AGRI-DRONE: Monitoring and Classification of Soil Fertility Based on Internet of Things Using Autonomous Drone
DOI:
https://doi.org/10.25124/cepat.v3i02.7952Keywords:
Soil Fertility, Weather Forecasting, Internet of Things, Agriculture, Monitoring, Prediction, ClassificationAbstract
Indonesia is an agricultural country that faces significant challenges in maintaining soil fertility to support farming and plantation productivity, exacerbated by weather fluctuations and climate change. To address this issue, an Internet of Things (IoT) system called Agri-Drone was developed. The system is designed for soil fertility classification using fuzzy logic and weather prediction using machine learning, assisting farmers in making informed crop management decisions. Agri-Drone integrates components such as Soil Test, Weather Station, and LoRa Gateway carried by autonomous drones, as well as a website for monitoring, improving resource use efficiency, reducing the risk of crop failure, and supporting national food security. The system has demonstrated significant success, with the Soil Test and Weather Station components demonstrating Quality of Service (QoS) levels with end-to-end delay and response times of less than 10 seconds. The measurement accuracy of soil elements including nitrogen (N) was 91.93%, phosphorus (P) was 91.31%, potassium (K) was 88.7%, pH was 95.03%, and moisture was 93.54%, with Relative Standard Deviation (RSD) values showing high precision. The LoRa Gateway maintained a stable connection over a wide range, and the Weather Station showed a very high level of precision. Machine learning for weather classification achieved 98% accuracy, and weather prediction achieved 72%. The website is user-friendly with an average score of 4.584 on a scale of 5 with a total of 51 respondents, and achieved 100% performance according to GTMetrix, enabling effective monitoring of measurement results.
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References
W. Budiharto, Smart Farming yang Berwawasan Lingkungan untuk. Unsri Press, 2019.
V. D N and Dr. S. Choudhary, “An AI solution for Soil Fertility and Crop Friendliness Detection and Monitoring,” Int J Eng Adv Technol, vol. 10, no. 3, pp. 172–175, Feb. 2021, doi: 10.35940/ijeat.C2270.0210321.
G. H. Sandi and Y. Fatma, “PEMANFAATAN TEKNOLOGI INTERNET OF THINGS (IOT) PADA BIDANG PERTANIAN,” 2023.
M. Y. Ridwan, L. Nurpulaela, and I. A. Bangsa, “Pengaplikasian Sistem IOT Pada Alat Penyiram Tanaman Otomatis Berbasis Arduino Nano,” JE-Unisla, vol. 7, no. 1, p. 26, Apr. 2022, doi: 10.30736/je-unisla.v7i1.766.
A. B. Nugroho, F. C. Hasibuan, and D. Perdana, “Implementation of self-hosted IoT ecosystem on NPK soil monitoring system,” CEPAT Journal of Computer Engineering: Progress, Application and Technology, vol. 3, no. 1, pp. 2963–6728, 2024, doi: 10.25124/cepat.v3i01.6698.
B. Siswanto, “SEBARAN UNSUR HARA N, P, K DAN PH DALAM TANAH,” BUANA SAINS, vol. 18, no. 2, p. 109, Feb. 2019, doi: 10.33366/bs.v18i2.1184.
A. Fakhrezi, “Rancang Bangun Sistem Monitoring Unsur Hara, Kelembaban, PH Tanah Dan Suhu Udara Berbasis Iot Menggunakanmikrokontroler ESP32 Iot Based Monitoring System Of Nutrient, Soil Moisture, Soil PH And Air Temprature Using ESP32 Microcontroller,” 2023.
L. Renaldi, “Implementasi Sistem Monitoring dan Controlling Unsur Hara dan Kelembaban Tanah pada Tanaman Cabai Berbasis IoT Menggunakan LoRa,” Telkom University Repository, 2020.
A. Galih Mardika and R. Kartadie, “MENGATUR KELEMBABAN TANAH MENGGUNAKAN SENSOR KELEMBABAN TANAH YL-69 BERBASIS ARDUINO PADA MEDIA TANAM POHON GAHARU.”
Z.-H. Zhou, “Open-environment machine learning,” Natl Sci Rev, vol. 9, no. 8, Aug. 2022, doi: 10.1093/nsr/nwac123.
M. M. Amri and R. Sumiharto, “Sistem Pengukuran Nitrogen, Fosfor, Kalium Dengan Local Binary Pattern Dan Analisis Regresi,” IJEIS (Indonesian Journal of Electronics and Instrumentation Systems), vol. 9, no. 2, p. 107, Oct. 2019, doi: 10.22146/ijeis.34132.
S. Triharto, L. Musa, and G. Sitanggang, “S U R V E I D A N P E M E T A A N U N S U R H A R A N , P , K , D A N p H T A N A H P A D A L A H A N S A W A H T A D A H H U J A N D I D E S A D U R I A N K E C A M A T A N P A N T A I L A B U Surveying and Mapping the Nitrogen, Phosphorus, Potassium Nutrients and Soil pH of Rain Fed Lowland in Desa Durian Kecamatan Pantai Labu,” vol. 2, no. 3, pp. 1195–1204, 2014.
A. Ibrahem Ahmed Osman, A. Najah Ahmed, M. F. Chow, Y. Feng Huang, and A. El-Shafie, “Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia,” Ain Shams Engineering Journal, vol. 12, no. 2, pp. 1545–1556, Jun. 2021, doi: 10.1016/j.asej.2020.11.011.
A. Sherstinsky, “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network,” Physica D, vol. 404, p. 132306, Mar. 2020, doi: 10.1016/j.physd.2019.132306.
N. Elsayed, A. Maida, and M. Bayoumi, “Effects of Different Activation Functions for Unsupervised Convolutional LSTM Spatiotemporal Learning,” Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 260–269, 2019, doi: 10.25046/aj040234.
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