiment. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science. 2020;88: 407-422.
Schaefer MT and Lamb DW, A combination of plant NDVI and LiDAR measurements improve the estimation of pasture biomass in tall fescue (Festuca arundinacea var. Fletcher). Remote Sensing. 2016;8(2): 109.
Oliveira RA, Näsi R, Niemeläinen O, et al., Machine learning estimators for the quantity and quality of grass swards used for silage production using drone-based imaging spectrometry and photogrammetry. Remote Sensing of Environment. 2020;246: 111830.
Meen G, Schellekens M, Slegers M, et al., Sound analysis in dairy cattle vocalisation as a potential welfare monitor. Computers and Electronics in Agriculture. 2015;118: 111-115.
Andriamandroso A, Bindelle J, Mercatoris B, et al., A review on the use of sensors to monitor cattle jaw movements and behavior when grazing. Biotechnologie, Agronomie, Société et Environnement. 2016;20.
Carpentier L, Vranken E, Berckmans D, et al., Development of sound-based poultry health monitoring tool for automated sneeze detection. Computers and electronics in agriculture. 2019;162: 573-581.
Bahlo C, Dahlhaus P, Thompson H, et al., The role of interoperable data standards in precision livestock farming in extensive livestock systems: A review. Computers and electronics in agriculture. 2019;156: 459-466.
Yaslıoğlu E and İlhan H, Güney Marmara Süt Sığırı Yetiştiriciliğinin Isı Stresi Yönünden Değerlendirilmesi. Tekirdağ Ziraat Fakültesi Dergisi. 2016;13(4): 12-19.
Boyacı S, Etlik Piliç Kümeslerinde, Isıtma ve Soğutma Derece Gün Değerlerinin Derece Gün Yöntemiyle Belirlenmesi: Kırşehir İli Örneği. Nevşehir Bilim ve Teknoloji Dergisi. 2018;7(1): 75-82.
Boyaci S, Akyüz A, and Kükürtcü M, Büyükbaş hayvan barınaklarında gübrenin yarattığı çevre kirliliği ve çözüm olanakları. Tarım Bilimleri Araştırma Dergisi. 2011;(1): 49-55.
Yaslıoğlu E and Türkmen E, IPARD Destekli Süt Sığırcılığı İşletmelerinde İç Ortam İklim Parametrelerinin Analizi (Bursa Karacabey Örneği). Journal of Agricultural Faculty of Gaziosmanpaşa University (JAFAG). 2017;34(Ek Sayı): 41-49.
Yaslıoglu E, İlhan H, Şimşek E, et al., Spatial and Temporal Variatıons of Temperature Humidity Index (THI) For Laying Hens in the Marmara Regıon, Turkey. Fresenius Environmental Bulletin. 2018;27(11): 7503-7509.
Bezen R, Edan Y, and Halachmi I, Computer vision system for measuring individual cow feed intake using RGB-D camera and deep learning algorithms. Computers and electronics in agriculture. 2020;172: 105345.
Rodenburg J, Robotic milking: Technology, farm design, and effects on work flow. Journal of Dairy Science. 2017;100(9): 7729-7738.
John A, Clark C, Freeman M, et al., Milking robot utilization, a successful precision livestock farming evolution. Animal. 2016;10(9): 1484-1492.
O’Mahony N, Campbell S, Carvalho A, et al., 3D vision for precision dairy farming. IFAC-PapersOnLine. 2019;52(30): 312-317.
Referanslar
Russell Stuart J and Norvig P, Artificial Intelligence: A Modern Approach. New Jersey: Prentice Hall; 2009.
McCarthy J, Minsky ML, Rochester N, et al., A proposal for the dartmouth summer research project on artificial intelligence. AI Magazine. 2006;27(4): 11-12. doi: https://doi.org/10.1609/aimag.v27i4.1904
Simon HA, Artificial intelligence: an empirical science. Artificial intelligence. 1995;77(1): 95-127.
Moschona DS. An Affective Service based on Multi-Modal Emotion Recognition, using EEG enabled Emotion Tracking and Speech Emotion Recognition. in 2020 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia). 2020. (pp. 1-3). doi: https://doi.org/10.1109/ICCE-Asia49877.2020.9277291.
Campbell M, Hoane Jr AJ, and Hsu F-h, Deep blue. Artificial intelligence. 2002;134(1-2): 57-83.
Robertson S, Azizpour H, Smith K, et al., Digital image analysis in breast pathology—from image processing techniques to artificial intelligence. Translational Research. 2018;194: 19-35.
Stoitsis J, Valavanis I, Mougiakakou SG, et al., Computer aided diagnosis based on medical image processing and artificial intelligence methods. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2006;569(2): 591-595.
Savadjiev P, Chong J, Dohan A, et al., Demystification of AI-driven medical image interpretation: past, present and future. European radiology. 2019;29: 1616-1624.
Karadöl H, Arslan S, and Gizlenci İ, Makine Görüsü Kullanarak Tarla Pülverizatöründe Bir Nokta Püskürtme Sisteminin Geliştirilmesi. Tarım Makinaları Bilimi Dergisi. 2018;14(1): 31-40.
Ma Y, Wang Z, Yang H, et al., Artificial intelligence applications in the development of autonomous vehicles: A survey. IEEE/CAA Journal of Automatica Sinica. 2020;7(2): 315-329.
Di X and Shi R, A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning. Transportation research part C: emerging technologies. 2021;125: 103008.
Fujita H, AI-based computer-aided diagnosis (AI-CAD): the latest review to read first. Radiological physics and technology. 2020;13(1): 6-19.
Jussupow E, Spohrer K, Heinzl A, et al., Augmenting medical diagnosis decisions? An investigation into physicians’ decision-making process with artificial intelligence. Information Systems Research. 2021;32(3): 713-735.
Mhlanga D, Artificial intelligence in the industry 4.0, and its impact on poverty, innovation, infrastructure development, and the sustainable development goals: Lessons from emerging economies? Sustainability. 2021;13(11): 5788.
Lin TCW, Artificial intelligence, finance, and the law. Fordham L. Rev. 2019;88: 531.
Shaikh TA, Rasool T, and Lone FR, Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture. 2022;198: 107119.
Yaslıoğlu E and Şimşek E, The Status ff Livestock Production in Terms ff Global Warming and Its Future Perspective, in Pioneer And Contemporary Studies in Agriculture, Forest and Water Issues. 2023. p. 69-82.
Said Mohamed E, Belal AA, Kotb Abd-Elmabod S, et al., Smart farming for improving agricultural management. The Egyptian Journal of Remote Sensing and Space Science. 2021;24(3, Part 2): 971-981. doi: https://doi.org/10.1016/j.ejrs.2021.08.007.
Lobell DB and Field CB, Global scale climate–crop yield relationships and the impacts of recent warming. Environmental research letters. 2007;2(1): 014002.
Muniasamy A. Machine Learning for Smart Farming: A Focus on Desert Agriculture. in 2020 International Conference on Computing and Information Technology (ICCIT-1441). 2020. (pp. 1-5). doi: 10.1109/ICCIT-144147971.2020.9213759.
Ahmed RA, Hemdan EED, El‐Shafai W, et al., Climate‐smart agriculture using intelligent techniques, blockchain and Internet of Things: Concepts, challenges, and opportunities. Transactions on Emerging Telecommunications Technologies. 2022;33(11): e4607.
Tao F, Yokozawa M, Liu J, et al., Climate–crop yield relationships at provincial scales in China and the impacts of recent climate trends. Climate Research. 2008;38(1): 83-94.
Dunnett A, Shirsath PB, Aggarwal PK, et al., Multi-objective land use allocation modelling for prioritizing climate-smart agricultural interventions. Ecological modelling. 2018;381: 23-35.
Tanriverdi C, Degirmenci H, and Sesveren S, Assessment of irrigation schemes in Turkey based on management types. African Journal of Biotechnology. 2011;10(11): 1997-2004.
Sesveren S and Karakaya FG, Kartalkaya sol sahil sulama birliği bazı performans göstergeleri, sulama problemleri ve çözüm önerileri. Journal of the Institute of Science and Technology. 2019;9(1): 76-84.
Perea RG, Water and energy demand forecasting in large-scale water distribution networks for irrigation using open data and machine learning algorithms. Computers and Electronics in Agriculture. 2021;188: 106327.
Simunek J, Sejna M, Van Genuchten MT, et al., HYDRUS-1D. Simulating the one-dimensional movement of water, heat, and multiple solutes in variably-saturated media, version. 1998;2.
Roy SK, Misra S, Raghuwanshi NS, et al., AgriSens: IoT-based dynamic irrigation scheduling system for water management of irrigated crops. IEEE Internet of Things Journal. 2020;8(6): 5023-5030.
Pardossi A, Incrocci L, Incrocci G, et al., Root zone sensors for irrigation management in intensive agriculture. Sensors. 2009;9(4): 2809-2835.
Machado MR, Júnior TR, Silva MR, et al. Smart water management system using the microcontroller ZR16S08 as IoT solution. in 2019 IEEE 10th Latin American Symposium on Circuits & Systems (LASCAS). IEEE. 2019. (pp. 169-172).
Kamienski C, Soininen J-P, Taumberger M, et al., Smart water management platform: IoT-based precision irrigation for agriculture. Sensors. 2019;19(2): 276.
Xie T, Huang Z, Chi Z, et al. Minimizing amortized cost of the on-demand irrigation system in smart farms. in Proceedings of the 3rd International Workshop on Cyber-Physical Systems for Smart Water Networks. 2017. (pp. 43-46).
Chen H, Chen A, Xu L, et al., A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources. Agricultural Water Management. 2020;240: 106303.
Subashini MM, Das S, Heble S, et al., Internet of things based wireless plant sensor for smart farming. Indonesian Journal of Electrical Engineering and Computer Science. 2018;10(2): 456-468.
Sureephong P, Wiangnak P, and Wicha S. The comparison of soil sensors for integrated creation of IOT-based Wetting front detector (WFD) with an efficient irrigation system to support precision farming. in 2017 International Conference on Digital Arts, Media and Technology (ICDAMT). IEEE. 2017. (pp. 132-135).
Guo Y, Zhang J, Yin C, et al., Plant disease identification based on deep learning algorithm in smart farming. 2020;2020: 1-11.
Khirade SD and Patil A. Plant disease detection using image processing. in 2015 International conference on computing communication control and automation. IEEE. 2015. (pp. 768-771).
Mehra T, Kumar V, and Gupta P. Maturity and disease detection in tomato using computer vision. in 2016 Fourth international conference on parallel, distributed and grid computing (PDGC). IEEE. 2016. (pp. 399-403).
Richard B, Qi A, and Fitt BD, Control of crop diseases through Integrated Crop Management to deliver climate‐smart farming systems for low‐and high‐input crop production. Plant Pathology. 2022;71(1): 187-206.
Potamitis I, Eliopoulos P, and Rigakis I, Automated remote insect surveillance at a global scale and the internet of things. Robotics. 2017;6(3): 19.
Rustia DJA and Lin T-T, An IoT-based wireless imaging and sensor node system for remote greenhouse pest monitoring. Chemical Engineering Transactions. 2017;58: 601-606.
Kapoor A, Bhat SI, Shidnal S, et al. Implementation of IoT (Internet of Things) and Image processing in smart agriculture. in 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS). IEEE. 2016. (pp. 21-26).
Foughali K, Fathallah K, and Frihida A, Using Cloud IOT for disease prevention in precision agriculture. Procedia computer science. 2018;130: 575-582.
Mohanraj I, Ashokumar K, and Naren J, Field monitoring and automation using IOT in agriculture domain. Procedia Computer Science. 2016;93: 931-939.
Ma D, Ding Q, Li Z, et al., Prototype of an aquacultural information system based on internet of things E-Nose. Intelligent Automation & Soft Computing. 2012;18(5): 569-579.
Boyacı S, Akyüz A, and Tanriverdi Ç, Comparison of heat requirements in greenhouses for Kirşehir and Kahramanmaraş provinces. Infrastruktura i Ekologia Terenów Wiejskich. 2022: 5-20.
Boyacı S, Abacı-Bayar AA, Başpınar A, et al., Kırşehir İlindeki Bazı Seralarda Yetiştirilen Bitkilerin Beslenme Durumlarının Toprak ve Yaprak Analizleri ile Değerlendirilmesi. Atatürk Üniversitesi Ziraat Fakültesi Dergisi. 2021;52(3): 273-287. doi: https://doi.org/10.17097/ataunizfd.887348.
Yaslıoğlu E and Durmuş S, Bursa ilinde yetiştiricilik yapılan seraların yapısal yönden değerlendirilmesi. Journal of Agricultural Faculty of Gaziosmanpaşa University (JAFAG). 2017;34(Ek Sayı): 164-171.
Boyacı S, Başpınar A, Atilgan A, et al., Determination of the Vertical Distribution Pattern of Indoor Climate Parameters in the Greenhouse Heated in the Winter Period. Rocznik Ochrona Środowiska. 2023;25: 105-115. doi: https://doi.org/10.54740/ros.2023.011.
Cayli A, Temperature and relative humidity spatial variability: An assessment of the environmental conditions inside greenhouses. Fresenius Environmental Bulletin. 2020;29(7): 4954-4962.
Lamprinos I and Charalambides M, Experimental assessment of ZigBee as the communication technology of a wireless sensor network for greenhouse monitoring. International Journal of Advanced Smart Sensor Network Systems. 2015;6.
García L, Parra L, Jimenez JM, et al., IoT-based smart irrigation systems: An overview on the recent trends on sensors and IoT systems for irrigation in precision agriculture. Sensors. 2020;20(4): 1042.
Chieochan O, Saokaew A, and Boonchieng E. IOT for smart farm: A case study of the Lingzhi mushroom farm at Maejo University. in 2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE. 2017. (pp. 1-6).
Pooja S, Uday D, Nagesh U, et al. Application of MQTT protocol for real time weather monitoring and precision farming. in 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT). IEEE. 2017. (pp. 1-6).
Jayaraman PP, Yavari A, Georgakopoulos D, et al., Internet of things platform for smart farming: Experiences and lessons learnt. Sensors. 2016;16(11): 1884.
Kalathas J, Bandekas D, Kosmidis A, et al., Seedbed based on IoT: A Case Study. Journal of Engineering Science & Technology Review. 2016;9(2).
Çaylı A, Akyüz A, Baytorun AN, et al., Control of Greenhouse Environmental Conditions with IOT Based Monitoring and Analysis System. Turkish Journal of Agriculture-Food Science and Technology. 2017;5(11): 1279-1289.
Banhazi T, Lehr H, Black J, et al. Precision livestock farming: Scientific concepts and commercial reality. in Proceedings of the XVth International Congress on Animal Hygiene: Animal Hygiene and Sustainable Livestock Production (ISAH 2011). University of Southern Queensland. 2011. (pp. 137-143).
Castrignanò A and Buttafuoco G, Chapter 3 - Data processing, in Agricultural Internet of Things and Decision Support for Precision Smart Farming, A. Castrignanò, et al., Editors. 2020, Academic Press. p. 139-182.doi: https://doi.org/10.1016/B978-0-12-818373-1.00003-2.
Naud O, Taylor J, Colizzi L, et al., Chapter 4 - Support to decision-making, in Agricultural Internet of Things and Decision Support for Precision Smart Farming, A. Castrignanò, et al., Editors. 2020, Academic Press. p. 183-224.doi: https://doi.org/10.1016/B978-0-12-818373-1.00004-4.
Sun Z, Zheng F, and Yin S, Perspectives of research and application of Big Data on smart agriculture. Journal of Agricultural Science and Technology (Beijing). 2013;15(6): 63-71.
Çaylı A, Makine Öğrenimi (ML) ve Tarımdaki Uygulamaları, in Biyosistem Mühendisliği III, A. Atılgan, et al., Editors. 2022, Akademisyen Yayınevi Ankara. p. 198.doi: 10.37609/akya.1414.
Cambra Baseca C, Sendra S, Lloret J, et al., A Smart Decision System for Digital Farming. Agronomy. 2019;9(5): 216.
Suakanto S, Engel VJL, Hutagalung M, et al. Sensor networks data acquisition and task management for decision support of smart farming. in 2016 International Conference on Information Technology Systems and Innovation (ICITSI). 2016. (pp. 1-5). doi: 10.1109/ICITSI.2016.7858233.
Debauche O, Mahmoudi S, Andriamandroso ALH, et al., Cloud services integration for farm animals’ behavior studies based on smartphones as activity sensors. Journal of Ambient Intelligence and Humanized Computing. 2019;10: 4651-4662.
Halachmi I and Guarino M, Precision livestock farming: a ‘per animal’approach using advanced monitoring technologies. Animal. 2016;10(9): 1482-1483.
Rowe E, Dawkins MS, and Gebhardt-Henrich SG, A systematic review of precision livestock farming in the poultry sector: Is technology focussed on improving bird welfare? Animals. 2019;9(9): 614.
Garcia R, Aguilar J, Toro M, et al., A systematic literature review on the use of machine learning in precision livestock farming. Computers and Electronics in Agriculture. 2020;179: 105826.
di Virgilio A, Morales JM, Lambertucci SA, et al., Multi-dimensional Precision Livestock Farming: A potential toolbox for sustainable rangeland management. PeerJ. 2018;6: e4867.
Beukes P, McCarthy S, Wims C, et al., Regular estimates of herbage mass can improve profitability of pasture-based dairy systems. Animal Production Science. 2019;59(2): 359-367.
Lussem U, Schellberg J, and Bareth G, Monitoring forage mass with low-cost UAV data: case study at the Rengen grassland experiment. PFG–Journal of Photogrammetry, Remote Sensing and Geoinformation Science. 2020;88: 407-422.
Schaefer MT and Lamb DW, A combination of plant NDVI and LiDAR measurements improve the estimation of pasture biomass in tall fescue (Festuca arundinacea var. Fletcher). Remote Sensing. 2016;8(2): 109.
Oliveira RA, Näsi R, Niemeläinen O, et al., Machine learning estimators for the quantity and quality of grass swards used for silage production using drone-based imaging spectrometry and photogrammetry. Remote Sensing of Environment. 2020;246: 111830.
Meen G, Schellekens M, Slegers M, et al., Sound analysis in dairy cattle vocalisation as a potential welfare monitor. Computers and Electronics in Agriculture. 2015;118: 111-115.
Andriamandroso A, Bindelle J, Mercatoris B, et al., A review on the use of sensors to monitor cattle jaw movements and behavior when grazing. Biotechnologie, Agronomie, Société et Environnement. 2016;20.
Carpentier L, Vranken E, Berckmans D, et al., Development of sound-based poultry health monitoring tool for automated sneeze detection. Computers and electronics in agriculture. 2019;162: 573-581.
Bahlo C, Dahlhaus P, Thompson H, et al., The role of interoperable data standards in precision livestock farming in extensive livestock systems: A review. Computers and electronics in agriculture. 2019;156: 459-466.
Yaslıoğlu E and İlhan H, Güney Marmara Süt Sığırı Yetiştiriciliğinin Isı Stresi Yönünden Değerlendirilmesi. Tekirdağ Ziraat Fakültesi Dergisi. 2016;13(4): 12-19.
Boyacı S, Etlik Piliç Kümeslerinde, Isıtma ve Soğutma Derece Gün Değerlerinin Derece Gün Yöntemiyle Belirlenmesi: Kırşehir İli Örneği. Nevşehir Bilim ve Teknoloji Dergisi. 2018;7(1): 75-82.
Boyaci S, Akyüz A, and Kükürtcü M, Büyükbaş hayvan barınaklarında gübrenin yarattığı çevre kirliliği ve çözüm olanakları. Tarım Bilimleri Araştırma Dergisi. 2011;(1): 49-55.
Yaslıoğlu E and Türkmen E, IPARD Destekli Süt Sığırcılığı İşletmelerinde İç Ortam İklim Parametrelerinin Analizi (Bursa Karacabey Örneği). Journal of Agricultural Faculty of Gaziosmanpaşa University (JAFAG). 2017;34(Ek Sayı): 41-49.
Yaslıoglu E, İlhan H, Şimşek E, et al., Spatial and Temporal Variatıons of Temperature Humidity Index (THI) For Laying Hens in the Marmara Regıon, Turkey. Fresenius Environmental Bulletin. 2018;27(11): 7503-7509.
Bezen R, Edan Y, and Halachmi I, Computer vision system for measuring individual cow feed intake using RGB-D camera and deep learning algorithms. Computers and electronics in agriculture. 2020;172: 105345.
Rodenburg J, Robotic milking: Technology, farm design, and effects on work flow. Journal of Dairy Science. 2017;100(9): 7729-7738.
John A, Clark C, Freeman M, et al., Milking robot utilization, a successful precision livestock farming evolution. Animal. 2016;10(9): 1484-1492.
O’Mahony N, Campbell S, Carvalho A, et al., 3D vision for precision dairy farming. IFAC-PapersOnLine. 2019;52(30): 312-317.
