Innovation and Technologies Synthesis of Various Components: A Contribution to the Precision Irrigation Development for Open-Field Fruit Orchards
Authors: P. Chatrabhuti, S. Visessri, T. Charinpanitkul
Abstract:
Precision irrigation (PI) technology has emerged as a solution to optimize water usage in agriculture, aiming to maximize crop yields while minimizing water waste. Developing a PI for commercialization requires developers to research, synthesize, evaluate, and select appropriate technologies and make use of such information to produce innovative products. The objective of this review is to facilitate innovators by providing them with a summary of existing knowledge and the identification of gaps in research linking to the innovative development of PI. This paper reviews and synthesizes technologies and components relevant to precision irrigation, highlighting its potential benefits and challenges. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework is used for the review. As a result of this review, the different technologies have limitations and may only be suitable for specific orchards or spatial settings. The current technologies are readily available in a range of options, from affordable controllers to high-performance systems that are both reliable and precise. Furthermore, the future prospects for incorporating artificial intelligence and machine learning techniques hold promise for advancing autonomous irrigation systems.
Keywords: Innovation synthesis, technology assessment, precision irrigation technologies, precision irrigation components, new product development.
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[1] A. Salmen, “New Product Launch Success: A Literature Review,” Acta Univ. Agric. Silvic. Mendelianae Brun., vol. 69, no. 1, pp. 151–176, Mar. 2021, doi: 10.11118/actaun.2021.008.
[2] H. W. Chesbrough, Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business School, 2003.
[3] S. Winterhalter, T. Weiblen, C. H. Wecht, and O. Gassmann, “Business model innovation processes in large corporations: insights from BASF,” JBS, vol. 38, no. 2, pp. 62–75, Apr. 2017, doi: 10.1108/JBS-10-2016-0116.
[4] A. Fernandez, “Steve Jobs,” Vidwat, vol. 5, no. 1, pp. 46–48, Jun. 2012.
[5] J. M. Podolny and H. T. Morten, “How Apple Is Organized for Innovation,” Harvard Business Review, no. HBR Special Summer 2021, pp. 100–108, Dec. 2021.
[6] O. Adeyemi, I. Grove, S. Peets, and T. Norton, “Advanced Monitoring and Management Systems for Improving Sustainability in Precision Irrigation,” Sustainability, vol. 9, no. 3, p. 353, Feb. 2017, doi: 10.3390/su9030353.
[7] J. P. Peters, L. Hooft, W. Grolman, and I. Stegeman, “Reporting quality of systematic reviews and meta-analyses of otorhinolaryngologic articles based on the PRISMA statement,” PLoS One, vol. 10, no. 8, p. e0136540, 2015.
[8] R. G. Allen, L. S. Pereira, D. Raes, and M. Smith, “Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56,” Fao, Rome, vol. 300, no. 9, p. D05109, 1998.
[9] A. Honrubia et al., “Comparison of wind speed measurements over complex terrain using a LIDAR system,” p. 7.
[10] Á. Ramos-Cenzano, M. Ogueta-Gutiérrez, and S. Pindado, “Cup anemometers’ performance analysis today: still room for improvement,” Journal of Energy Systems, pp. 129–138, Dec. 2019, doi: 10.30521/jes.614212.
[11] E. Roibas-Millan, J. Cubas, and S. Pindado, “Studies on Cup Anemometer Performances Carried out at IDR/UPM Institute. Past and Present Research,” Energies, vol. 10, no. 11, p. 1860, Nov. 2017, doi: 10.3390/en10111860.
[12] E. C. Barbosa and L. Klok, “Weather stations comparison,” p. 19.
[13] B. Bauer-Marschallinger et al., “Toward Global Soil Moisture Monitoring with Sentinel-1: Harnessing Assets and Overcoming Obstacles,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 1, pp. 520–539, Jan. 2019, doi: 10.1109/TGRS.2018.2858004.
[14] J. M. Domínguez-Niño, J. Oliver-Manera, J. Girona, and J. Casadesús, “Differential irrigation scheduling by an automated algorithm of water balance tuned by capacitance-type soil moisture sensors,” Agricultural Water Management, vol. 228, p. 105880, Feb. 2020, doi: 10.1016/j.agwat.2019.105880.
[15] M. S. Farooq, S. Riaz, A. Abid, K. Abid, and M. A. Naeem, “A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming,” IEEE Access, vol. 7, pp. 156237–156271, 2019, doi: 10.1109/ACCESS.2019.2949703.
[16] K. Phasinam et al., “Application of IoT and Cloud Computing in Automation of Agriculture Irrigation,” Journal of Food Quality, vol. 2022, 2022, doi: 10.1155/2022/8285969.
[17] A. Sagheer, M. Mohammed, K. Riad, and M. Alhajhoj, “A Cloud-Based IoT Platform for Precision Control of Soilless Greenhouse Cultivation,” Sensors-Basel, vol. 21, no. 1, Jan. 2021, doi: ARTN 223 10.3390/s21010223.
[18] E. Wang et al., “Development of a closed-loop irrigation system for sugarcane farms using the Internet of Things,” Computers and Electronics in Agriculture, vol. 172, p. 105376, May 2020, doi: 10.1016/j.compag.2020.105376.
[19] L. Hamami and B. Nassereddine, “Application of wireless sensor networks in the field of irrigation: A review,” Comput Electron Agr, vol. 179, Dec. 2020, doi: ARTN 105782 10.1016/j.compag.2020.105782.
[20] P. Fraga-Lamas et al., “Design and Experimental Validation of a LoRaWAN Fog Computing Based Architecture for IoT Enabled Smart Campus Applications,” Sensors-Basel, vol. 19, no. 15, Aug. 2019, doi: ARTN 3287 10.3390/s19153287.
[21] I. Froiz-Miguez et al., “Design, Implementation, and Empirical Validation of an IoT Smart Irrigation System for Fog Computing Applications Based on LoRa and LoRaWAN Sensor Nodes dagger,” Sensors-Basel, vol. 20, no. 23, Dec. 2020, doi: 10.3390/s20236865.
[22] O. Elijah et al., “Effect of Weather Condition on LoRa IoT Communication Technology in a Tropical Region: Malaysia,” IEEE Access, vol. 9, pp. 72835–72843, 2021, doi: 10.1109/ACCESS.2021.3080317.
[23] S. K. Sah Tyagi, A. Mukherjee, S. R. Pokhrel, and K. K. Hiran, “An Intelligent and Optimal Resource Allocation Approach in Sensor Networks for Smart Agri-IoT,” IEEE Sensors Journal, vol. 21, no. 16, pp. 17439–17446, Aug. 2021, doi: 10.1109/JSEN.2020.3020889.
[24] Y. Ampatzidis, V. Partel, and L. Costa, “Agroview: Cloud-based application to process, analyze and visualize UAV-collected data for precision agriculture applications utilizing artificial intelligence,” Computers and Electronics in Agriculture, vol. 174, p. 105457, Jul. 2020, doi: 10.1016/j.compag.2020.105457.
[25] M. Ayaz, M. Ammad-Uddin, Z. Sharif, A. Mansour, and E.-H. M. Aggoune, “Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk,” IEEE Access, vol. 7, pp. 129551–129583, 2019, doi: 10.1109/ACCESS.2019.2932609.
[26] B. Keswani et al., “Adapting weather conditionsbased IoT enabled smart irrigation technique in precision agriculture mechanisms,” Neural Comput Appl, vol. 31, pp. 277–292, Jan. 2019, doi: 10.1007/s00521-018-3737-1.
[27] G. S. Prasanna Lakshmi, P. N. Asha, G. Sandhya, S. Vivek Sharma, S. Shilpashree, and S. G. Subramanya, “An intelligent IOT sensor coupled precision irrigation model for agriculture,” Measurement: Sensors, vol. 25, p. 100608, Feb. 2023, doi: 10.1016/j.measen.2022.100608.
[28] S. C. Kakarla and Y. Ampatzidis, “Types of Unmanned Aerial Vehicles (UAVs), Sensing Technologies, and Software for Agricultural Applications: AE565/AE565, 10/2021,” EDIS, vol. 2021, no. 5, Oct. 2021, doi: 10.32473/edis-ae565-2021.
[29] J. Abdulridha, Y. Ampatzidis, J. Qureshi, and P. Roberts, “Laboratory and UAV-Based Identification and Classification of Tomato Yellow Leaf Curl, Bacterial Spot, and Target Spot Diseases in Tomato Utilizing Hyperspectral Imaging and Machine Learning,” Remote Sensing, vol. 12, no. 17, p. 2732, Aug. 2020, doi: 10.3390/rs12172732.
[30] V. Gonzalez-Dugo et al., “Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard,” Precision Agriculture, vol. 14, no. 6, pp. 660–678, 2013, doi: 10.1007/s11119-013-9322-9.
[31] Z. Zhou, Y. Majeed, G. Diverres Naranjo, and E. M. T. Gambacorta, “Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications,” Computers and Electronics in Agriculture, vol. 182, 2021, doi: 10.1016/j.compag.2021.106019.
[32] S. Guan, Z. Zhu, and G. Wang, “A Review on UAV-Based Remote Sensing Technologies for Construction and Civil Applications,” Drones, vol. 6, no. 5, p. 117, May 2022, doi: 10.3390/drones6050117.
[33] J. L. Chávez, F. J. Pierce, T. V. Elliott, and R. G. Evans, “A remote irrigation monitoring and control system for continuous move systems. Part A: Description and development,” Precision Agriculture, vol. 11, no. 1, pp. 1–10, 2010, doi: 10.1007/s11119-009-9109-1.
[34] Y. A. Rivas-Sanchez, M. F. Moreno-Perez, and J. Roldan-Canas, “Environment Control with Low-Cost Microcontrollers and Microprocessors: Application for Green Walls,” Sustainability-Basel, vol. 11, no. 3, Feb. 2019, doi: 10.3390/su11030782.
[35] S. K. Roy, S. Misra, N. S. Raghuwanshi, and S. K. Das, “AgriSens: IoT-Based Dynamic Irrigation Scheduling System for Water Management of Irrigated Crops,” IEEE Internet of Things Journal, vol. 8, no. 6, pp. 5023–5030, 2021, doi: 10.1109/JIOT.2020.3036126.
[36] M. F. Isik, Y. Sonmez, C. Yilmaz, V. Ozdemir, and E. N. Yilmaz, “Precision Irrigation System (PIS) Using Sensor Network Technology Integrated with IOS/Android Application,” Appl Sci-Basel, vol. 7, no. 9, Sep. 2017, doi: 10.3390/app7090891.
[37] A. Sharma, A. Jain, P. Gupta, and V. Chowdary, “Machine Learning Applications for Precision Agriculture: A Comprehensive Review,” IEEE Access, vol. 9, pp. 4843–4873, 2021, doi: 10.1109/ACCESS.2020.3048415.
[38] A. U. G. Sankararao, P. Rajalakshmi, and S. Choudhary, “Machine Learning-Based Ensemble Band Selection for Early Water Stress Identification in Groundnut Canopy Using UAV-Based Hyperspectral Imaging,” IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1–5, 2023, doi: 10.1109/LGRS.2023.3284675.
[39] C. Corbari, R. Salerno, A. Ceppi, V. Telesca, and M. Mancini, “Smart irrigation forecast using satellite LANDSAT data and meteo-hydrological modeling,” Agr Water Manage, vol. 212, pp. 283–294, Feb. 2019, doi: 10.1016/j.agwat.2018.09.005.
[40] R. Filgueiras et al., “Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data,” Agricultural Water Management, vol. 241, 2020, doi: 10.1016/j.agwat.2020.106346.
[41] C. L. Chang and K. M. Lin, “Smart Agricultural Machine with a Computer Vision-Based Weeding and Variable-Rate Irrigation Scheme,” Robotics, vol. 7, no. 3, Sep. 2018, doi: 10.3390/robotics7030038.
[42] S. A. O’Shaughnessy et al., “Identifying advantages and disadvantages of variable rate irrigation: An updated review,” Applied Engineering in Agriculture, vol. 35, no. 6, pp. 837–852, 2019, doi: 10.13031/aea.13128837.
[43] J. Neupane and W. Guo, “Agronomic basis and strategies for precision water management: A review,” Agronomy, vol. 9, no. 2, 2019, doi: 10.3390/agronomy9020087.
[44] R. Liao, S. Zhang, X. Zhang, M. Wang, H. Wu, and L. Zhangzhong, “Development of smart irrigation systems based on real-time soil moisture data in a greenhouse: Proof of concept,” Agricultural Water Management, vol. 245, p. 106632, Feb. 2021, doi: 10.1016/j.agwat.2020.106632.
[45] E. Bwambale, F. K. Abagale, and G. K. Anornu, “Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review,” Agricultural Water Management, vol. 260, 2022, doi: 10.1016/j.agwat.2021.107324.
[46] S. I. Hassan, M. M. Alam, U. Illahi, M. A. Al Ghamdi, S. H. Almotiri, and M. M. Su’ud, “A Systematic Review on Monitoring and Advanced Control Strategies in Smart Agriculture,” IEEE Access, vol. 9, pp. 32517–32548, 2021, doi: 10.1109/ACCESS.2021.3057865.