Efficient Smart Water Management Techniques to Enhance Crop Productivity for Maize

P. Prema *

Tamil Nadu Agricultural University, Agricultural College and Research Institute, Madurai, India.

A. Veeramani

TNAU, Agricultural College and Research Institute, Chettinadu, India.

J. Kannan

Tamil Nadu Agricultural University, Coimbatore, India.

T. Sampath Kumar

Tamil Nadu Agricultural University, Agricultural College and Research Institute, Madurai, India.

B. Sivasankari

Tamil Nadu Agricultural University, Agricultural College and Research Institute, Madurai, India.

P. Jona Innisai Rani

TNAU, Anbil Dharmalingam Agricultural College and Research Institute, Tiruchirappalli, India.

S. Amutha

Tamil Nadu Agricultural University, Agricultural College and Research Institute, Madurai, India.

*Author to whom correspondence should be addressed.


Abstract

Maize is identified as the “Queen of Cereals” owing to its high yield. Water scarcity is a major problem, where agriculture consumes significant freshwater. Smart Irrigation with Internet of Things technology can help farmers to maximize crop production with less water. The field experiment on sensor-based drip Irrigation and farmer practice was conducted in the research field to enhance growth, yield, and water efficiency. The experiment comprised eight treatments such as T1: IoT based Drip Irrigation at 60% depletion level, T2: IoT based Drip Irrigation at 80% depletion level, T3: Drip Irrigation at 60% PE, T4: Drip Irrigation at 80% PE, T5: Drip irrigation as Normal Practice, T6: Surface Irrigation at 60% IW/CPE ratio, T7: Surface Irrigation at 80% IW/ CPE ratio and T8: Flood irrigation as Farmer’s Practice. The proposed systems-based Drip irrigation at a 60% depletion level can be recommended for hybrid maize to augment higher grain and straw yield. The highest water saving was recorded in IoT-based Drip Irrigation at a 60% depletion level(46.88%) followed by IoT-based Drip Irrigation at an 80% depletion level(40.63%).

Keywords: Irrigation, sensor based irrigation, weather based monitoring, automatic irrigation system


How to Cite

Prema, P., Veeramani, A., Kannan, J., Kumar, T. S., Sivasankari, B., Rani, P. J. I., & Amutha, S. (2024). Efficient Smart Water Management Techniques to Enhance Crop Productivity for Maize. International Journal of Plant & Soil Science, 36(6), 266–271. https://doi.org/10.9734/ijpss/2024/v36i64629

Downloads

Download data is not yet available.

References

Liakos KG, Busato P, Moshou D, Pearson S, Bochtis D. Machine learning in agriculture: A review, Sensors. 2018; 18(8):2674.

Jha K, Doshi A, Patel P, Shah M. A comprehensive review on automation in agriculture using artificial intelligence, Artif. Intell. Agric. 2019;2:1–12.

Khan N, Ray RL, Sargani GR, Ihtisham M, Khayyam M, Ismail S. Current progress and future prospects of agriculture technology: gateway to sustainable agriculture, Sustainability. 2021;13(9): 4883.

Van Es H, Woodard J. Innovation in agriculture and food systems in the digital age. The Global Innovation Index. 2017;97–104.

Tantalaki N, Souravlas S, Roumeliotis M. Data-driven decision making in precision agriculture: the rise of big data in agricultural systems, J. Agric. Food Inf. 2019;20(4):344–380.

Elijah O, Rahman TA, Orikumhi I, Leow CY, Hindia MN. An overview of the Internet of Things (IoT) and data analytics in agriculture: benefits and challenges, IEEE Internet Things J. 2018;5(5):3758–3773.

Weersink A, Fraser E, Pannell D, Duncan E, Rotz S. Opportunities and challenges for big data in agricultural and environmental analysis, Annu. Rev. Resour. Econ. 2018;10(1):19–37.

Abedin Z, et al. An interoperable IP-based WSN for smart irrigation systems; 2017.

Anagha CS, Pawar PM, Tamizharasan PS. Cost-effective IoT-based intelligent irrigation system. Int J Syst Assur Eng Manag. 2023;14(Suppl 1):263–274.

Chaithra C, Hanumanthappa DC, Mudalagiriyappa SG, Sukanya TS, Lathashree AV. Sensor and SMI based Irrigation Management in Maize [Zea mays (L.)] to Enhance Growth, Yield and Water Use Efficiency.