An economic feasibility assessment framework for underutilised crops using Support Vector Machine

Oh, Mei Shin; Chen, Zhiyuan; Jahanshiri, Ebrahim; Isa, Dino; Wong, Yee Wan

doi:10.1016/j.compag.2019.105116

Cited by 10 publications

(6 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…One of the major drawbacks of selection of underutilised crops in different agroecological zones through the suitability assessment and yield prediction of underutilised crops is the lack of evaluation frameworks and models. However, with the growing concern in relation to neglected and underutilized crops, dedicated land evaluation frameworks and protocols [8,47,48] and crop models/modelling approaches [38,39,49,50] which show potential have been carried out in the recent past. Other than the method used here for crop selection, different approaches/frameworks as mentioned above can be tested to select the most suitable crops.…”

Section: Discussionmentioning

confidence: 99%

A Novel Crop Shortlisting Method for Sustainable Agricultural Diversification across Italy

et al. 2022

Self Cite

View full text Add to dashboard Cite

Whilst current policies recognize the impacts that changes in climates and markets have imposed on the Italian food system, less attention is given to the development of systematic approaches to identify alternative cropping systems across Italy and the European continent. In this article, a novel evidence-based crop shortlisting method was developed to address crop diversification needs for Italy as an example for the whole of Europe. In order to shortlist possible options from a pool of 2700 crops, a crop–climate–soil matching exercise was performed across Italian territory, and crops with more than 70% suitability were chosen for further analysis. In the second phase, a multi-criteria ranking index was employed to assign ranks to chosen crops of four main types: (i) cereals and pseudocereals, (ii) legumes, (iii) starchy roots/tubers, and (iv) vegetables. To provide a comprehensive analysis, all of the abovementioned criteria were compared for both major crops that are grown in the region and potential underutilised crops (UCs). The results of evaluation of four major criteria (namely (a) calorie and nutrition demand, (b) functions and uses, (c) availability and accessibility to their genomic material, (d) possession of adaptive traits, and (e) physiological traits) revealed the potential for teff, faba bean, cowpea, green arrow arum, Jerusalem artichoke, Fig-leaved Gourd, and Watercress. We discuss the implication of utilising such systematic approaches to crop selection and developing transformative solutions for food security with the aim of providing a primer for mainstreaming UCs in policy and investment plans.

show abstract

Section: Discussionmentioning

confidence: 99%

A Novel Crop Shortlisting Method for Sustainable Agricultural Diversification across Italy

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…It has primarily been utilized as a function optimizer, and that was seen to be a useful global optimization method, particularly for multi-model and non-continuous processes. Because a large amount of literature on MLP-GA and SVM-GA has already been published in previous research [37][38][39], we cite it only in this part. Figure 1A,B shows a schematic representation of the suggested hybrid algorithms.…”

Section: Hybrid Artificial Intelligence Algorithm Based On Ga (Geneti...mentioning

confidence: 99%

Development of Novel Hybrid Models for Prediction of Drought- and Stress-Tolerance Indices in Teosinte Introgressed Maize Lines Using Artificial Intelligence Techniques

et al. 2022

View full text Add to dashboard Cite

Maize (Zea mays subsp. mays) is a staple food crop in the world. Drought is one of the most common abiotic challenges that maize faces when it comes to growth, development, and production. Further knowledge of drought tolerance could aid with maize production. However, there has been less study focused on investigating in depth the drought tolerance of inbred maize lines using artificial intelligence techniques. In this study, multi-layer perceptron (MLP), support vector machine (SVM), genetic algorithm-based multi-layer perceptron (MLP-GA), and genetic algorithm-based support vector machine (SVM-GA) hybrid artificial intelligence algorithms were used for the prediction of drought tolerance and stress tolerance indices in teosinte maize lines. Correspondingly, the gamma test technique was applied to determine efficient input and output vectors. The potential of the developed models was evaluated based on statistical indices and graphical representations. The results of the gamma test based on the least value of gamma and standard error indices show that days of anthesis (DOA), days of silking (DOS), yield index (YI), and gross yield per plant (GYP) information vector arrangements were determined to be an efficient information vector combination for the drought-tolerance index (DTI) as well as the stress-tolerance index (STI). The MLP, SVM, MLP-GA, and SVM-GA algorithms’ results were compared based on statistical indices and visual interpretations that have satisfactorily predict the drought-tolerance index and stress-tolerance index in maize crops. The genetic algorithm-based hybrid models (MLP-GA and SVM-GA) were found to better predict the drought-tolerance index and stress-tolerance index in maize crops. Similarly, the SVM-GA model was found to have the highest potential to forecast the DTI and STI in maize crops, compared to the MLP, SVM, and MLP-GA models.

show abstract

“…Mulyawan et al [8], Sreedevi and Anuradha [9] and Adnan et al [10] adopted support-vector-machines-optimized particle swarm optimization to classify the human development index, electrocardiogram signals and dissolved oxygen, respectively. Oh et al [11] employed a combination of a support vector machine and genetic algorithm to generate training data from the approximate model of general cash crops. Huo et al [12] classified the usual faults of fuel cells using an extreme learning machine and a support vector machine optimized using a genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Consumer Purchasing Power Prediction of Interest E-Commerce Based on Cost-Sensitive Support Vector Machine

Ye,

Yang,

Sun

2023

Sustainability

View full text Add to dashboard Cite

The traditional e-commerce business chain is being reconstructed around the content of short videos and live streams, and the interest e-commerce is thriving as a new trend in the e-commerce industry. Diversified content promotes the rapid development of interest e-commerce. For consumers, their preferences for different content reflect their consumption level to a certain extent. The purpose of this study is to accurately predict the purchasing power level with the consumer content preference, and provide new ideas for interest e-commerce business. In this paper, the new swarm intelligence algorithm is used to find the optimal misclassification cost, and three cost-sensitive models are established. On this basis, the content preference of interest e-commerce consumers is used to predict the level of purchasing power. The results show that the content preference of interest e-commerce consumers, such as “fashion”, “photography” and “interpretation”, have a significant effect on the prediction of purchasing power at the 95% confidence level. The accuracies of the optimized cost-sensitive support vector machine in predicting consumer purchasing power are all above 0.9, and the highest is 0.9792. This study effectively alleviates the problem that the classification results tend to be biased towards negative samples, especially when the imbalanced rate of the sample is high. It not only provides researchers with an efficient parameter optimization method, but also reflects the relationship between consumer content preference and purchasing power, providing data support for interest e-commerce operations.

show abstract

An economic feasibility assessment framework for underutilised crops using Support Vector Machine

Cited by 10 publications

References 40 publications

A Novel Crop Shortlisting Method for Sustainable Agricultural Diversification across Italy

A Novel Crop Shortlisting Method for Sustainable Agricultural Diversification across Italy

Development of Novel Hybrid Models for Prediction of Drought- and Stress-Tolerance Indices in Teosinte Introgressed Maize Lines Using Artificial Intelligence Techniques

Consumer Purchasing Power Prediction of Interest E-Commerce Based on Cost-Sensitive Support Vector Machine

Contact Info

Product

Resources

About