Developing machine learning models for wheat yield prediction using ground-based data, satellite-based actual evapotranspiration and vegetation indices

Jahromi, Mojtaba Naghdyzadegan; Zand‐Parsa, Shahrokh; Razzaghi, Fatemeh; Jamshidi, Sajad; Didari, Shohreh; Doosthosseini, Ali; Pourghasemi, Hamid Reza

doi:10.1016/j.eja.2023.126820

Cited by 14 publications

(3 citation statements)

References 95 publications

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“…These quality images make it possible to extract and calculate relevant indicators, such as the normalized difference vegetation index (NDVI), enhanced vegetation index, crop water stress index, green vegetation index, and soil-adjusted vegetation index 11 . Using these indices, researchers have developed analytical and empirical models to monitor crops 12 , 13 and predict crop yields 14 , 15 . Lai et al 16 .…”

Section: Introductionmentioning

confidence: 99%

CBTA: a CNN-BiGRU method with triple attention for winter wheat yield prediction

Ye,

Ma,

Jin

et al. 2024

J. Appl. Rem. Sens.

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Timely and accurate prediction of winter wheat yield contributes to ensuring national food security. We propose a CNN-bidirectional gated recurrent unit method with triple attention for winter wheat yield prediction, named CBTA. This deep learning model uses convolutional neural networks to mine the spatial spectral information in hyperspectral remote sensing images. Furthermore, the bidirectional gated recurrent unit is used to adaptively learn the time dependence between the various stages of winter wheat growth. Data from Henan Province, China, is used in this study to train the model and also verify its prediction performance and stability. The results from our experiment show that our proposed model has an excellent effect on yield prediction in the county, with root-mean-square-error, mean absolute error, and R 2 of 0.469 t∕ha, 0.336 t∕ha, and 0.827, respectively. Moreover, our findings suggested that the precision of our model using the data from sowing to heading-flowering stage was very close to that from sowing to ripening stage, which proves that the CBTA model can accurately predict the yield of winter wheat 1 to 2 months in advance.

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Section: Introductionmentioning

confidence: 99%

CBTA: a CNN-BiGRU method with triple attention for winter wheat yield prediction

Ye,

Ma,

Jin

et al. 2024

J. Appl. Rem. Sens.

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show abstract

“…In the past, satellite and manned aircraft data were expensive. Currently, satellite data are readily accessible by open access and have been widely employed for agricultural water management 8 , 9 and crop monitoring 10 , 11 . Nevertheless, their effectiveness in precision agriculture is restricted owing to their limitations in spatiotemporal resolution 12 , 13 and depending on the atmospheric conditions.…”

Section: Introductionmentioning

confidence: 99%

Accuracy evaluation of reflectance, normalized difference vegetation index, and normalized difference water index using corrected unmanned aerial vehicle multispectral images by bidirectional reflectance distribution function and solar irradiance

Jin,

Kim,

Kim

et al. 2023

J. Appl. Rem. Sens.

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.In precision agriculture, vegetation and soil are monitored by multispectral sensors that can observe outside the visible bands. In contrast to satellites and manned aircraft, unmanned aerial vehicles (UAVs) allow anyone to easily acquire near-real time data at a reasonable price. However, UAV images do not account for the anisotropic reflectance and solar irradiance from the ground surface, so extracting the reflectance of vegetation is difficult. To solve this problem, this study developed a bidirectional reflectance distribution function (BRDF) that expresses the anisotropic reflectance of the Earth’s surface as a function of the geometric relationship with the UAV sensor and the Sun. To compensate for the effect of changes in solar incident energy due to clouds and solar irradiance, the solar irradiance was measured and corrected on the ground rather than in the air to avoid errors due to the flight attitude. Before processing by the BRDF and correcting for the solar irradiance, the UAV obtained striated orthomosaic images for which the vegetation indices were affected by the position and attitude of the Sun and the UAV sensor. After the correction, consistent values were calculated for the vegetation indices throughout the images. The accuracy of the UAV data was analyzed by comparison with Sentinel 2A. Reflectance differences are 0.02% to 6.37% from the image without correction. After applying the correction, it reduced to 0.27%, 0.61%, 0.16%, and 0.65% from the blue, green, red, and near-infrared bands, respectively. This study is valuable for obtaining accurate values for vegetation indices under a wide range of weather and geometric conditions at different sites because UAVs to collect images are a rare case under optimal conditions.

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“…Global rainfall is unevenly distributed, and winter wheat needs multiple SI during the growth period to achieve high yields [25,26]. However, over irrigation causes the depletion of groundwater resources and decreases water utilization efficiency, which seriously threatens the sustainable development of agriculture [27].…”

Section: Introductionmentioning

confidence: 99%

Improving Stem Lodging Resistance, Yield, and Water Efficiency of Wheat by Adjusting Supplemental Irrigation Frequency

Feng,

Shi,

Wang

et al. 2023

Agronomy

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Optimizing supplemental irrigation (SI) measures and enhancing stem lodging resistance can be the keys to achieving a high and stable yield and high efficiency in wheat. The experiment was established as a two-factor field trial in 2018–2020. We used four SI combinations at different stages: rain-fed (T0), SI at jointing (T1), SI at jointing + anthesis (T2), and SI at regreening + jointing + anthesis (T3) with ‘Bainong4199’ (BN4199) and ‘Zhoumai18’ (ZM18) as experimental materials. We researched the effects of different SI combinations on the stem characteristics, stem vigor, grain filling, and yield of winter wheat. The results suggest that the basal internode at the anthesis stage grew with the increase in SI amount, but the stem fracture resistance of T1 and T2 was higher than that of T0 and T3. As grain filling continued, the lodging index increased and stem vigor decreased. In comparison with T3, the average stem lodging index of T2 decreased by 21.92% for ‘BN4199’ and 36.63% for ‘ZM18’, but the WUE increased by 29.76% and 14.92%, respectively. The grain yield increased with the increase in irrigation times during the growth period; there was no significant difference between T2 and T3 in 2018–2019. In a biennial comparison, the grain yield of all treatments in 2019–2020 was significantly lower than those in 2018–2019, and the grain yield of ‘ZM 18’ was lower than that of ‘BN 4199’. Correlation analysis displayed that there were significant positive correlations between post-anthesis stem vigor and the dry matter contribution rate of post-anthesis to grains and between the grain filling rate at 21–28 days after anthesis (DAA) and stem strength at 30 DAA. In summary, selecting a high-yield lodging-resistant wheat variety with SI at jointing and anthesis was beneficial for forming strong stems and maintaining higher stem vigor at the later growth stage for grain filling, which reduced lodging risk and ensured high yield and high WUE.

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Developing machine learning models for wheat yield prediction using ground-based data, satellite-based actual evapotranspiration and vegetation indices

Cited by 14 publications

References 95 publications

CBTA: a CNN-BiGRU method with triple attention for winter wheat yield prediction

CBTA: a CNN-BiGRU method with triple attention for winter wheat yield prediction

Accuracy evaluation of reflectance, normalized difference vegetation index, and normalized difference water index using corrected unmanned aerial vehicle multispectral images by bidirectional reflectance distribution function and solar irradiance

Improving Stem Lodging Resistance, Yield, and Water Efficiency of Wheat by Adjusting Supplemental Irrigation Frequency

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