Smart watering of ornamental plants: exploring the potential of decision trees in precision agriculture based on IoT

Hafiyyan Putra Pratama, Dewi Indriati Hadi Putri, Hafiziani Eka Putri, Elysa Nensy Irawan, Makna A’raaf Kautsar

Abstract

Ornamental plant farmers face various challenges due to climate change and environmental stress that significantly affect plant health and growth. This research overcomes these challenges by developing an intelligent watering system that uses internet of things (IoT) technology and decision trees (DTs) algorithms to optimize the use of planting land by ensuring plants grow in the most optimal conditions, both in terms of water and nutrients and increase land productivity. The system is built by integrating various sensors to monitor soil moisture, air humidity, temperature, and light intensity in real-time. The collected data is used to automate watering schedules and provide recommendations on suitable plant species based on the soil nutrient content of nitrogen (N), phosphorus (P), and potassium (K). The use of the DTs algorithm helps in analyzing the data from the sensors and providing recommendations on the most suitable plants for the land. The smart watering system was tested in three zones, each simulating a different watering scenario, and successfully maintained optimal conditions for plant growth in each zone. The machine learning (ML) model with the DTs algorithm can predict the right type of ornamental plants based on the existing land conditions in three watering zones, with an accuracy of 89 %, 90 %, and 91 %, respectively. Furthermore, farmers can follow these recommendations to minimize damage and death of plants so that the level of productivity on the land becomes optimal.


10.55981/j.mev.2024.963


Keywords


decision tree; precision agriculture; internet of things (IoT).

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References


A. Pereira, “Plant abiotic stress challenges from the changing environment,” Frontiers in Plant Science, vol. 7, 2016.

R. L. Naylor, D. S. Battisti, D. J. Vimont, W. P. Falcon, and M. Burke, “Assessing risks of climate variability and climate change for Indonesian rice agriculture,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, pp. 7752-7, 2007.

A. Keil, M. Zeller, A. Wida, B. Sanim, and R. Birner, “What determines farmers’ resilience towards ENSO-related drought? An empirical assessment in Central Sulawesi, Indonesia,” Climatic Change, vol. 86, pp. 291-307, 2008.

H. P. Pratama, D. I. H. Putri, and Sudjani, “Prototype

Penyiraman Otomatis Berbasis IOT untuk Multi Zona

Tanaman Hias,” Jurnal Sistem Cerdas, vol. 5, no. 1, pp.

–11, 2022.

B. Rajendran, B. L. P. Suganthi, S. Deepa, S. Danush, and

S. Mathesh, “An Efficient Indoor Nursery Controlled by

IOT and Monitored by Android App.,” International

journal of innovative science and research technology,

vol. 9, pp. 2322-2325, 2024.

K. N. Siva, R. Kumar G., A. Bagubali, and K. V. Krishnan,

“Smart watering of plants.” 2019 International

Conference on Vision Towards Emerging Trends in

Communication and Networking (ViTECoN), IEEE, pp.

-4, 2019.

A. Elvanidi and N. Katsoulas, “Machine Learning-Based

Crop Stress Detection in Greenhouses,” Plants, vol. 12(1),

p. 52, 2023.

A. S. Petropoulou, B. van Marrewijk, F. de Zwart, A.

Elings, M. Bijlaard, T. van Daalen, G. Jansen, and S.

Hemming, “Lettuce Production in Intelligent

Greenhouses—3D Imaging and Computer Vision for

Plant Spacing Decisions,” Sensors, vol. 6, p. 2929, 2023.

Y. Meng, M. Xu, S. Yoon, Y. Jeong, and D. S. Park,

“Flexible and high quality plant growth prediction with

limited data,” Frontiers in Plant Science, 2022.

S. M. Ichami, G. N. Karuku, A. Sila, F. O. Ayuke, and K.

D. Shepherd, “Spatial approach for diagnosis of yield-

limiting nutrients in smallholder agroecosystem

landscape using population-based farm survey data,”

PLoS One, 17(2),: e0262754, 2022.

H. Singh, N. K. Halder, B. Singh, J. Singh, S. Sharma, and

Y. Shacham-Diamand, “Smart Farming Revolution:

Portable and Real-Time Soil Nitrogen and Phosphorus

Monitoring for Sustainable Agriculture,” Sensors, vol.

(13), p. 5914, 2023.

M. I. Hossain et al., “Development of electrochemical

sensors for quick detection of environmental (soil and

water) NPK ions,” RSC Advances, 14, 9137-9158, 2024.

Gopi C., V. Pramodh, M. S. Charan, K. S. K. Reddy, and

D. SaiVamsi, “Cloud-based Air Quality Monitoring

through Wireless Sensor Network using NodeMCU,”

International Journal for Research in Applied Science and

Engineering Technology, vol. 10, 2022.

H. P. Pratama, A. S. Prihatmanto, and A. Sukoco,

“Implementation Messaging Broker Middleware for

Architecture of Public Transportation Monitoring

System,” in 2020 6th International Conference on

Interactive Digital Media (ICIDM), IEEE, pp. 1–5, 2020.

R. Wijaya, A. S. Prihatmanto, and Kuspriyanto,

“Preliminary design of estimation heart disease by using

machine learning ANN within one year,” in 2013 Joint

International Conference on Rural Information &

Communication Technology and Electric-Vehicle

Technology (rICT & ICeV-T), IEEE, pp. 1–4, 2013.

A. S. Iskandar, A. S. Prihatmanto, and Y. Priyana,

“Design and implementation electronic stethoscope on

smart chair for monitoring heart rate and stress levels

driver,” in 2015 4th International Conference on

Interactive Digital Media (ICIDM), IEEE, pp. 1–6, 2015.

T. Adiono, S. F. Anindya, S. Fuada, K. Afifah, and I. G.

Purwanda, “Efficient android software development

using mit app inventor 2 for bluetooth-based smart

home,” Wireless Personal Communications, vol. 105, pp.

–256, 2019.

A. K. Pratihast, B. DeVries, V. Avitabile, S. De Bruin, M.

Herold, and A. Bergsma, “Design and implementation of

an interactive web-based near real-time forest

monitoring system,” PLoS One, vol. 11(3): e0150935,

P. Valsalan, T. A. B. Baomar, and A. H. O. Baabood, “IoT

based health monitoring system,” Journal of critical

reviews, vol. 7, no. 4, pp. 739–743, 2020.

L. Venica, E. N. Irawan, and D. I. H. Putri, “IoT with

Firebase: Smart Ring Android App Using MAX30100 for

Fatigue Detection,” Journal of Electrical, Electronic,

Information, and Communication Technology, vol. 6, no.

, pp. 8–15, 2024.

H. Fakhrurroja, E. T. Nuryatno, A. Munandar, M. Fahmi,

and N. A. Mahardiono, “Water quality assessment

monitoring system using fuzzy logic and the internet of

things,” Journal of Mechatronics, Electrical Power, and

Vehicular Technology, vol. 14, no. 2, pp. 198–207, 2023.

A. A. Dehghani, N. Movahedi, K. Ghorbani, S. Eslamian,

“Decision tree algorithms,” Handbook of

Hydroinformatics, Elsevier, pp. 171–187, 2023.

L. Vanneschi, S. Silva, “Decision Tree Learning,” in

Lectures on Intelligent System. Natural Computing

Series, Springer, Cham., pp. 149-159, 2023.

S. Hulu, P. Sihombing, and Sutarman, “Analysis of

Performance Cross Validation Method and K-Nearest

Neighbor in Classification Data,” International Journal

of Research and Review, vol. 7, no. 4, pp. 69–73, 2020.

G. C. Cawley, “Over-fitting in model selection and its

avoidance,” Lecture Notes in Computer Science, vol.

, Springer, Berlin, Heidelberg, pp. 1–1, 2012.

A. Lazidis, K. Tsakos, and E. G. M. Petrakis, “Publish-

Subscribe approaches for the IoT and the cloud:

Functional and performance evaluation of open-source

systems,” Internet of things, vol. 19, p. 100538, 2022.


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