Machine Learning Regression Techniques for the Silage Maize Yield Prediction Using Time-Series Images of Landsat 8 OLI

Abstract. Considering the high maize yield loses that are caused by diseases incidences as well as incomprehensive monitoring initiatives in the crop farming sector of agriculture, there is a need to come up with spatially explicit, cheap, fast and consistent approaches for monitoring as well as forecasting food crop diseases, such as maize gray leaf spot. This study, therefore, we sought to investigate the usability, strength and practicality of the forthcoming HyspIRI in detecting disease progression of Maize Gray leafy spot infections in relation to the Sentinel-2 MSI, Landsat 8 OLI spectral configurations. Maize Gray leafy spot disease progression that were discriminated based on partial least squares –discriminant analysis (PLS-DA) algorithm were (i) healthy, (ii) intermediate and (ii) severely infected maize crops. Comparatively, the results show that the HyspIRI’s simulated spectral settings slightly performed better than those of Sentinel-2 MSI, VENμS and Landsat 8 OLI sensor. HyspIRI exhibited an overall accuracy of 0.98 compared to 0.95, 0.93 and 0.89 exhibited by Sentinel-2 MSI, VENμS and Landsat 8 OLI sensor sensors, respectively. Further, the results showed that the visible section the red-edge and NIR covered by all the four sensors were the most influential spectral regions for discriminating different Maize Gray leafy spot infections. These findings underscore the added value and potential scientific breakthroughs likely to be brought about by the upcoming hyperspectral HyspIRI sensor in precision agriculture and forecasting of crop disease epidemics to ensure food security.

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Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US Midwest

Environmental Research Letters ◽

10.1088/1748-9326/ab7df9 ◽

2020 ◽

Vol 15 (6) ◽

pp. 064005

Author(s):

Yanghui Kang ◽

Mutlu Ozdogan ◽

Xiaojin Zhu ◽

Zhiwei Ye ◽

Christopher Hain ◽

...

Keyword(s):

Machine Learning ◽

Environmental Variables ◽

Learning Algorithms ◽

Maize Yield ◽

Comparative Assessment ◽

Machine Learning Algorithms ◽

Yield Prediction ◽

The Us ◽

Us Midwest

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Combining Optical, Fluorescence, Thermal Satellite, and Environmental Data to Predict County-Level Maize Yield in China Using Machine Learning Approaches

Remote Sensing ◽

10.3390/rs12010021 ◽

2019 ◽

Vol 12 (1) ◽

pp. 21 ◽

Cited By ~ 6

Author(s):

Liangliang Zhang ◽

Zhao Zhang ◽

Yuchuan Luo ◽

Juan Cao ◽

Fulu Tao

Keyword(s):

Machine Learning ◽

Crop Yield ◽

Satellite Data ◽

Maize Yield ◽

Environmental Data ◽

Yield Prediction ◽

County Level ◽

Climate Data ◽

Source Data ◽

Optical Fluorescence

Maize is an extremely important grain crop, and the demand has increased sharply throughout the world. China contributes nearly one-fifth of the total production alone with its decreasing arable land. Timely and accurate prediction of maize yield in China is critical for ensuring global food security. Previous studies primarily used either visible or near-infrared (NIR) based vegetation indices (VIs), or climate data, or both to predict crop yield. However, other satellite data from different spectral bands have been underutilized, which contain unique information on crop growth and yield. In addition, although a joint application of multi-source data significantly improves crop yield prediction, the combinations of input variables that could achieve the best results have not been well investigated. Here we integrated optical, fluorescence, thermal satellite, and environmental data to predict county-level maize yield across four agro-ecological zones (AEZs) in China using a regression-based method (LASSO), two machine learning (ML) methods (RF and XGBoost), and deep learning (DL) network (LSTM). The results showed that combining multi-source data explained more than 75% of yield variation. Satellite data at the silking stage contributed more information than other variables, and solar-induced chlorophyll fluorescence (SIF) had an almost equivalent performance with the enhanced vegetation index (EVI) largely due to the low signal to noise ratio and coarse spatial resolution. The extremely high temperature and vapor pressure deficit during the reproductive period were the most important climate variables affecting maize production in China. Soil properties and management factors contained extra information on crop growth conditions that cannot be fully captured by satellite and climate data. We found that ML and DL approaches definitely outperformed regression-based methods, and ML had more computational efficiency and easier generalizations relative to DL. Our study is an important effort to combine multi-source remote sensed and environmental data for large-scale yield prediction. The proposed methodology provides a paradigm for other crop yield predictions and in other regions.

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Mapping of rice growth phases and bare land using Landsat-8 OLI with machine learning

International Journal of Remote Sensing ◽

10.1080/01431161.2020.1779378 ◽

2020 ◽

Vol 41 (21) ◽

pp. 8428-8452 ◽

Cited By ~ 1

Author(s):

Fadhlullah Ramadhani ◽

Reddy Pullanagari ◽

Gabor Kereszturi ◽

Jonathan Procter

Keyword(s):

Machine Learning ◽

Landsat 8 ◽

Growth Phases ◽

Landsat 8 Oli ◽

Rice Growth ◽

Bare Land ◽

Land Using

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A comparison of machine learning algorithms for regional wheat yield prediction using NDVI time series of SPOT-VGT

2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics) ◽

10.1109/agro-geoinformatics.2016.7577625 ◽

2016 ◽

Cited By ~ 1

Author(s):

Michiel Stas ◽

Jos Van Orshoven ◽

Qinghan Dong ◽

Stien Heremans ◽

Beier Zhang

Keyword(s):

Machine Learning ◽

Time Series ◽

Learning Algorithms ◽

Wheat Yield ◽

Machine Learning Algorithms ◽

Yield Prediction ◽

Ndvi Time Series

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Predicting Maize Yield at the Plot Scale of Different Fertilizer Systems by Multi-Source Data and Machine Learning Methods

Remote Sensing ◽

10.3390/rs13183760 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3760

Author(s):

Linghua Meng ◽

Huanjun Liu ◽

Susan L. Ustin ◽

Xinle Zhang

Keyword(s):

Machine Learning ◽

Crop Yield ◽

Satellite Data ◽

Maize Yield ◽

Environmental Data ◽

Yield Prediction ◽

Climate Data ◽

Multiple Sources ◽

Adaptive Boosting ◽

Source Data

Timely and reliable maize yield prediction is essential for the agricultural supply chain and food security. Previous studies using either climate or satellite data or both to build empirical or statistical models have prevailed for decades. However, to what extent climate and satellite data can improve yield prediction is still unknown. In addition, fertilizer information may also improve crop yield prediction, especially in regions with different fertilizer systems, such as cover crop, mineral fertilizer, or compost. Machine learning (ML) has been widely and successfully applied in crop yield prediction. Here, we attempted to predict maize yield from 1994 to 2007 at the plot scale by integrating multi-source data, including monthly climate data, satellite data (i.e., vegetation indices (VIs)), fertilizer data, and soil data to explore the accuracy of different inputs to yield prediction. The results show that incorporating all of the datasets using random forests (RF) and AB (adaptive boosting) can achieve better performances in yield prediction (R2: 0.85~0.98). In addition, the combination of VIs, climate data, and soil data (VCS) can predict maize yield more effectively than other combinations (e.g., combinations of all data and combinations of VIs and soil data). Furthermore, we also found that including different fertilizer systems had different prediction accuracies. This paper aggregates data from multiple sources and distinguishes the effects of different fertilization scenarios on crop yield predictions. In addition, the effects of different data on crop yield were analyzed in this study. Our study provides a paradigm that can be used to improve yield predictions for other crops and is an important effort that combines multi-source remotely sensed and environmental data for maize yield prediction at the plot scale and develops timely and robust methods for maize yield prediction grown under different fertilizing systems.

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