Sunflower crop yield prediction by advanced statistical modeling using satellite-derived vegetation indices and crop phenology

Amankulova, Khilola; Farmonov, Nizom; Mukhtorov, Uzbekkhon; Mucsi, László

doi:10.1080/10106049.2023.2197509

Cited by 12 publications

(3 citation statements)

References 53 publications

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“…In their study, Morales and Vilalobos 25 , also found that RFR algorithm had a better performance than artificial neural networks and regularized linear models in yield prediction in sunflower and wheat and was also easier to execute. Amankulova et al 17 also found RFR to be very effective for sunflower crop yield prediction using satellite-derived vegetation indices and crop phenology.…”

Section: Resultsmentioning

confidence: 99%

Oil yield prediction for sunflower hybrid selection using different machine learning algorithms

Cvejić,

Hrnjaković,

Jocković

et al. 2023

Sci Rep

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Due to the increased demand for sunflower production, its breeding assignment is the intensification of the development of highly productive oil seed hybrids to satisfy the edible oil industry. Sunflower Oil Yield Prediction (SOYP) can help breeders to identify desirable new hybrids with high oil yield and their characteristics using machine learning (ML) algorithms. In this study, we developed ML models to predict oil yield using two sets of features. Moreover, we evaluated the most relevant features for accurate SOYP. ML algorithms that were used and compared were Artificial Neural Network (ANN), Support Vector Regression, K-Nearest Neighbour, and Random Forest Regressor (RFR). The dataset consisted of samples for 1250 hybrids of which 70% were randomly selected and were used to train the model and 30% were used to test the model and assess its performance. Employing MAE, MSE, RMSE and R2 evaluation metrics, RFR consistently outperformed in all datasets, achieving a peak of 0.92 for R2 in 2019. In contrast, ANN recorded the lowest MAE, reaching 65 in 2018 The paper revealed that in addition to seed yield, the following characteristics of hybrids were important for SOYP: resistance to broomrape (Or) and downy mildew (Pl) and maturity. It was also disclosed that the locality feature could be used for the estimation of sunflower oil yield but it is highly dependable on weather conditions that affect the oil content and seed yield. Up to our knowledge, this is the first study in which ML was used for sunflower oil yield prediction. The obtained results indicate that ML has great potential for application in oil yield prediction, but also selection of parental lines for hybrid production, RFR algorithm was found to be the most effective and along with locality feature is going to be further evaluated as an alternative method for genotypic selection.

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Section: Resultsmentioning

confidence: 99%

Oil yield prediction for sunflower hybrid selection using different machine learning algorithms

Cvejić,

Hrnjaković,

Jocković

et al. 2023

Sci Rep

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“…Furthermore, SVMs and RFs are used in comparison with multiple linear regression. Results show that the highest correlation was observed with vegetation indices obtained during the inflorescence emergence stage, and the RF model achieved better accuracy [78,195]. An interesting application of ML models is sunflower oil yield prediction, which allows breeders to select the most productive varieties.…”

Section: Overview Of Digital Sunflower Phenotypingmentioning

confidence: 97%

“…ML was used to predict water consumption and rationalize its use [75]. The analysis of vegetation indices, essential to crop management, is another example of ML application in precision agriculture [76][77][78]. A recent study investigated the possibility of estimating the NDVI (Normalized Difference Vegetation Index) through an artificial intelligence approach from RGB images, a revolution for small-sized farms due to the low-cost cameras used [79].…”

Section: Advantages Disadvantagesmentioning

confidence: 99%

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Centorame,

Gasperini,

Ilari

et al. 2024

Agronomy

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Machine learning is a widespread technology that plays a crucial role in digitalisation and aims to explore rules and patterns in large datasets to autonomously solve non-linear problems, taking advantage of multiple source data. Due to its versatility, machine learning can be applied to agriculture. Better crop management, plant health assessment, and early disease detection are some of the main challenges facing the agricultural sector. Plant phenotyping can play a key role in addressing these challenges, especially when combined with machine learning techniques. Therefore, this study reviews available scientific literature on the applications of machine learning algorithms in plant phenotyping with a specific focus on sunflowers. The most common algorithms in the agricultural field are described to emphasise possible uses. Subsequently, the overview highlights machine learning application on phenotyping in three primaries areas: crop management (i.e., yield prediction, biomass estimation, and growth stage monitoring), plant health (i.e., nutritional status and water stress), and disease detection. Finally, we focus on the adoption of machine learning techniques in sunflower phenotyping. The role of machine learning in plant phenotyping has been thoroughly investigated. Artificial neural networks and stacked models seems to be the best way to analyse data.

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Integrating the Sentinel-1, Sentinel-2 and topographic data into soybean yield modelling using machine learning

Amankulova,

Farmonov,

Omonov

et al. 2024

Advances in Space Research

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Sunflower crop yield prediction by advanced statistical modeling using satellite-derived vegetation indices and crop phenology

Cited by 12 publications

References 53 publications

Oil yield prediction for sunflower hybrid selection using different machine learning algorithms

Oil yield prediction for sunflower hybrid selection using different machine learning algorithms

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Integrating the Sentinel-1, Sentinel-2 and topographic data into soybean yield modelling using machine learning

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