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Agronomy
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Comparison of Sustainable Approaches in Conservation and Protected Agriculture around...
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Comparison of Sustainable Approaches in Conservation and Protected Agriculture around the World

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Farming Sustainability".

Deadline for manuscript submissions: 15 June 2024 | Viewed by 2692

Special Issue Editors

Dr. Ming Li

E-Mail Website
Guest Editor
National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 10089, China
Interests: fruit and vegetable production quality control; plant disease warning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. José Bienvenido-Barcena

E-Mail Website
Guest Editor
Facultad de Ciencias Económicas y Empresariales, Universidad de Almeria, Almeria, Spain
Interests: ICT in agriculture and sustainability
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio Bliska Júnior

E-Mail Website
Guest Editor
College of Agribusiness, State University of Campinas, Campinas, São Paulo 13083-970, Brazil
Interests: protecded cultivation; plasticulture; soilless cultivation; agribusiness management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Agriculture is facing truly important challenges. The demand for food increases as the population increases, and this demand must be fulfilled sustainably, as resources are limited and, in some cases, over-exploited. A holistic approach comprehensively using the concept of sustainability, ecologically, socially and economically, is required.

In the first Volume, "Comparison of Sustainable Approaches in Conservation and Protected Agriculture: Asia, Latin America and Europe" (https://www.mdpi.com/journal/agronomy/special_issues/conservation_protected_agriculture_asia_latinamerica_europe), we presented the status of three different areas of the world that experience extremely different conditions, and collected 13 published papers plus more submission under the support of the academic field, and got nearly 16,000 reviews. In this new volume, we would like to enlarge the study areas to all the world, such as North America, Africa and Oceania.

  • North America: This area is the most developed field with advanced technologies in agriculture. Especially with the leading position of AI (e.g., ChatGPT) in USA, a lot of industrial and non-agronomy experts have entered the agriculture industry to help customers improve digitalization and reduce the cost.
  • Africa: The African Union said that due to the lack of infrastructure, Africa lost about 30% of its agricultural output every year. African agriculture faces soil and fertilizer problems. There is a risk of over-exploitation and erosion of African soil. At the same time, the African continent currently uses 18 kg of fertilizer per hectare of land, which is seriously below the international standard. There is great potential to introduce current studies and cases to improve Africa's agricultural productivity and ensure food security.
  • Oceania: This area has the most valued livestock industry, which supports agronomy crop development. However, the manure and greenhouse gases from the livestock industry have a long-term effect on the sustainable development of agronomy. Solving these problems and balancing the industries are beneficial to other continents.

We would like to invite more authors to submit their manuscripts to enhance the influence of Sustainable Approaches in Conservation and Protected Agriculture around the world.

Dr. Ming Li
Prof. Dr. José Bienvenido-Barcena
Dr. Antonio Bliska Júnior
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. Agronomy is an international peer-reviewed open access monthly 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

  • sustainable agriculture
  • fast agricultural development
  • biodiversity maintenance
  • agricultural evolution toward sustainability

Related Special Issue

Published Papers (3 papers)

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Research

21 pages, 10447 KiB  
Article
Leveraging Hyperspectral Images for Accurate Insect Classification with a Novel Two-Branch Self-Correlation Approach
by Siqiao Tan, Shuzhen Hu, Shaofang He, Lei Zhu, Yanlin Qian and Yangjun Deng
Agronomy 2024, 14(4), 863; https://doi.org/10.3390/agronomy14040863 - 20 Apr 2024
Viewed by 223
Abstract
Insect recognition, crucial for agriculture and ecology studies, benefits from advancements in RGB image-based deep learning, yet still confronts accuracy challenges. To address this gap, the HI30 dataset is introduced, comprising 2115 hyperspectral images across 30 insect categories, which offers richer information than [...] Read more.
Insect recognition, crucial for agriculture and ecology studies, benefits from advancements in RGB image-based deep learning, yet still confronts accuracy challenges. To address this gap, the HI30 dataset is introduced, comprising 2115 hyperspectral images across 30 insect categories, which offers richer information than RGB data for enhancing classification accuracy. To effectively harness this dataset, this study presents the Two-Branch Self-Correlation Network (TBSCN), a novel approach that combines spectrum correlation and random patch correlation branches to exploit both spectral and spatial information. The effectiveness of the HI30 and TBSCN is demonstrated through comprehensive testing. Notably, while ImageNet-pre-trained networks adapted to hyperspectral data achieved an 81.32% accuracy, models developed from scratch with the HI30 dataset saw a substantial 9% increase in performance. Furthermore, applying TBSCN to hyperspectral data raised the accuracy to 93.96%. Extensive testing confirms the superiority of hyperspectral data and validates TBSCN’s efficacy and robustness, significantly advancing insect classification and demonstrating these tools’ potential to enhance precision and reliability. Full article
(This article belongs to the Special Issue Comparison of Sustainable Approaches in Conservation and Protected Agriculture around the World)
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18 pages, 3802 KiB  
Article
Exogenous Melatonin Alleviates the Inhibitory Effect of NaHCO3 on Tomato Growth by Regulating the Root pH Value and Promoting Plant Photosynthesis
by Yuanling Yang, Sihui Guan, Xiyao Jiang, Ming Li, Shaowei Wei and Ming Diao
Agronomy 2023, 13(11), 2777; https://doi.org/10.3390/agronomy13112777 - 08 Nov 2023
Viewed by 707
Abstract
Soil salinity is a severe threat to agricultural production. Most saline soils turn alkaline, increasing the soil pH and, in turn, hampering the growth and development of crops. In this study, the effects of a foliar spray of melatonin (MT; 100 μmol·L−1 [...] Read more.
Soil salinity is a severe threat to agricultural production. Most saline soils turn alkaline, increasing the soil pH and, in turn, hampering the growth and development of crops. In this study, the effects of a foliar spray of melatonin (MT; 100 μmol·L−1) on the pH of the root environment, growth of tomato seedlings, endogenous MT levels, rapid chlorophyll fluorescence induction kinetics, and key enzymes of the Calvin cycle under alkaline (60 mmol·L−1 NaHCO3) stress were studied in Riegel 87-5 tomatoes. The results revealed that the growth and photosynthesis of tomato seedlings were inhibited by increased pH in the root environment under alkali stress; however, the application of exogenous MT reduced the pH of the root environment, alleviated the inhibition of growth of tomato seedlings under alkali stress, increased the content of photosynthetic pigments, alleviated the damage of the donor and acceptor sides of the photosynthetic electron transport chain, increased the activity and efficiency of photosynthetic electron transport, and optimized the share of the light energy allocated to PSII reaction centers. Increased expression levels of Calvin-cycle enzymes, including fructose-1,6-bisphosphate aldolase (FBA), fructose-1,6-bisphosphate esterase (FBP), and phosphoglycerate kinase (PGK), led to enhanced photosynthetic performance in tomato seedlings. Exogenous MT boosted endogenous MT levels and stimulated the production and secretion of organic acids in the root system. This regulation of organic acid content reduced the environmental pH in the inter-root zone, alleviating the damage caused by alkali stress. This study indicated that the exogenous administration of MT may mediate an increase in endogenous MT levels, regulate the efficiency of photosynthesis and root pH levels, and play a crucial role in mitigating injury caused by alkali stress in tomato seedlings. Full article
(This article belongs to the Special Issue Comparison of Sustainable Approaches in Conservation and Protected Agriculture around the World)
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11 pages, 2250 KiB  
Article
Classification of Appearance Quality of Red Grape Based on Transfer Learning of Convolution Neural Network
by Zhihua Zha, Dongyuan Shi, Xiaohui Chen, Hui Shi and Jie Wu
Agronomy 2023, 13(8), 2015; https://doi.org/10.3390/agronomy13082015 - 29 Jul 2023
Cited by 1 | Viewed by 952
Abstract
Grapes are a globally popular fruit, with grape cultivation worldwide being second only to citrus. This article focuses on the low efficiency and accuracy of traditional manual grading of red grape external appearance and proposes a small-sample red grape external appearance grading model [...] Read more.
Grapes are a globally popular fruit, with grape cultivation worldwide being second only to citrus. This article focuses on the low efficiency and accuracy of traditional manual grading of red grape external appearance and proposes a small-sample red grape external appearance grading model based on transfer learning with convolutional neural networks (CNNs). In the naturally growing vineyards, 195,120,135 samples of Grade I, Grade II, and Grade III red grapes were collected using a Canon EOS 550D camera, and a data set of 1800 samples was obtained using data enhancement technology. Then, the CNN transfer learning method was used to transfer the pre-trained AlexNet, VGG16, GoogleNet, InceptionV3, and ResNet50 network models on the ImageNet image dataset to the red grape image grading task. By comparing the classification performance of the CNN models of these five different network depths with fine-tuning, ResNet50 with a learning rate of 0.001 and a loop number of 10 was determined to be the best feature extractor for red grape images. Moreover, given the small number of red grape image samples in this study, different convolutional layer features output by the ResNet50 feature extractor were analyzed layer by layer to determine the effect of deep features extracted by each convolutional layer on Support Vector Machine (SVM) classification performance. This analysis helped to obtain a ResNet50 + SVM red grape external appearance grading model based on the optimal ResNet50 feature extraction strategy. Experimental data showed that the classification model constructed using the feature parameters extracted from the 10th node of the ResNet50 network achieved an accuracy rate of 95.08% for red grape grading. These research results provide a reference for the online grading of red grape clusters based on external appearance quality and have certain guiding significance for the quality and efficiency of grape industry circulation and production. Full article
(This article belongs to the Special Issue Comparison of Sustainable Approaches in Conservation and Protected Agriculture around the World)
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