Decision Support Systems for Horticulture

Shabir, Mir; Padma, T.

doi:10.1079/cabicompendium.24010452

Cited by 2 publications

(6 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Annual financial results are maximized by making optimal choices from alternatives for structure types, covering material, shade screening, whitewash, thermal screens, heating and cooling systems, and CO 2 enrichment. Further research on this topic can be found in the efficient land use planning section presented by Mir and Padma [7]. As shown in our exploratory study, strategic production design requires the consideration of a vast number of additional factors in practice.…”

Section: Pertinent Literature and Gapsmentioning

confidence: 86%

“…Based on the results of our literature research, so far only Mir and Padma [7] presents an overview of different decision support systems in a horticultural context. They review multiple plant-specific contributions for general purposes in horticulture and applications for integrated plant protection, nutrient management, and land use.…”

Section: Literature Overview and Research Questionsmentioning

confidence: 99%

“…They review multiple plant-specific contributions for general purposes in horticulture and applications for integrated plant protection, nutrient management, and land use. Therewith, Mir and Padma [7] provide a first overview of relevant decision support systems for horticulture, but their findings are limited to the aforementioned topics which exclude the areas of distribution and sales planning as well as some procurement planning problems. These are, however, relevant to investigate when aiming to provide a complete overview of supply chain planning problems and contributions.…”

Section: Literature Overview and Research Questionsmentioning

confidence: 99%

“…Existing contributions on supply chain management barely touch the prevalent planning problems or analyze selected planning problems in very specific contexts (see Section 2). In consequence, there is no general and holistic overview of planning problems in the horticultural business [7]. Such an overview could however help to systematize and understand the interaction of the multiple decisions.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Decision Support in Horticultural Supply Chains: A Planning Problem Framework for Small and Medium-Sized Enterprises

Drechsler

Holzapfel

2022

Agriculture

View full text Add to dashboard Cite

This paper investigates and systematizes planning problems along the supply chain of small and medium-sized companies in the horticultural market of ornamental plants, perennials, and cut flowers. The sector faces considerable challenges such as multiple planning uncertainties, product perishability, and considerable lead times. However, decisions in practice are often based on rules of thumb. Data-driven decision support is thus necessary to professionalize supply chain, logistics, and operations planning in the sector. We explore the practical planning problems with the help of expert interviews with people in charge of typical companies active in the market. We structure the planning problems along the supply chain according to their time horizon and highlight the critical elements of the planning tasks and horticultural specifics. We examine the status quo of research on decision support for these planning tasks with the help of a structured literature review, highlight research gaps, and outline promising future research directions. We find that the tactical planning domains of material/product requirement, production, and demand planning are especially critical in practice, and that there is a great need for research to develop practically relevant decision support systems. Such systems are currently available only to a limited extent in literature and are not fully compatible with requirements in the ornamental horticultural sector. By structuring and detailing the relevant decision problems, we contribute to an understanding of planning problems and decision-making in horticultural supply chains, and we provide a first comprehensive overview of planning problems, aligned literature, and research gaps for the horticultural business.

show abstract

Section: Pertinent Literature and Gapsmentioning

confidence: 86%

Section: Literature Overview and Research Questionsmentioning

confidence: 99%

Section: Literature Overview and Research Questionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Decision Support in Horticultural Supply Chains: A Planning Problem Framework for Small and Medium-Sized Enterprises

Drechsler

Holzapfel

2022

Agriculture

View full text Add to dashboard Cite

show abstract

“…It accounts for over 67.7% of the country’s total apple production 1 . The area under fruit cultivation in the study area is 0.333 M ha (2020 to 2021), where the highest average apple production was recorded in the Baramulla district (439 thousand MT) during 2016 to 2020 2 . The horticultural sector in the Kashmir Valley contributes over 27% to the region’s economy 3 .…”

Section: Introductionmentioning

confidence: 97%

Apple yield prediction mapping using machine learning techniques through the Google Earth Engine cloud in Kashmir Valley, India

Singha

Gulzar

Swain

et al. 2023

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

.Our study established a machine learning (ML) model that could predict the apple yield based on various satellite multisensor data, such as climatological, SAR backscatter, terrain distribution, and soil factors, grouped as 26 subcriteria. A total of 986 apple orchards database were collected from 2018 to 2021 in Kashmir Valley, India covering an area of 277953.7 ha farmland. The novelty of our research is the integration of Google Earth Engine cloud and ML models, namely random forest, support vector machine, extreme gradient boosting, K-nearest neighbors, and Cubist along with the geographic information system/remote sensing technology to create an accurate and comprehensive apple yield prediction model in the precision agriculture realm for highlands. The multicollinearity testing indicated that the tolerance and VIF values of all the conditioning factors were <0.1 and <6.85, respectively, indicating no multicollinearity problems among the apple yield suitability factors. Among the tested ML models, the Cubist model performed best, with R2 of 0.83, root-mean-squared error of 0.56 t / ha, and mean absolute error of 0.2 t / ha. The results showed a low mean fruit yield during 2018 of 12.36 ton / ha, whereas maximum fruit yield was reflected in 2021 of 14.05 ton / ha. The heat map revealed the highest normalized differential vegetation index along with vertical-vertical/ vertical-horizontal polarization backscatter, detected during the pre-event of severe snowfall compared to on- and postevent of snowfall for the respective years. Untimely snowing and infestation due to fungi and bacterial diseases regularly reduce fruit yield in the study area. Our study successfully used of high-resolution optical-SAR data combined with ML models as a promising tool for monitoring the yield variability over the highland areas.

show abstract

Decision Support Systems for Horticulture

Abstract: This datasheet on Decision Support Systems for Horticulture covers Identity, Overview, Description, Further Information.

Cited by 2 publications

References 51 publications

Decision Support in Horticultural Supply Chains: A Planning Problem Framework for Small and Medium-Sized Enterprises

Decision Support in Horticultural Supply Chains: A Planning Problem Framework for Small and Medium-Sized Enterprises

Apple yield prediction mapping using machine learning techniques through the Google Earth Engine cloud in Kashmir Valley, India

Contact Info

Product

Resources

About