Field-scale yield prediction of winter wheat under different irrigation regimes based on dynamic fusion of multimodal UAV imagery

Ma, Juncheng; Liu, Binhui; Lin, Ji; Zhu, Zhicheng; Wu, Yongfeng; Jiao, Weihua

doi:10.1016/j.jag.2023.103292

Cited by 11 publications

(10 citation statements)

References 28 publications

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“…Water deficits could cause local variations in winter wheat canopies [16,45]. In this study, the well-watered treatment had dense canopy structures.…”

Section: Performance Of the Yield Prediction Model Across Water Treat...mentioning

confidence: 77%

“…The sensitivity to these factors varies between cultivars, regions, site conditions, and growth stages [9,10], which causes varying levels of impact on the yield. Therefore, timely and accurate wheat yield prediction at the field scale can help field-specific management be performed [4,[11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…UAVbased RGB [8,29,30] and multispectral [6,8,26,30] imagery are the two main image types for imagery-based yield prediction. Although the RGB imagery contains less canopy spectral information than the multispectral imagery, it has better ground resolution and, therefore, more informative canopy spatial features [16,19]. Consequently, CNNs with RGB imagery are not necessarily inferior to those with multispectral imagery on field-scale yield prediction [6,26,29].…”

Section: Introductionmentioning

confidence: 99%

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Wheat Yield Prediction Using Unmanned Aerial Vehicle RGB-Imagery-Based Convolutional Neural Network and Limited Training Samples

Ma,

Wu,

Liu

et al. 2023

Remote Sensing

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Low-cost UAV RGB imagery combined with deep learning models has demonstrated the potential for the development of a feasible tool for field-scale yield prediction. However, collecting sufficient labeled training samples at the field scale remains a considerable challenge, significantly limiting the practical use. In this study, a split-merge framework was proposed to address the issue of limited training samples at the field scale. Based on the split-merge framework, a yield prediction method for winter wheat using the state-of-the-art Efficientnetv2_s (Efficientnetv2_s_spw) and UAV RGB imagery was presented. In order to demonstrate the effectiveness of the split-merge framework, in this study, Efficientnetv2_s_pw was built by directly feeding the plot images to Efficientnetv2_s. The results indicated that the proposed split-merge framework effectively enlarged the training samples, thus enabling improved yield prediction performance. Efficientnetv2_s_spw performed best at the grain-filling stage, with a coefficient of determination of 0.6341 and a mean absolute percentage error of 7.43%. The proposed split-merge framework improved the model ability to extract indicative image features, partially mitigating the saturation issues. Efficientnetv2_s_spw demonstrated excellent adaptability across the water treatments and was recommended at the grain-filling stage. Increasing the ground resolution of input images may further improve the estimation performance. Alternatively, improved performance may be achieved by incorporating additional data sources, such as the canopy height model (CHM). This study indicates that Efficientnetv2_s_spw is a promising tool for field-scale yield prediction of winter wheat, providing a practical solution to field-specific crop management.

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“…Water deficits could cause local variations in winter wheat canopies [16,45]. In this study, the well-watered treatment had dense canopy structures.…”

Section: Performance Of the Yield Prediction Model Across Water Treat...mentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Wheat Yield Prediction Using Unmanned Aerial Vehicle RGB-Imagery-Based Convolutional Neural Network and Limited Training Samples

Ma,

Wu,

Liu

et al. 2023

Remote Sensing

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“…Due to their adjustable flight altitude and high spatial resolution, camera data can be reliably collected from small areas or plots [10,11,22,29]. An additional advantage of drones is that they make it possible-among other applications-to forecast harvests [30][31][32]. However, alternative options for field data collection exist alongside remote-sensing techniques.…”

Section: Introductionmentioning

confidence: 99%

Yield Prediction Using NDVI Values from GreenSeeker and MicaSense Cameras at Different Stages of Winter Wheat Phenology

Zsebő,

Bede,

Kukorelli

et al. 2024

Drones

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This work aims to compare and statistically analyze Normalized Difference Vegetation Index (NDVI) values provided by GreenSeeker handheld crop sensor measurements and calculate NDVI values derived from the MicaSense RedEdge-MX Dual Camera, to predict in-season winter wheat (Triticum aestivum L.) yield, improving a yield prediction model with cumulative growing degree days (CGDD) and days from sowing (DFS) data. The study area was located in Mosonmagyaróvár, Hungary. A small-scale field trial in winter wheat was constructed as a randomized block design including Environmental: N-135.3, P2O5-77.5, K2O-0; Balance: N-135.1, P2O5-91, K2O-0; Genezis: N-135, P2O5-75, K2O-45; and Control: N, P, K 0 kg/ha. The crop growth was monitored every second week between April and June 2022 and 2023, respectively. NDVI measurements recorded by GreenSeeker were taken at three pre-defined GPS points for each plot; NDVI values based on the MicaSense camera Red and NIR bands were calculated for the same points. Results showed a significant difference (p ≤ 0.05) between the Control and treated areas by GreenSeeker measurements and Micasense-based calculated NDVI values throughout the growing season, except for the heading stage. At the heading stage, significant differences could be measured by GreenSeeker. However, remotely sensed images did not show significant differences between the treated and Control parcels. Nevertheless, both sensors were found suitable for yield prediction, and 226 DAS was the most appropriate date for predicting winter wheat’s yield in treated plots based on NDVI values and meteorological data.

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“…Moreover, these conventional techniques are constrained by their limited capacity to obtain samples, often leading to incomplete and non-real-time yield data. They can also inadvertently cause soil compaction and damage ( Ma et al., 2023 ). The emergence of drone remote sensing technology has revolutionized agricultural practices.…”

Section: Introductionmentioning

confidence: 99%

An integrated feature selection approach to high water stress yield prediction

Li,

Zhou,

Cheng

et al. 2023

Front. Plant Sci.

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The timely and precise prediction of winter wheat yield plays a critical role in understanding food supply dynamics and ensuring global food security. In recent years, the application of unmanned aerial remote sensing has significantly advanced agricultural yield prediction research. This has led to the emergence of numerous vegetation indices that are sensitive to yield variations. However, not all of these vegetation indices are universally suitable for predicting yields across different environments and crop types. Consequently, the process of feature selection for vegetation index sets becomes essential to enhance the performance of yield prediction models. This study aims to develop an integrated feature selection method known as PCRF-RFE, with a focus on vegetation index feature selection. Initially, building upon prior research, we acquired multispectral images during the flowering and grain filling stages and identified 35 yield-sensitive multispectral indices. We then applied the Pearson correlation coefficient (PC) and random forest importance (RF) methods to select relevant features for the vegetation index set. Feature filtering thresholds were set at 0.53 and 1.9 for the respective methods. The union set of features selected by both methods was used for recursive feature elimination (RFE), ultimately yielding the optimal subset of features for constructing Cubist and Recurrent Neural Network (RNN) yield prediction models. The results of this study demonstrate that the Cubist model, constructed using the optimal subset of features obtained through the integrated feature selection method (PCRF-RFE), consistently outperformed the RNN model. It exhibited the highest accuracy during both the flowering and grain filling stages, surpassing models constructed using all features or subsets derived from a single feature selection method. This confirms the efficacy of the PCRF-RFE method and offers valuable insights and references for future research in the realms of feature selection and yield prediction studies.

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Field-scale yield prediction of winter wheat under different irrigation regimes based on dynamic fusion of multimodal UAV imagery

Cited by 11 publications

References 28 publications

Wheat Yield Prediction Using Unmanned Aerial Vehicle RGB-Imagery-Based Convolutional Neural Network and Limited Training Samples

Wheat Yield Prediction Using Unmanned Aerial Vehicle RGB-Imagery-Based Convolutional Neural Network and Limited Training Samples

Yield Prediction Using NDVI Values from GreenSeeker and MicaSense Cameras at Different Stages of Winter Wheat Phenology

An integrated feature selection approach to high water stress yield prediction

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