Accuracy of NDVI-derived corn yield predictions is impacted by time of sensing

Maresma, Ángel; Chamberlain, Lindsay A.; Tagarakis, Aristotelis C.; Kharel, Tulsi P.; Godwin, Greg; Czymmek, Karl; Shields, Elson J.; Ketterings, Quirine M.

doi:10.1016/j.compag.2020.105236

Cited by 52 publications

(49 citation statements)

References 39 publications

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“…Whether at the individual sites or pooled data, stronger yield predictions were recorded from those variables collected towards the reproductive stages from 85 days and the best at 105 days after emergence. These findings resonate with those reported by Maresma et al (2020) who concluded that best yield predictions are obtained by scanning maize at or after V10 stage of growth. Fernandez-Ordoñez & Soria-Ruiz (2017) also found strong yield prediction when NDVI was recorded at flowering.…”

Section: Predicting Maize Grain Yield From Greenseeker Normalized Difsupporting

confidence: 92%

“…The GreenSeeker NDVI readings and aboveground dry biomass produced R 2 ranging from 0.23-0.53 and 0.30-0.61 (in Embu), and 0.31-0.64 and 0.30-0.50 (in Kirinyaga) respectively. The use of NDVI reading in predicting grain yields has been reported by several researchers (Sultana et al, 2014;Fernandez-Ordoñez & Soria-Ruiz, 2017;Maresma et al, 2020). The pooled GreenSeeker NDVI readings and aboveground biomass data recorded significant positive prediction of grain yield ( Figure 6).…”

Section: Predicting Maize Grain Yield From Greenseeker Normalized Difsupporting

confidence: 59%

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Prediction of Maize Yields from In-Season GreenSeeker Normalized Difference Vegetation Index and Dry Biomass as Influenced by Different Nutrient Combinations

Otieno¹,

Chemining’wa²,

Zingore³

2020

JAS

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To mitigate low maize productivity, improve on-farm planning and policy implementation, the right fertilizer combinations and yield forecasting should be prioritized. Therefore, this research aimed at assessing the effect of applying different nutrient combinations on maize growth and yield and in-season grain yield prediction from biomass and normalized difference vegetation index (NDVI) readings. The research was done in Embu and Kirinyaga counties, in Central Kenya. Nutrient combinations tested were P+K, N+K, N+P, N+P+K, and N+P+K+Ca+Mg+Zn+B+S. The results showed consistently lowest and highest NDVI reading, dry biomass, and grain yields due to P+K and N+P+K+Ca+Mg+Zn+B+S treatments, respectively. Positive NDVI responses of 56%, 14%, 15%, and 15% were recorded with N, P, K, and combined Ca+Mg+Zn+B+S, respectively. These nutrients, in the same order, recorded 54%, 20%, 8%, and 18% positive responses with biomass. The GreenSeeker NDVI reading with grain yield and aboveground dry biomass with grain yield recorded R2 ranging from 0.23-0.53 and 0.30-0.61 (in Embu), and 0.31-0.64 and 0.30-0.50 (in Kirinyaga), respectively. When data were pooled, the prediction strength increased, reaching a maximum of 67% and 58% with NDVI and biomass, respectively. Yield prediction was even more robust when the independent variables were combined through multiple linear model at both 85 and 105 days after emergence. From this research, it is evident that the effects of balanced fertilizer application are detectable from NDVI readings—providing a tool for tracking and monitoring nutrient management effects—not just from the nitrogen perspective as commonly studied but from the combined effects of multiple nutrients. Also, grain yield could be accurately predicted early before harvesting by combining NDVI and biomass yields.

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Section: Predicting Maize Grain Yield From Greenseeker Normalized Difsupporting

confidence: 92%

Section: Predicting Maize Grain Yield From Greenseeker Normalized Difsupporting

confidence: 59%

Prediction of Maize Yields from In-Season GreenSeeker Normalized Difference Vegetation Index and Dry Biomass as Influenced by Different Nutrient Combinations

Otieno¹,

Chemining’wa²,

Zingore³

2020

JAS

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“…The leaves of a healthy plant absorb more red light and reflect more near-infrared light, resulting in higher NDVI values than plants under stress [ 58 ]. NDVI has been used to estimate leaf chlorophyll contents [ 58 ], photosynthetic activity [ 59 , 60 ], plant biomass [ 61 , 62 ], yield [ 63 , 64 ], and responses to stresses, such as salt [ 65 ] and drought [ 21 ]. This study demonstrated a potential alternative for quantifying plant damage due to flood stress in field conditions.…”

Section: Resultsmentioning

confidence: 99%

Qualification of Soybean Responses to Flooding Stress Using UAV-Based Imagery and Deep Learning

et al. 2021

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Soybean is sensitive to flooding stress that may result in poor seed quality and significant yield reduction. Soybean production under flooding could be sustained by developing flood-tolerant cultivars through breeding programs. Conventionally, soybean tolerance to flooding in field conditions is evaluated by visually rating the shoot injury/damage due to flooding stress, which is labor-intensive and subjective to human error. Recent developments of field high-throughput phenotyping technology have shown great potential in measuring crop traits and detecting crop responses to abiotic and biotic stresses. The goal of this study was to investigate the potential in estimating flood-induced soybean injuries using UAV-based image features collected at different flight heights. The flooding injury score (FIS) of 724 soybean breeding plots was taken visually by breeders when soybean showed obvious injury symptoms. Aerial images were taken on the same day using a five-band multispectral and an infrared (IR) thermal camera at 20, 50, and 80 m above ground. Five image features, i.e., canopy temperature, normalized difference vegetation index, canopy area, width, and length, were extracted from the images at three flight heights. A deep learning model was used to classify the soybean breeding plots to five FIS ratings based on the extracted image features. Results show that the image features were significantly different at three flight heights. The best classification performance was obtained by the model developed using image features at 20 m with 0.9 for the five-level FIS. The results indicate that the proposed method is very promising in estimating FIS for soybean breeding.

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“…These systems provide high spatial resolution images and, in combination with their ease of use, quick acquisition times, and low operational cost, they have become particularly popular for monitoring agricultural fields [ 3 ]. Several studies have utilized UASs for crop management purposes, such as yield prediction and site-specific fertilization [ 4 ] by capturing multispectral images, irrigation using thermal imaging [ 5 ], or for field scouting using RGB (Red-Green-Blue) orthomosaics [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Orchard Mapping with Deep Learning Semantic Segmentation

Anagnostis

Tagarakis

Kateris

et al. 2021

Sensors

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This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e., different seasons, different tree ages, different levels of weed coverage). The implemented dataset was composed of images from three different walnut orchards. The achieved variability of the dataset resulted in obtaining images that fell under seven different use cases. The best-trained model achieved 91%, 90%, and 87% accuracy for training, validation, and testing, respectively. The trained model was also tested on never-before-seen orthomosaic images or orchards based on two methods (oversampling and undersampling) in order to tackle issues with out-of-the-field boundary transparent pixels from the image. Even though the training dataset did not contain orthomosaic images, it achieved performance levels that reached up to 99%, demonstrating the robustness of the proposed approach.

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Accuracy of NDVI-derived corn yield predictions is impacted by time of sensing

Cited by 52 publications

References 39 publications

Prediction of Maize Yields from In-Season GreenSeeker Normalized Difference Vegetation Index and Dry Biomass as Influenced by Different Nutrient Combinations

Prediction of Maize Yields from In-Season GreenSeeker Normalized Difference Vegetation Index and Dry Biomass as Influenced by Different Nutrient Combinations

Qualification of Soybean Responses to Flooding Stress Using UAV-Based Imagery and Deep Learning

Orchard Mapping with Deep Learning Semantic Segmentation

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