Machine Learning-based Irrigation Control Optimization

Murthy, A. Hema; Green, Curtis; Stoleru, Radu; Bhunia, Suman; Swanson, Charles; Chaspari, Theodora

doi:10.1145/3360322.3360854

Cited by 12 publications

(8 citation statements)

References 16 publications

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“…Murthy et al [60], On scheduling irrigation, is very important, especially because traditional irrigation uses a lot of water, which causes fatal things such as water pollution and water wastage. By utilizing (Weather-aware Runoff Prevention Irrigation Control (WaRPIC)), the recording of the data obtained to form a machine learning model focused on predicting the Maximum Allowable Run Time (MAR).…”

Section: Referencesmentioning

confidence: 99%

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A Systematic Review of Current Trends in Artificial Intelligence for Smart Farming to Enhance Crop Yield

Widianto

Ardimansyah²,

Pohan³

et al. 2022

Journal of Robotics and Control

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Current technology has been widely applied for development, one of which has an Artificial Intelligence (AI) applied to Smart Farming. AI can give special capabilities to be programmed as needed. In cooperation with agricultural systems, AI is part of improving the quality of agriculture. This technology is no stranger to being applied in basic fields such as agriculture. This smart technology is needed to increase crop yields for various regions by utilizing the current trends paper. This is necessary because less land is available for agriculture, and there is a greater need for food sources. Therefore, this systematic review aims to collect the current trends in AI studies for Smart Farming papers using the latest year features from 2018-2022. This paper is handy for researchers and industry in looking for the latest papers on research to enhance crop yields. The authors utilized Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) of 534 articles from IEEE, ACM, MDPI, IAES, and ScienceDirect. After going through a careful process, 67 papers were found that were judged according to the criteria. After the authors got some of the current trends, the author has discussed several factors regarding the results obtained to enhance crop yields, such as Weather, Soil, Irrigation, Unmanned Aerial Vehicle (UAV), Pest Control, Weed Control, and Disease Control.

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Section: Referencesmentioning

confidence: 99%

“…Topic Solution [58] Irrigation Management [59], [61]- [66] Irrigation System [60] Irrigation Control [67] Irrigation Timing Decision [68] Irrigation Predictive…”

Section: Referencesmentioning

confidence: 99%

A Systematic Review of Current Trends in Artificial Intelligence for Smart Farming to Enhance Crop Yield

Widianto

Ardimansyah²,

Pohan³

et al. 2022

Journal of Robotics and Control

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“…However, the model requires ∼ 1000 hours as the IoT solution uses a single sensor on the top soil layer. [5] uses a different approach by trying to replicate farmer's behavior by directly observing that. Of course, the farmer's behavior is not necessarily the best approach for irrigation and an IoT solution using sensory data can provide superior results.…”

Section: A Motivationmentioning

confidence: 99%

“…Our main method is to find the relationships between the soil layers, which will definitely be the safest way to predict soil moisture. With this method, you can implement all the previous articles mentioned in the introduction [5], [6], [7], and each person can add new option to the whole system only by paying for the desired sensor, without having to pay extra money.…”

Section: Climate-smart Agriculturementioning

confidence: 99%

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Smart Irrigation IoT Solution using Transfer Learning for Neural Networks

Risheh

Jalili

Nazerfard

2020

Preprint

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In this paper we develop a reliable system for smart irrigation of greenhouses using artificial neural networks, and an IoT architecture. Our solution uses four sensors in different layers of soil to predict future moisture. Using a dataset we collected by running experiments on different soils, we show high performance of neural networks compared to existing alternative method of support vector regression. To reduce the processing power of neural network for the IoT edge devices, we propose using transfer learning. Transfer learning also speeds up training performance with small amount of training data, and allows integrating climate sensors to a pre-trained model, which are the other two challenges of smart irrigation of greenhouses. Our proposed IoT architecture shows a complete solution for smart irrigation.

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A Proposal of Iot Based Evapotranspiration Irrigation System for Water Conservation

Walia¹,

Sengupta²

2022

Lecture Notes in Electrical Engineering

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Machine Learning-based Irrigation Control Optimization

Cited by 12 publications

References 16 publications

A Systematic Review of Current Trends in Artificial Intelligence for Smart Farming to Enhance Crop Yield

A Systematic Review of Current Trends in Artificial Intelligence for Smart Farming to Enhance Crop Yield

Smart Irrigation IoT Solution using Transfer Learning for Neural Networks

A Proposal of Iot Based Evapotranspiration Irrigation System for Water Conservation

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