Water optimization technique for precision irrigation system using IoT and machine learning

Vianny, D. Maria Manuel; John, A; Mohan, Senthil Kumar; Sarlan, Aliza; Adimoolam,; Ahmadian, Ali

doi:10.1016/j.seta.2022.102307

Cited by 18 publications

(6 citation statements)

References 22 publications

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“…Tables 9 and 10 clearly illustrate the optimization of water usage following the implementation of transfer learning. Our proposed work was compared with the recent work [8,[27][28][29], and it optimized the irrigation requirements. Compared to previous work, total water usage is reduced.…”

Section: Comparison With Other Methods Of Estimation and Transfer Of ...mentioning

confidence: 99%

“…The authors of [16] propose machine-learning and IoT techniques to manage smart irrigation with the help of different parameters, such as various soil conditions, environmental parameters, temperature and nearest features, which are considered for requirement calculation. The authors of [8] propose a system for optimizing the water requirement of crops using the WSN and different node sensors. Control devices are used to manage the crop using mobile and web applications.…”

Section: Irrigation Techniquesmentioning

confidence: 99%

“…IoT framework [5,8,9,16,23] The site-specific variable-rate sprinkler irrigation [9] (SS-VRT) used crop and soil conditions for irrigation, KMO [23] is used to analyse the factors of irrigations, SWAMP [5] architecture provides better scalability, Federated learning [16] is used for irrigation without sharing the data, using machine learning.…”

Section: Previous Models Advantages Limitationsmentioning

confidence: 99%

“…SS-VRT does not support long-term application, KMO is not considered as a real factor affecting the irrigation, SWAMP is not considered as one of the the multiple features for irrigation and the Federated learning [8].…”

Section: Previous Models Advantages Limitationsmentioning

confidence: 99%

“…Compared to previous methodologies, this work is the first to introduce transfer learning to an irrigation system and forecast irrigation requirements using transfer learning to predict one farm from another. Compared to the previous method of irrigation requirements prediction [8], our method of LSTM and ANFIS with transfer learning reduces by 30.24% the water requirements in the single node of a banana tree in the implementations. Our method was tuned to consume 1.16% less water in a single banana-tree node than in ref.…”

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confidence: 91%

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Sustainable Irrigation Requirement Prediction Using Internet of Things and Transfer Learning

Blessy

Kumar

Prabagaran³

et al. 2023

Sustainability

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Irrigation systems are a crucial research area because it is essential to conserve fresh water and utilize it wisely. As a part of this study, the reliability of predicting the usage of water in the present and future is investigated in order to develop an effective prediction model to communicate demand. In order to improve prediction, we develop a prediction model and share the updated model with nearby farmers. In order to forecast the irrigation requirements, the recommended model utilizes the Internet of Things (IoT), k-nearest neighbours (KNN), cloud storage, long short-term memory (LSTM), and adaptive network fuzzy inference system (ANFIS) techniques. By collecting real-time environmental data, KNN identifies the closest water requirement from the roots and its surrounding. In order to predict short-term requirements, ANFIS is used. To transfer the new requirements for better prediction, transfer learning is used. Time-series-data updates are predicted using LSTM for future forecasting, and the integrated model is shared with other farmers using cloud environments to enhance forecasting and analysis. For implementation, a period of nine to ten months of data was collected from February to December 2021, and banana tree was used to implement the planned strategy. Four farms, with measurements, were considered at varying intervals to determine the minimum and maximum irrigation needs. The requirements of farms were collected over time and compared to the predictions. Future requirements at 8, 16, 24, 32, and 48 h were also anticipated. The results indicated were compared to manual water pouring, and, thus, the entire crop used less water, making our prediction model a real-world option for irrigation. The prediction model was evaluated using R2, MSLE and the average initial prediction value of R2 was 0.945. After using transfer learning, the prediction of the model of Farm-2, 3 and 4 were 0.951, 0.958 and 0.967, respectively.

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Section: Comparison With Other Methods Of Estimation and Transfer Of ...mentioning

confidence: 99%

Section: Irrigation Techniquesmentioning

confidence: 99%