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Applied Sciences
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Sustainable and Smart Agriculture
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Sustainable and Smart Agriculture

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 1139

Special Issue Editors

Dr. Wei Ma

E-Mail Website
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

E-Mail Website
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

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Applied Sciences 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 2400 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

  • agricultural robots
  • agricultural technology
  • smart agriculture

Published Papers (2 papers)

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Research

14 pages, 1729 KiB  
Article
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
Viewed by 200
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 [...] Read more.
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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15 pages, 6463 KiB  
Article
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 1 | Viewed by 674
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, [...] Read more.
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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