Prediction of Nitrogen Dosage in ‘Alicante Bouschet’ Vineyards with Machine Learning Models

Brunetto, Gustavo; Silva, Lincon Oliveira Stefanello da; Kulmann, Matheus Severo de Souza; Tassinari, Adriele; Souza, Rodrigo Otávio Schneider de; Rozane, Danilo Eduardo; Tiecher, Tadeu Luís; Ceretta, Carlos Alberto; Ferreira, Paulo Ademar Avelar; Siqueira, Gustavo Nogara de; Parent, Léon E.

doi:10.3390/plants11182419

Plants

2022

DOI: 10.3390/plants11182419

|View full text |Cite

Prediction of Nitrogen Dosage in ‘Alicante Bouschet’ Vineyards with Machine Learning Models

Gustavo Brunetto

Lincon Oliveira Stefanello da Silva

Matheus Severo de Souza Kulmann

et al.

Abstract: Vineyard soils normally do not provide the amount of nitrogen (N) necessary for red wine production. Traditionally, the N concentration in leaves guides the N fertilization of vineyards to reach high grape yields and chemical composition under the ceteris paribus assumption. Moreover, the carryover effects of nutrients and carbohydrates stored by perennials such as grapevines are neglected. Where a well-documented database is assembled, machine learning (ML) methods can account for key site-specific features a… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2022

2024

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 5 publications

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Boosting of fruit choices using machine learning-based pomological recommendation system

Dutta,

Gupta,

Juneja

et al. 2023

SN Appl. Sci.

View full text Add to dashboard Cite

Pomology, also known as fruticulture, is a significant contributor to the economies of many nations worldwide. While vertical farming methods are not well-suited for fruit cultivation, substrate-based cultivation is commonly practiced. Vertical farming methods use no soil for cultivation of the plants, and the cultivation is done in vertically stacked layers. Therefore, smaller herbs are best suited for such cultivation, whereas, the majority of the fruit trees are big and woody. Therefore, vertical farming methods are not well suited for fruit trees. However, to maximize fruit production, smarter substrate cultivation methods are needed. Utilizing remote sensing techniques, such as Internet of Things (IoT) devices, agriculture sensors, and cloud computing, allows for precision agriculture and smart farming in autonomous systems. Nevertheless, a lack of understanding of fruit nutrient requirements, growing conditions, and soil health conditions can result in reduced fruit production. To address these challenges, this paper proposes an intelligent model based on machine learning that recommends the best fruit to grow based on prevailing soil and climatic conditions. The system is trained on a dataset that includes details on eleven different fruits, such as Nitrogen (N), Phosphorous (P), Potassium (K), temperature, humidity, pH, and rainfall. The model takes into account the soil type and nutrient contents to recommend the most suitable fruit to grow in the prevailing climate. To enhance the model's efficiency, two novel techniques, Gradient-based Side Sampling (GOSS) and Exclusive Feature Bundling (EFB), have been incorporated. The results show that the proposed system has achieved 99% accuracy in recommending the right fruit based on the given environmental conditions. As a result, this system has the potential to significantly improve the profitability of the pomology industry and boost national economies.

show abstract

Boosting of fruit choices using machine learning-based pomological recommendation system

Dutta,

Gupta,

Juneja

et al. 2023

SN Appl. Sci.

View full text Add to dashboard Cite

show abstract

Design of precise fertilization method for greenhouse vegetables based on improved backpropagation neural network

Tang,

Sun,

Aridas

et al. 2024

Front. Sustain. Food Syst.

View full text Add to dashboard Cite

The traditional method of detecting crop nutrients is based on the direct chemical detection method in the laboratory, which causes great damage to crops. In order to solve the above problems, the main goal of this study is to design a precise fertilization method for greenhouse vegetables based on the improved back-propagation neural network (IM-BPNN) algorithm to increase fertilizer utilization efficiency, reduce production costs, and improve the economic viability of agriculture. First, soil samples from the farm in china are selected. With the laboratory treatment, available phosphorus, available potassium, and alkaline nitrogen are extracted. These data are preprocessed by the z-score (zero-mean normalization) standardization method. Then, the BPNN (backpropagation neural network) algorithm is improved by being trained and combined with the characteristics of the dual particle swarm optimization algorithm. After that, the soil sample data are divided into training and test sets, and the model is established by setting parameters, weights, and network hierarchy. Finally, the NBTY (nutrient balance target yield),BPNN (backpropagation neural network) and IM-BPNN algorithm are used to calculate the amount of fertilizer. Compared with the BPNN and NBTY algorithm, it shows that the IM-BPNN algorithm can more accurately determine the amount of fertilizer required by vegetables and avoid over-application, which can improve fertilizer utilization efficiency, reduce production costs, and improve the economic feasibility of agriculture.

show abstract

Compositional Nutrient Diagnosis Methodology and Its Effectiveness to Identify Nutrient Levels in Yerba Mate (Ilex paraguariensis)

Lisboa,

Abichequer,

de São José

et al. 2024

Agriculture

View full text Add to dashboard Cite

Yerba mate is a forest species of both cultural and economic importance growing in the subtropical regions of South America, especially in the south of Brazil. Despite its importance, yerba mate has never received enough attention from researchers, so the nutritional sufficiency ranges and critical levels have not yet been determined. This research aimed to establish these parameters for yerba mate to enable its foliar diagnosis. A total of 167 leaf samples were collected from production fields located in the five yerba mate-growing regions in Rio Grande do Sul, and the leaf nutrients were determined by standard chemical methods. The yield of each production field was accessed, and the cutoff value separating low- and high-yield groups was calculated in 16.75 Mg ha−1. The multivariate compositional nutrient diagnosis (CND) standards were determined, and nutrient interactions were estimated by correlation and principal component analyses. There was no positive correlation between any single nutrient and yield, even in the high-yield population, evidencing that a higher yield is the outcome of the balance among all nutrients. Excess of B occurred in one-third of the low-yield samples, while deficiency of Cu and K occurred in one-fourth of these samples. Finally, we established the adequate leaf nutrient levels for yerba mate.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Prediction of Nitrogen Dosage in ‘Alicante Bouschet’ Vineyards with Machine Learning Models

Cited by 5 publications

References 68 publications

Boosting of fruit choices using machine learning-based pomological recommendation system

Boosting of fruit choices using machine learning-based pomological recommendation system

Design of precise fertilization method for greenhouse vegetables based on improved backpropagation neural network

Compositional Nutrient Diagnosis Methodology and Its Effectiveness to Identify Nutrient Levels in Yerba Mate (Ilex paraguariensis)

Contact Info

Product

Resources

About