Application of Statistics for Quality and Safety in Agricultural Production

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Product Quality and Safety".

Deadline for manuscript submissions: 25 August 2026 | Viewed by 962

Special Issue Editor


E-Mail Website
Guest Editor
Department of Agricultural, Food and Forest Sciences (SAAF), University of Study of Palermo, Viale delle Scienze, 90128 Palermo, Italy
Interests: statistics in agriculture; forestry and food technologies; applied statistical methods

Special Issue Information

Dear Colleagues,

Statistics provides essential tools for monitoring, analyzing, and decision-making in agricultural production. They help in understanding disease prevalence, optimizing resource allocation, and ensuring food safety throughout the production chain. Statistical models are used to assess disease resistance, track disease spread and evaluate the effectiveness of pest and disease control measures.

Statistical methods can optimize resource allocation and management by predicting crop yields, minimizing water waste, and determining the optimal amount and type of fertilizer. Contamination monitoring is also crucial, as it helps with monitoring levels of contaminants in food products at various stages of the production chain. Traceability systems can integrate statistical data analysis to track the origin and movement of food products, enabling faster identification and recall of potentially unsafe products.

Statistical process control techniques can identify and eliminate defects in agricultural products, improving overall quality and reducing waste. Risk assessment helps with prioritizing resources for risk reduction and prevention.

Data collection and analysis can be carried out through surveys and censuses, data from farm records and market transactions, and new technologies such as sensors, drones, and remote sensing. Statistical analysis of environmental data can help with assessing the environmental impact of agricultural practices.

By fully exploiting statistical methods, agricultural producers can enhance the quality and safety of their products, optimize resource utilization, and promote sustainable agricultural practice, while competent authorities can better monitor ongoing processes.

Prof. Dr. Stefano Barone
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • statistics in agricultural production
  • statistical methods for resource allocation
  • statistics for agricultural food quality and safety
  • statistics for food contamination monitoring, traceability systems, and risk assessment
  • statistics for sustainability and environmental monitoring in agricultural production and operations

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

17 pages, 3642 KB  
Article
Spatiotemporal Analysis for Real-Time Non-Destructive Brix Estimation in Apples
by Ha-Na Kim, Myeong-Won Bae, Yong-Jin Cho and Dong-Hoon Lee
Agriculture 2026, 16(2), 172; https://doi.org/10.3390/agriculture16020172 - 9 Jan 2026
Viewed by 485
Abstract
Predicting internal quality parameters, such as Brix and water content, of apples, is essential for quality control. Existing near-infrared (NIR) and hyperspectral imaging (HSI)-based techniques have limited applicability due to their dependence on equipment and environmental sensitivity. In this study, a transportable quality [...] Read more.
Predicting internal quality parameters, such as Brix and water content, of apples, is essential for quality control. Existing near-infrared (NIR) and hyperspectral imaging (HSI)-based techniques have limited applicability due to their dependence on equipment and environmental sensitivity. In this study, a transportable quality assessment system was proposed using spatiotemporal domain analysis with long-wave infrared (LWIR)-based thermal diffusion phenomics, enabling non-destructive prediction of the internal Brix of apples during transport. After cooling, the thermal gradient of the apple surface during the cooling-to-equilibrium interval was extracted. This gradient was used as an input variable for multiple linear regression, Ridge, and Lasso models, and the prediction performance was assessed. Overall, 492 specimens of 5 cultivars of apple (Hongro, Arisoo, Sinano Gold, Stored Fuji, and Fuji) were included in the experiment. The thermal diffusion response of each specimen was imaged at a sampling frequency of 8.9 Hz using LWIR-based thermal imaging, and the temperature changes over time were compared. In cross-validation of the integrated model for all cultivars, the coefficient of determination (R2cv) was 0.80, and the RMSEcv was 0.86 °Brix, demonstrating stable prediction accuracy within ±1 °Brix. In terms of cultivar, Arisoo (Cultivar 2) and Fuji (Cultivar 5) showed high prediction reliability (R2cv = 0.74–0.77), while Hongro (Cultivar 1) and Stored Fuji (Cultivar 4) showed relatively weak correlations. This is thought to be due to differences in thermal diffusion characteristics between cultivars, depending on their tissue density and water content. The LWIR-based thermal diffusion analysis presented in this study is less sensitive to changes in reflectance and illuminance compared to conventional NIR and visible light spectrophotometry, as it enables real-time measurements during transport without requiring a separate light source. Surface heat distribution phenomics due to external heat sources serves as an index that proximally reflects changes in the internal Brix of apples. Later, this could be developed into a reliable commercial screening system to obtain extensive data accounting for diversity between cultivars and to elucidate the effects of interference using external environmental factors. Full article
Show Figures

Figure 1

Back to TopTop