Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning

Khaki, Saeed; Pham, Hieu; Wang, Lizhi

doi:10.1038/s41598-021-89779-z

Cited by 100 publications

(64 citation statements)

References 55 publications

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“…They found RNN-CNN model achieved a root-mean-square-error 9% for corn and 8% for soybean, which outperformed all other implemented methods. Later, in 2021 [21], the researchers used expanded data collected from more counties across the United States, i.e., covered 1132 counties for corn and 1076 counties for soybean. They proposed a new convolutional neural network model called YieldNet, which utilised transfer learning between corn and soybean yield predictions by sharing the weights of the backbone feature extractor.…”

Section: Machine Learning Applied To Remotely Sensed Datamentioning

confidence: 99%

Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks

et al. 2021

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Farm-scale crop yield prediction is a natural development of sustainable agriculture, producing a rich amount of food without depleting and polluting environmental resources. Recent studies on crop yield production are limited to regional-scale predictions. The regional-scale crop yield predictions usually face challenges in capturing local yield variations based on farm management decisions and the condition of the field. For this research, we identified the need to create a large and reusable farm-scale crop yield production dataset, which could provide precise farm-scale ground-truth prediction targets. Therefore, we utilise multi-temporal data, such as Sentinel-2 satellite images, weather data, farm data, grain delivery data, and cadastre-specific data. We introduce a deep hybrid neural network model to train this multi-temporal data. This model combines the features of convolutional layers and recurrent neural networks to predict farm-scale crop yield production across Norway. The proposed model could efficiently make the target predictions with the mean absolute error of 76 kg per 1000 m2. In conclusion, the reusable farm-scale multi-temporal crop yield dataset and the proposed novel model could meet the actual requirements for the prediction targets in this paper, providing further valuable insights for the research community.

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Section: Machine Learning Applied To Remotely Sensed Datamentioning

confidence: 99%

Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks

et al. 2021

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“…Recently, a study used MODIS satellite data for the large-scale prediction of maize (Zea mays) and soybean yield. The proposed deep CNN outperformed six other machine and deep learning models and predicted the yield across 2208 counties in the USA with an error of 8.74% [21]. In another study, a deep CNN trained with growers' field images (RGB and NDVI), was used for predicting wheat and barley (Hordeum vulgare) yield one month after sowing, with an 8.8% of error [22].…”

Section: Introductionmentioning

confidence: 95%

Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection

et al. 2021

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Assessing crop production in the field often requires breeders to wait until the end of the season to collect yield-related measurements, limiting the pace of the breeding cycle. Early prediction of crop performance can reduce this constraint by allowing breeders more time to focus on the highest-performing varieties. Here, we present a multimodal deep learning model for predicting the performance of maize (Zea mays) at an early developmental stage, offering the potential to accelerate crop breeding. We employed multispectral images and eight vegetation indices, collected by an uncrewed aerial vehicle approximately 60 days after sowing, over three consecutive growing cycles (2017, 2018 and 2019). The multimodal deep learning approach was used to integrate field management and genotype information with the multispectral data, providing context to the conditions that the plants experienced during the trial. Model performance was assessed using holdout data, in which the model accurately predicted the yield (RMSE 1.07 t/ha, a relative RMSE of 7.60% of 16 t/ha, and R2 score 0.73) and identified the majority of high-yielding varieties, outperforming previously published models for early yield prediction. The inclusion of vegetation indices was important for model performance, with a normalized difference vegetation index and green with normalized difference vegetation index contributing the most to model performance. The model provides a decision support tool, identifying promising lines early in the field trial.

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“…Ma et al developed a county-level corn yield prediction model based on the Bayesian Neural Network (BNN) using multiple publicly available data sources over 20 years, including satellite images, climate observations, soil property maps and historical yield records [ 37 ]. Khaki et al proposed a convolutional neural network model called YieldNet to predict corn and soybean yield based on MODIS products [ 38 ]. Yang et al tried to use one-year hyperspectral imagery to train a CNN classification model to estimate corn grain yield [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery

Nong

et al. 2022

Sensors

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Sugarcane is the main industrial crop for sugar production, and its growth status is closely related to fertilizer, water, and light input. Unmanned aerial vehicle (UAV)-based multispectral imagery is widely used for high-throughput phenotyping, since it can rapidly predict crop vigor at field scale. This study focused on the potential of drone multispectral images in predicting canopy nitrogen concentration (CNC) and irrigation levels for sugarcane. An experiment was carried out in a sugarcane field with three irrigation levels and five fertilizer levels. Multispectral images at an altitude of 40 m were acquired during the elongating stage. Partial least square (PLS), backpropagation neural network (BPNN), and extreme learning machine (ELM) were adopted to establish CNC prediction models based on various combinations of band reflectance and vegetation indices. The simple ratio pigment index (SRPI), normalized pigment chlorophyll index (NPCI), and normalized green-blue difference index (NGBDI) were selected as model inputs due to their higher grey relational degree with the CNC and lower correlation between one another. The PLS model based on the five-band reflectance and the three vegetation indices achieved the best accuracy (Rv = 0.79, RMSEv = 0.11). Support vector machine (SVM) and BPNN were then used to classify the irrigation levels based on five spectral features which had high correlations with irrigation levels. SVM reached a higher accuracy of 80.6%. The results of this study demonstrated that high resolution multispectral images could provide effective information for CNC prediction and water irrigation level recognition for sugarcane crop.

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Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning

Cited by 100 publications

References 55 publications

Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks

Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks

Maize Yield Prediction at an Early Developmental Stage Using Multispectral Images and Genotype Data for Preliminary Hybrid Selection

Sugarcane Nitrogen Concentration and Irrigation Level Prediction Based on UAV Multispectral Imagery

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