Soil moisture modeling based on stochastic behavior of forces on a no-till chisel opener

Johann, André Luiz; Araújo, Augusto Guilherme de; Delalibera, Hevandro Colonhese; Hirakawa, André Riyuiti

doi:10.1016/j.compag.2015.12.020

Cited by 40 publications

(8 citation statements)

References 21 publications

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“…The aim was the accurate estimation of soil temperature for agricultural management. The last study [ 113 ] presented a novel method for the estimation of soil moisture, based on ANN models using data from force sensors on a no-till chisel opener.…”

Section: Reviewmentioning

confidence: 99%

Machine Learning in Agriculture: A Review

Λιάκος

Busato

Moshou

et al. 2018

Sensors

1,772

827

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Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.

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Section: Reviewmentioning

confidence: 99%

Machine Learning in Agriculture: A Review

Λιάκος

Busato

Moshou

et al. 2018

Sensors

1,772

827

View full text Add to dashboard Cite

show abstract

“…Many studies have approached regression problems, in which the response variable is continuous, with machine learning to solve an ecological problem ( James et al., 2013 ). These studies include but not limited to crop yield predictions ( Drummond et al., 2003 ; Vincenzi et al., 2011 ; González Sánchez et al., 2014 ; Jeong et al., 2016 ; Pantazi et al., 2016 ; Cai et al., 2017 ; Chlingaryan et al., 2018 ; Crane-Droesch, 2018 ; Basso and Liu, 2019 ; Khaki and Wang, 2019 ; Shahhosseini et al., 2019c ; Emirhüseyinoğlu and Ryan, 2020 ; Khaki et al., 2020 ), crop quality ( Hoogenboom et al., 2004 ; Karimi et al., 2008 ; Mutanga et al., 2012 ; Shekoofa et al., 2014 ; Qin et al., 2018 ; Ansarifar and Wang, 2019 ; Khaki et al, 2019 ; Lawes et al., 2019 ; Moeinizade et al, 2019 ), water management ( Mohammadi et al., 2015 ; Feng et al., 2017 ; Mehdizadeh et al., 2017 ), soil management ( Johann et al., 2016 ; Morellos et al., 2016 ; Nahvi et al., 2016 ), and others.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Corn Yield With Machine Learning Ensembles

2020

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The emergence of new technologies to synthesize and analyze big data with highperformance computing has increased our capacity to more accurately predict crop yields. Recent research has shown that machine learning (ML) can provide reasonable predictions faster and with higher flexibility compared to simulation crop modeling. However, a single machine learning model can be outperformed by a "committee" of models (machine learning ensembles) that can reduce prediction bias, variance, or both and is able to better capture the underlying distribution of the data. Yet, there are many aspects to be investigated with regard to prediction accuracy, time of the prediction, and scale. The earlier the prediction during the growing season the better, but this has not been thoroughly investigated as previous studies considered all data available to predict yields. This paper provides a machine leaning based framework to forecast corn yields in three US Corn Belt states (Illinois, Indiana, and Iowa) considering complete and partial inseason weather knowledge. Several ensemble models are designed using blocked sequential procedure to generate out-of-bag predictions. The forecasts are made in county-level scale and aggregated for agricultural district and state level scales. Results show that the proposed optimized weighted ensemble and the average ensemble are the most precise models with RRMSE of 9.5%. Stacked LASSO makes the least biased predictions (MBE of 53 kg/ha), while other ensemble models also outperformed the base learners in terms of bias. On the contrary, although random k-fold cross-validation is replaced by blocked sequential procedure, it is shown that stacked ensembles perform not as good as weighted ensemble models for time series data sets as they require the data to be non-IID to perform favorably. Comparing our proposed model forecasts with the literature demonstrates the acceptable performance of forecasts made by our proposed ensemble model. Results from the scenario of having partial in-season weather knowledge reveals that decent yield forecasts with RRMSE of 9.2% can be made as early as June 1 st. Moreover, it was shown that the proposed model performed better than individual models and benchmark ensembles at agricultural district and statelevel scales as well as county-level scale. To find the marginal effect of each input feature on the forecasts made by the proposed ensemble model, a methodology is suggested that is the basis for finding feature importance for the ensemble model. The findings

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“…Similar results were obtained by other researchers. Johann et al compared computational models based on ANN and ANFIS in estimating the soil moisture from the stochastic information of the horizontal and vertical forces acting on a no-till chisel opener using autoregressive error function parameters [31].…”

Section: Soft Computing Modelsmentioning

confidence: 99%

Application of Soft Computing Techniques for the Analysis of Tractive Properties of a Low-Power Agricultural Tractor under Various Soil Conditions

2020

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Considering the fuel consumption and soil compaction, optimization of the performance of tractors is crucial for modern agricultural practices. The tractive performance is influenced by many factors, making it difficult to be modeled. In this work, the traction force and tractive efficiency of a low-power tractor, as affected by soil coefficient, vertical load, horizontal deformation, soil compaction, and soil moisture, were studied. The optimal work of a tractor is a compromise between the maximum traction force and the maximum tractive efficiency. Optimizing these factors is complex and requires accurate models. To this end, the performances of soft computing approaches, including neural networks, genetic algorithms, and adaptive network fuzzy inference system, were evaluated. The optimal performance was realized by neural networks trained by backpropagation as well as backpropagation combined with a genetic algorithm, with a coefficient of determination of 0.955 for the traction force and 0.954 for the tractive efficiency. Based on models with the best accuracy, a sensitivity analysis was performed. The results showed that the traction performance is mainly influenced by the soil type; nevertheless, the vertical load and soil moisture also exhibited a relatively strong influence.

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Soil moisture modeling based on stochastic behavior of forces on a no-till chisel opener

Cited by 40 publications

References 21 publications

Machine Learning in Agriculture: A Review

Machine Learning in Agriculture: A Review

Forecasting Corn Yield With Machine Learning Ensembles

Application of Soft Computing Techniques for the Analysis of Tractive Properties of a Low-Power Agricultural Tractor under Various Soil Conditions

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