Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

Liu, Yadong; Kim, Junhwan; Fleisher, David H.; Kim, Kwang S.

doi:10.3390/rs13163069

Cited by 3 publications

(1 citation statement)

References 54 publications

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“…We report these metrics because Chai and Draxler (2014) showed that both MAPE/MAE and RMSE may reveal more about model performance when combined [ 50 ].…”

Section: Methodsmentioning

confidence: 99%

Spatial and Spectral Dependencies of Maize Yield Estimation Using Remote Sensing

Burglewski,

Srinivasagan,

Ketterings

et al. 2024

Sensors

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Corn (Zea mays L.) is the most abundant food/feed crop, making accurate yield estimation a critical data point for monitoring global food production. Sensors with varying spatial/spectral configurations have been used to develop corn yield models from intra-field (0.1 m ground sample distance (GSD)) to regional scales (>250 m GSD). Understanding the spatial and spectral dependencies of these models is imperative to result interpretation, scaling, and deploying models. We leveraged high spatial resolution hyperspectral data collected with an unmanned aerial system mounted sensor (272 spectral bands from 0.4–1 μm at 0.063 m GSD) to estimate silage yield. We subjected our imagery to three band selection algorithms to quantitatively assess spectral reflectance features applicability to yield estimation. We then derived 11 spectral configurations, which were spatially resampled to multiple GSDs, and applied to a support vector regression (SVR) yield estimation model. Results indicate that accuracy degrades above 4 m GSD across all configurations, and a seven-band multispectral sensor which samples the red edge and multiple near-infrared bands resulted in higher accuracy in 90% of regression trials. These results bode well for our quest toward a definitive sensor definition for global corn yield modeling, with only temporal dependencies requiring additional investigation.

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“…We report these metrics because Chai and Draxler (2014) showed that both MAPE/MAE and RMSE may reveal more about model performance when combined [ 50 ].…”

Section: Methodsmentioning

confidence: 99%

Spatial and Spectral Dependencies of Maize Yield Estimation Using Remote Sensing

Burglewski,

Srinivasagan,

Ketterings

et al. 2024

Sensors

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Assessment of the relevance of features associated with corn crop yield prediction in Colombia, a country in the Neotropical zone

Sierra-Forero,

Baron-Velandia,

Vanegas-Ayala

2024

Int. j. inf. tecnol.

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Obtaining highly accurate predictive models to precisely estimate corn crop yields is essential for making informed decisions in a sustainable agriculture environment. There are various approaches to achieve this goal, including models based on Fuzzy Logic, Association Rules, and Machine Learning. However, some of these models have limitations in terms of the accuracy of their predictions, attributable to the high complexity and non-linearity in the interactions between factors. While Machine Learning techniques alone can achieve high precision, the inclusion of multiple attributes can reduce it. This study focuses on identifying the most influential factors at the regional level through a comprehensive analysis of the relevance of features associated with corn crop yields in Colombia, a country in the Neotropical zone. To accomplish this, climatological time series and historical yield records are used through a methodology based on CRISP- DM, widely used in the field of data mining, involving a review of related work, data cleaning and transformation, relevance evaluation using the RReliefF algorithm, and verification of the performance of the most influential factors through prediction algorithms. The results obtained demonstrate that solar radiation, precipitation, vapor pressure, and maximum and minimum temperatures exert the greatest influence on corn crop yield prediction, with a relevance factor of 0.033, 0.032, 0.026, 0.022, and 0.021, respectively. In the validation of the performance of the selected factors, two predictive models were implemented. The first, based on Artificial Neural Networks, yielded a RMSE of 0.1216 with the subset of variables and 0.1403 with all available variables. In the second, Linear Regression was applied, resulting in an RMSE of 0.1417 with the subset of variables and 0.1424 with all available variables. These results highlight the importance of the selected features as the most influential climatic factors in defining highly accurate predictive models in the Neotropical zone.

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Early-Season Crop Mapping by PRISMA Images Using Machine/Deep Learning Approaches: Italy and Iran Test Cases

Mirzaei,

Pascucci,

Carfora

et al. 2024

Remote Sensing

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Despite its high importance for crop yield prediction and monitoring, early-season crop mapping is severely hampered by the absence of timely ground truth. To cope with this issue, this study aims at evaluating the capability of PRISMA hyperspectral satellite images compared with Sentinel-2 multispectral imagery to produce early- and in-season crop maps using consolidated machine and deep learning algorithms. Results show that the accuracy of crop type classification using Sentinel-2 images is meaningfully poor compared with PRISMA (14% in overall accuracy (OA)). The 1D-CNN algorithm, with 89%, 91%, and 92% OA for winter, summer, and perennial cultivations, respectively, shows for the PRISMA images the highest accuracy in the in-season crop mapping and the fastest algorithm that achieves acceptable accuracy (OA 80%) for the winter, summer, and perennial cultivations early-season mapping using PRISMA images. Moreover, the 1D-CNN algorithm shows a limited reduction (6%) in performance, appearing to be the best algorithm for crop mapping within operational use in cross-farm applications. Machine/deep learning classification algorithms applied on the test fields cross-scene demonstrate that PRISMA hyperspectral time series images can provide good results for early- and in-season crop mapping.

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Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

Cited by 3 publications

References 54 publications

Spatial and Spectral Dependencies of Maize Yield Estimation Using Remote Sensing

Spatial and Spectral Dependencies of Maize Yield Estimation Using Remote Sensing

Assessment of the relevance of features associated with corn crop yield prediction in Colombia, a country in the Neotropical zone

Early-Season Crop Mapping by PRISMA Images Using Machine/Deep Learning Approaches: Italy and Iran Test Cases

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