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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 14737

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Special Issue Editors

Dr. Wei Ma

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Guest Editor

Institute of Urban Agriculture, Chinese Academy of Agriculture Sciences, Chengdu 610213, China
Interests: agricultural robots; intelligent gardening robots
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Xiu Wang

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Guest Editor

1. Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
2. National Engineering Research Center of Intelligent Equipment for Agriculture (NERCIEA), Beijing 100097, China
Interests: intelligent agricultural equipment

Special Issue Information

Dear Colleagues,

This Special Issue focuses on theoretical and technological innovation in sustainable and smart agriculture in various research fields. However, considering the application of green and low-carbon technologies in various aspects of modern agriculture, papers that discuss relevant carbon footprint methods are also welcome. The loss of agricultural products in the harvesting process has always been a problem in agricultural production. We invite authors to submit papers that propose various innovative solutions, such as online sensing, intelligent decision making, and variable execution. The quality and safety of agricultural products, as well as green processing, are key to improving the added value of agricultural products.

Based on the shortcomings of existing technology and innovative methods, we welcome submissions that demonstrate methods of making food processing more environmentally friendly and energy-saving. The populations of big cities, and thus, food demand will continue to increase in the future, and three-dimensional agricultural cultivation can help produce more high-quality food on limited land. Vertical agriculture and plant factories represent key technological innovations, and papers demonstrating related methods are welcome. The human exploration of Mars requires advanced agricultural technology, agricultural facilities, photobiology, and the exploration of interstellar agricultural methods, which may allow vegetables and other foods to grow in underground or mobile spaces. For this Special Issue, we value contributions that demonstrate innovative thinking.

Dr. Wei Ma
Prof. Dr. Xiu Wang
Guest Editors

Manuscript Submission Information

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Keywords

agricultural robots
agricultural technology
smart agriculture

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Published Papers (7 papers)

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Research

17 pages, 1490 KB

Open AccessArticle

3D Reconstruction and Discrete Element Modeling of Wheat Kernels for Numerical Simulation of Grain-Storage Behavior

by Ziqing Zhang, Qirui Wang, Chao Zhao, Kaixu Bai, Qikeng Xu, Peifang Xin, Chunqi Bai and Hao Zhang

Appl. Sci. 2026, 16(6), 2915; https://doi.org/10.3390/app16062915 - 18 Mar 2026

Viewed by 264

Abstract

The physical structure formed during the packing of granular grain constitutes a fundamental ecological factor within the grain bulk ecosystem. Accurate simulations of grain-packing behavior help to deepen our understanding of this ecosystem. In this study, a white hard wheat was selected as the test material, and a high-fidelity multi-sphere discrete element model of wheat kernels was constructed using three-dimensional laser scanning. Physical experiments were conducted to determine the basic physical properties of the kernels, including true density and bulk density. Using the angle of repose as the calibration parameter, the wheat-packing process was investigated with the discrete element method (DEM). The results indicated that the coefficients of static and rolling friction between particles were highly significant factors governing the angle of repose. The optimal parameter combination consisted of a particle–particle coefficient of restitution of 0.500, a coefficient of static friction of 0.388, and a coefficient of rolling friction of 0.054. The mean angle of repose obtained from the DEM packing simulation was 28.46°, corresponding to a relative error of 3.16% compared with the physical experiment. This calibrated parameter set is therefore considered accurate and reliable, and it provides baseline data for DEM simulations of wheat grain bulks. Full article

(This article belongs to the Special Issue Sustainable and Smart Agriculture)

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28 pages, 15959 KB

Open AccessArticle

A Proof of Concept for an Agrifood Data Space Based on Open Data and Interoperability

by Cristina Martinez-Ruedas, Adela Pérez-Galvín and Rafael Linares-Burgos

Appl. Sci. 2026, 16(4), 1831; https://doi.org/10.3390/app16041831 - 12 Feb 2026

Viewed by 402

Abstract

The creation of unified, open, secure, reliable, and agile data spaces is essential for collecting, storing, and sharing data in a standardized and accessible manner, promoting data reuse and addressing current interoperability limitations. In this context, this research presents a proof of concept for a unified agronomic data space based on the structured integration of heterogeneous open data sources. The central hypothesis is that the automated acquisition, preprocessing, and harmonization of publicly available agronomic data can significantly improve accessibility, usability, and interoperability for agricultural decision support applications. To this end, a comprehensive analysis of relevant open data sources was conducted, followed by the design and implementation of configurable algorithms for automated data downloading, cleaning, validation, and integration. The proposed approach explicitly addresses key challenges such as heterogeneous data formats, inconsistent spatial and temporal resolutions, missing values, and outlier detection. As a result, a unified access point was developed, providing reliable agronomic information, including (i) preprocessed climatological time series, (ii) crop and phytosanitary data, (iii) high-resolution aerial orthophotography, (iv) remote-sensing imagery, (v) pest-related information, and (vi) time series of major vegetation indices. The proof of concept was implemented for olive groves in the Andalusian region of Spain; however, the methodology is fully transferable to other crops, regions, and institutional contexts where comparable open data sources are available. The results demonstrate the potential of shared agronomic data spaces to enhance data reuse, support scalable analytics, and facilitate interoperable, data-driven agricultural management beyond the specific regional case study. Full article

(This article belongs to the Special Issue Sustainable and Smart Agriculture)

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17 pages, 7082 KB

Open AccessArticle

Image Classification of Raw Beef Cuts Based on the Improvement of YOLOv11n Using Wavelet Convolution

by Hongsen Liao, Yongsong Hu, Mei Zhang and Wei Ma

Appl. Sci. 2026, 16(1), 332; https://doi.org/10.3390/app16010332 - 29 Dec 2025

Viewed by 557

Abstract

In recent years, with changes in dietary structure, beef has become the third most consumed meat in China after pork and chicken, with its consumption increasing by approximately 50%. The quality and commercial value of beef vary considerably across different muscles. However, due to the high similarity in the appearance of beef cuts and strong background interference, traditional image features are often insufficient for accurate classification. In this study, an improved convolutional neural network based on YOLOv11 was proposed. Four beef muscles were categorized: sirloin (longissimus dorsi), fillet/tenderloin (psoas major), oyster blade (infraspinatus), and ribeye (longissimus thoracis). A dataset comprising 3598 images was established to support model training and validation. We divided the dataset into training, testing, and validation sets in a 6:2:2 ratio. To enhance model performance, wavelet convolution (WTConv) was employed to effectively expand the receptive field and improve image understanding, while a large separable kernel attention (LSKA) module was introduced to strengthen local feature representation and reduce background interference. Comparative experiments were conducted with other deep learning models as well as ablation tests to validate the proposed model’s effectiveness. Experimental results demonstrated that the proposed model achieved a classification accuracy of 98.50%, with Macro-Precision and Macro-Recall reaching 97.38% and 97.38%, respectively, and a detection speed of 147.66 FPS. These findings confirm the potential of the YOLOv11n-cls model for accurate beef classification and its practical application in intelligent meat recognition and processing within the Chinese beef industry. Full article

(This article belongs to the Special Issue Sustainable and Smart Agriculture)

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17 pages, 10541 KB

Open AccessArticle

Design and Test of Seedling-Picking Mechanism of Fully Automatic Transplanting Machine

by Biao Zhou, Hong Miao, Chunsong Guan, Xin Ji and Xiaochan Wang

Appl. Sci. 2024, 14(20), 9235; https://doi.org/10.3390/app14209235 - 11 Oct 2024

Cited by 7 | Viewed by 3998

Abstract

The seedling retrieval mechanism is a crucial component of fully automatic transplanting machines, significantly influencing the quality, reliability, and efficiency of the transplanting process. Nonetheless, the existing seedling retrieval mechanisms in current transplanting machines exhibit several deficiencies, including substantial damage to seedlings and inadequate retrieval accuracy. To overcome these challenges, we propose an integrated approach combining pneumatic and mechanical techniques to further improve performance. By employing a lower thimble elevation and clamping mechanism, alongside a mathematical model based on the seedling removal process, this method ensures precise seedling extraction and minimizes damage to the root system and substrate. The novelty of this study lies in its ability to reduce the adhesion between seedlings and the holes of the plug plate, thereby minimizing non-destructive extraction of the seedlings and preserving the integrity of the matrix, which is essential for ensuring healthy seedling growth. Moreover, the optimization of the seedling retrieval trajectory enhances the accuracy of the seedling retrieval mechanism while also meeting the requisite speed requirements. Experimental results indicate that at a rate of 72 seedlings per minute, the extraction success rate reached 94.90%, and the casting success rate was 98.53%. The seedling injury rate was only 1.95%, resulting in an overall success rate of 91.69%. These findings confirm that the device meets operational efficiency requirements and delivers effective performance. Full article

(This article belongs to the Special Issue Sustainable and Smart Agriculture)

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14 pages, 2825 KB

Open AccessArticle

Method of Planning and Scheduling the Production Process of Yellow Mealworm Larvae for a Small Enterprise

by Arkadiusz Kowalski

Appl. Sci. 2024, 14(16), 7051; https://doi.org/10.3390/app14167051 - 12 Aug 2024

Cited by 3 | Viewed by 3812

Abstract

In the context of the growing demand for alternative protein sources with the growth of the human population and increasing ecological awareness, the rearing of yellow mealworm larvae (Tenebrio molitor) is a promising option for the production of sustainable protein. The article presents a comprehensive approach to planning and scheduling the production of yellow mealworm larvae in a small enterprise, focusing on the organizational, technical, and economic aspects of the production process. The production installation, the method of rearing using an automated feeding system, and the monitoring of larvae development were described and an attempt was made to identify the key parameters of the process that affect its efficiency. Particular attention was paid to the calculation algorithm implemented in the spreadsheet, which allows the selection of the production batch size and the frequency of their launch, so as to maximize the available capacity of storage racks for cuvettes. In addition, the article analyses logistical challenges related to the production of larvae, including transport activities in order to meet, among others, the demand for feed. Finally, the estimation of revenues and economic indicators, such as profitability and return on investment, is presented, pointing to the need for further improvements in the production process and cost optimization to achieve favorable financial results. The results of the research emphasize the potential of rearing yellow mealworm larvae as a sustainable source of protein while simultaneously pointing to key areas that require further research and development. Full article

(This article belongs to the Special Issue Sustainable and Smart Agriculture)

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14 pages, 1729 KB

Open AccessArticle

Improving Tomato Fruit Volatiles through Organic Instead of Inorganic Nutrient Solution by Precision Fertilization

by Youli Li, Xiaobei Han, Si Li, Rongchao Shi, Jiu Xu, Qian Zhao, Tianxiang Liu and Wenzhong Guo

Appl. Sci. 2024, 14(11), 4584; https://doi.org/10.3390/app14114584 - 27 May 2024

Cited by 2 | Viewed by 1804

Abstract

This study investigated the effects of irrigation with a fully inorganic nutrient solution (control; NNNN) and an organic instead of an inorganic nutrient solution (OIINS) at the flowering–fruit setting (ONNN), fruit expanding (NONN), color turning (NNON), and harvest (NNNO) stages of the first spike on the type and content of tomato fruit volatiles to provide a theoretical basis for tomato aroma improvement and high-quality cultivation. Compared with the control (NNNN), the results showed that all OIINS-related treatments decreased the number of fruit volatiles and increased the relative content of common volatile compounds, characteristic effect compounds, aldehydes, and cis-3-hexenal. In particular, the relative order of performance of the OIINS-related treatments was NNNO > NNON > ONNN > NONN in terms of the relative content of characteristic compounds. For all treatments, the relative cis-3-hexenal and trans-2-hexenal percentages were 20.99–51.49% and 20.22–27.81%, respectively. Moreover, hexanal was only detected in tomato fruits under the NNNN and NNNO treatments. The effects of irrigation with OIINS on tomato fruit volatiles were related to the fruit developmental stage. At the mature stage, the organic nutrient solution was conducive to the accumulation of characteristic compounds and improved the fruit aroma quality. Full article

(This article belongs to the Special Issue Sustainable and Smart Agriculture)

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14 pages, 6463 KB

Open AccessArticle

Approach of Dynamic Tracking and Counting for Obscured Citrus in Smart Orchard Based on Machine Vision

by Yuliang Feng, Wei Ma, Yu Tan, Hao Yan, Jianping Qian, Zhiwei Tian and Ang Gao

Appl. Sci. 2024, 14(3), 1136; https://doi.org/10.3390/app14031136 - 29 Jan 2024

Cited by 16 | Viewed by 2438

Abstract

The approach of dynamic tracking and counting for obscured citrus based on machine vision is a key element to realizing orchard yield measurement and smart orchard production management. In this study, focusing on citrus images and dynamic videos in a modern planting mode, we proposed the citrus detection and dynamic counting method based on the lightweight target detection network YOLOv7-tiny, Kalman filter tracking, and the Hungarian algorithm. The YOLOv7-tiny model was used to detect the citrus in the video, and the Kalman filter algorithm was used for the predictive tracking of the detected fruits. In order to realize optimal matching, the Hungarian algorithm was improved in terms of Euclidean distance and overlap matching and the two stages life filter was added; finally, the drawing lines counting strategy was proposed. ln this study, the detection performance, tracking performance, and counting effect of the algorithms are tested respectively; the results showed that the average detection accuracy of the YOLOv7-tiny model reached 97.23%, the detection accuracy in orchard dynamic detection reached 95.12%, the multi-target tracking accuracy and the precision of the improved dynamic counting algorithm reached 67.14% and 74.65% respectively, which were higher than those of the pre-improvement algorithm, and the average counting accuracy of the improved algorithm reached 81.02%. The method was proposed to effectively help fruit farmers grasp the number of citruses and provide a technical reference for the study of yield measurement in modernized citrus orchards and a scientific decision-making basis for the intelligent management of orchards. Full article

(This article belongs to the Special Issue Sustainable and Smart Agriculture)

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