Machine Learning based Regression Model for Prediction of Soil Surface Humidity over Moderately Vegetated Fields

Acar, Emrullah; Özerdem, Mehmet Siraç; Ustundag, Burak Berk

doi:10.1109/agro-geoinformatics.2019.8820461

Cited by 12 publications

(5 citation statements)

References 10 publications

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“…A scientific analysis of soil nutrients, soil moisture, pH is important for determining the soil properties. Acar et al [18] employed an extreme learning machine (ELM) based regression model for prediction of soil surface humidity. The author selected two terrains having area 4 KM2 and 16 KM2 located in Dicle university campus for experimental analysis.…”

Section: A Soil Properties and Weather Predictionmentioning

confidence: 99%

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

et al. 2021

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Agriculture plays a vital role in the economic growth of any country. With the increase of population, frequent changes in climatic conditions and limited resources, it becomes a challenging task to fulfil the food requirement of the present population. Precision agriculture also known as smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine ability to learn without being explicitly programmed. ML together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. In this article, authors present a systematic review of ML applications in the field of agriculture. The areas that are focused are prediction of soil parameters such as organic carbon and moisture content, crop yield prediction, disease and weed detection in crops and species detection. ML with computer vision are reviewed for the classification of a different set of crop images in order to monitor the crop quality and yield assessment. This approach can be integrated for enhanced livestock production by predicting fertility patterns, diagnosing eating disorders, cattle behaviour based on ML models using data collected by collar sensors, etc. Intelligent irrigation which includes drip irrigation and intelligent harvesting techniques are also reviewed that reduces human labour to a great extent. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.

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Section: A Soil Properties and Weather Predictionmentioning

confidence: 99%

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

et al. 2021

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“…Topic Solution [47] Soil Nutrition [51] Soil Classification [48] Soil Detection System [49] Soil Humidity Sensing [52], [53] Soil Moisture Prediction and Forecast [54] Soil Moisture Evaluation [55], [56] Soil Prediction [57] Soil Estimate Soil Moisture [50] Soil Analysis Kwok and Sun [59], On technology in ML can be used for learning and is very important in agriculture. In recent years, many studies have taken advantage of machine learning as part of AI.…”

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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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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“…The pictures generated by the imaging sensor are utilised to analyse crucial irrigation characteristics including as water demand, leaf quality, and fertiliser requirements. Acar et al [10] used an extreme learning machine (ELM) based on a regression model to forecast soil moisture on the surface. The information gathered there is utilised to predict soil moisture content.…”

Section: Smart Farming Solutions Based On Iot and Machine Learning Ap...mentioning

confidence: 99%

“…SVR and K-Means clustering Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist's tacit knowledge, Goldstein et al [8] Temperature -solar radiation -relative air humidity GBRT, regression tree model (RTM), and boosted trees classifiers (BTC) Machine learning based regression model for prediction of soil surface humidity over moderately vegetated fields, Acar et al [10] Soil surface moisture Extreme learning machine regression (ELM-R) Modeling of soft sensor based on DBN-ELM and its application in measurement of nutrient solution composition for soilless culture, Wang et al [11] pH value, temperature, and flow rate ELM AMSR2 soil moisture downscaling using multi sensor products through machine learning approach, Park et al [12] Soil moisture RF and Cubist algorithms A comparative study between a new method and other machine learning algorithms for soil organic carbon and total nitrogen prediction using near infrared spectroscopy, Reda et al [13] Soil organic carbon (SOC) and total nitrogen (TN)…”

Section: Svmrmentioning

confidence: 99%

A comprehensive analysis on IoT based smart farming solutions using machine learning algorithms

Aliar¹,

Justindhas²,

Alagarsamy³

et al. 2022

Bulletin EEI

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Agriculture and farming are the most important and basic industries that are very important to humanity and generate a considerable portion of any nation's GDP. For good agricultural and farming management, technological advancements and support are required. Smart agriculture (or) farming is a set of approaches that uses a variety of current information and communication technology to improve the production and quality of agricultural products with minimum human involvement and at a lower cost. Smart farming is mostly based on IoT technology, since there is a need to continually monitor numerous aspects in the agricultural field, such as water level, light, soil characteristics, plant development, and so on. Machine learning algorithms are used in smart farming to increase production and reduce the risk of crop damage. Data analytics has been shown through extensive study to improve the accuracy and predictability of smart agricultural systems. Data analytics is utilised in agricultural fields to make decisions and recommend acceptable crops for production. This study provides a comprehensive overview of the different methods and structures utilised in smart farming. It also provides a thorough analysis of different designs and recommends appropriate answers to today's smart farming problems.

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Machine Learning based Regression Model for Prediction of Soil Surface Humidity over Moderately Vegetated Fields

Cited by 12 publications

References 10 publications

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

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

A comprehensive analysis on IoT based smart farming solutions using machine learning algorithms

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