Estimating Soil Moisture Over Winter Wheat Fields During Growing Season Using Machine-Learning Methods

Chen, Lin; Xing, Minfeng; He, Bin; Wang, Jinfei; Shang, Jiali; Huang, Xiaodong; Xu, Min

doi:10.1109/jstars.2021.3067890

Cited by 53 publications

(26 citation statements)

References 51 publications

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“…Among the model parameters of SVR, gamma (kernel parameter) and C (penalty coefficient) have great influences on the estimation results, so they should be optimized through parameter tuning [17,27,36]. In addition, the kernels of linear, radial basis function (RBF) and sigmoid are commonly used; they were applied and tested in this study.…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

“…Recursive feature elimination (RFE) is another widely used method for variable selection. The method uses a base model to perform multiple rounds of training, during which the weakest features are eliminated until a specified number of features is reached [27,28]. The base model could be SVM [27], RF [29], or other regression models.…”

Section: Introductionmentioning

confidence: 99%

“…The method uses a base model to perform multiple rounds of training, during which the weakest features are eliminated until a specified number of features is reached [27,28]. The base model could be SVM [27], RF [29], or other regression models. Nevertheless, there have been few previous studies that used variable selection in remote sensing of vegetation chlorophyll contents, although variable selection is critical.…”

Section: Introductionmentioning

confidence: 99%

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UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

et al. 2021

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In recent years, the delay in sowing has become a major obstacle to high wheat yield in Jiangsu Province, one of the major wheat producing areas in China; hence, it is necessary to screen wheat varieties are resilient for late sowing. This study aimed to provide an effective, fast, and non-destructive monitoring method of soil plant analysis development (SPAD) values, which can represent leaf chlorophyll contents, for late-sown winter wheat variety screening. This study acquired multispectral images using an unmanned aerial vehicle (UAV) at the overwintering stage of winter wheat growth, and further processed these images to extract reflectance of five single spectral bands and calculated 26 spectral vegetation indices. Based on these 31 variables, this study combined three variable selection methods (i.e., recursive feature elimination (RFE), random forest (RF), and Pearson correlation coefficient (r)) with four machine learning algorithms (i.e., random forest regression (RFR), linear kernel-based support vector regression (SVR), radial basis function (RBF) kernel-based SVR, and sigmoid kernel-based SVR), resulted in seven SVR models (i.e., RFE-SVR_linear, RF-SVR_linear, RF-SVR_RBF, RF-SVR_sigmoid, r-SVR_linear, r-SVR_RBF, and r-SVR_sigmoid) and three RFR models (i.e., RFE-RFR, RF-RFR, and r-RFR). The performances of the 10 machine learning models were evaluated and compared with each other according to the achieved coefficient of determination (R2), residual prediction deviation (RPD), root mean square error (RMSE), and relative RMSE (RRMSE) in SPAD estimation. Of the 10 models, the best one was the RF-SVR_sigmoid model, which was the combination of the RF variable selection method and the sigmoid kernel-based SVR algorithm. It achieved high accuracy in estimating SPAD values of the wheat canopy (R2 = 0.754, RPD = 2.017, RMSE = 1.716 and RRMSE = 4.504%). The newly developed UAV- and machine learning-based model provided a promising and real time method to monitor chlorophyll contents at the overwintering stage, which can benefit late-sown winter wheat variety screening.

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Section: Support Vector Regression (Svr)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

et al. 2021

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“…Chen et al [8] have proposed "Estimation of Soil Moisture Over Winter Wheat Fields During Growing Season" by making use of machine learning methods with highly nonlinear tuning capabilities not limited to physical parameters. Machine-Learning methods were used over winter wheat fields to estimate soil moisture during its growing season.…”

Section: Literature Surveymentioning

confidence: 99%

Mulberry Leaf Yield Prediction Using Machine Learning Techniques

Srikantaiah¹,

Deeksha²

2021

Atlantis Highlights in Computer Sciences

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Soil nutrients are essential for the growth of healthy crops. India produces a humungous quantity of Mulberry leaves which in turn produces the raw silk. Since the climatic conditions in India is favourable, Mulberry is grown throughout the year. Majority of the farmers hardly pay attention to the nature of soil and abiotic factors due to which leaves become malnutritious and thus when they are consumed by the silkworm, desired quality end-product, raw silk, will not be produced. It is beneficial for the farmers to know the amount of yield that their land can produce so that they can plan in advance. In this paper, different Machine Learning techniques are used in predicting the yield of the Mulberry crops based on the soil parameters. Three advanced machine-learning models are selected and compared, namely, Multiple linear regression, Ridge regression and Random Forest Regression (RF). The experimental results show that Random Forest Regression outperforms other algorithms.

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“…During the growth season of the crops, A.Sharma et al suggested assessing soil moisture over winter wheat fields using machine-learning algorithms [2]. RADARSAT-2 data was gathered using quad polarizations and 240 sample plots.…”

Section: Introductionmentioning

confidence: 99%

Review of Machine learning models for Crop Yield Prediction

Aravind¹

2021

Proceedings of the First International Conference on Combinatorial and Optimization, ICCAP 2021, December 7-8 2021, Chennai, In

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Agriculture is not only a necessary component of human life, but it is also one of India's most important sources of employment. Agriculture employs more than half of our country's population. It is the economic backbone of our country. Farmers can benefit from early diagnosis and control of issues in order to increase crop productivity. Crop yield prediction is an essential scientific area that aids in food security. Based on many parameters, a machine learning model will grasp the pattern of the crop and yield and estimate the yield of the region in which the farmer would crop. The machine learning algorithm can then be distributed after implementation with a web-based visual application that is convenient to use. The results obtained will be granted access to the farmers. This study investigates various machine learning models that are employed in predicting the crop yield.

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Estimating Soil Moisture Over Winter Wheat Fields During Growing Season Using Machine-Learning Methods

Cited by 53 publications

References 51 publications

UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

UAV- and Machine Learning-Based Retrieval of Wheat SPAD Values at the Overwintering Stage for Variety Screening

Mulberry Leaf Yield Prediction Using Machine Learning Techniques

Review of Machine learning models for Crop Yield Prediction

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