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Agronomy
Special Issues
Effects of Integrated Environment Management on Crop Photosynthesis
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Effects of Integrated Environment Management on Crop Photosynthesis

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Plant-Crop Biology and Biochemistry".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 2150

Special Issue Editor

Prof. Dr. Jin Hu

E-Mail Website
Guest Editor
College of Information Engineering, Northwest A&F University, Yangling, Xianyang, China
Interests: agricultural artificial intelligence; agricultural big data; precision agriculture; efficient production of crops
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The environment has a significant impact on the growth of crops. Suitable environmental conditions can enhance crop photosynthesis, yield and quality. The coupling of multiple environmental factors increases the complexity of environmental impacts on crops. Plant photosynthesis can be used to monitor the growth of crops, and multiple integrated environmental factors influence plant photosynthesis, which is conducive to environmental regulation. With the emergence of smart agriculture, data analysis and mining using artificial intelligence algorithms for crop–environment interaction are becoming increasingly important. Additionally, the construction of crop growth/photosynthesis models is also a key method to achieve intelligent environmental regulation.

The impact and mechanism of environmental information on crop growth and photosynthesis are the foundation of environmental regulation. Submissions to this Special Issue must be within the scope of plant–environment interaction.

Plant species: agronomic crops, horticultural crops, cereal crops, forestry crops, medicinal crops.

Research areas of interest include:

  • Data analysis and mining using artificial intelligence algorithms for plant–environment interaction;
  • Crop growth/photosynthesis models based on artificial intelligence algorithms;
  • Prediction and identification models for crop phenotype and disease.
  • We welcome papers covering the following topics:
  • Data analysis and mining using artificial intelligence algorithms for plant–environment interaction;
  • The influence mechanism of the environment on plant photosynthesis;
  • Environmental intelligent decision regulation;
  • Crop growth/photosynthesis models.

Prof. Dr. Jin Hu
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 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

  • environmental regulation
  • models
  • photosynthesis
  • phenotypic model
  • disease detection model
  • growth model

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

Published Papers (2 papers)

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Research

12 pages, 2733 KiB  
Article
A Study of Kale Recognition Based on Semantic Segmentation
by Huarui Wu, Wang Guo, Chang Liu and Xiang Sun
Agronomy 2024, 14(5), 894; https://doi.org/10.3390/agronomy14050894 - 25 Apr 2024
Cited by 1 | Viewed by 848
Abstract
The kale crop is an important bulk vegetable, and automatic segmentation to recognize kale is fundamental for effective field management. However, complex backgrounds and texture-rich edge details make fine segmentation of kale difficult. To this end, we constructed a kale dataset in a [...] Read more.
The kale crop is an important bulk vegetable, and automatic segmentation to recognize kale is fundamental for effective field management. However, complex backgrounds and texture-rich edge details make fine segmentation of kale difficult. To this end, we constructed a kale dataset in a real field scenario and proposed an UperNet semantic segmentation model with a Swin transformer as the backbone network and improved the model according to the growth characteristics of kale. Firstly, a channel attention module (CAM) is introduced into the Swin transformer module to improve the representation ability of the network and enhance the extraction of kale outer leaf and leaf bulb information; secondly, the extraction accuracy of kale target edges is improved in the decoding part by designing an attention refinement module (ARM); lastly, the uneven distribution of classes is solved by modifying the optimizer and loss function to solve the class distribution problem. The experimental results show that the improved model in this paper has excellent performance in feature extraction, and the average intersection and merger ratio (mIOU) of the improved kale segmentation can be up to 91.2%, and the average pixel accuracy (mPA) can be up to 95.2%, which is 2.1 percentage points and 4.7 percentage points higher than the original UperNet model, respectively, and it effectively improves the segmentation recognition of kale. Full article
(This article belongs to the Special Issue Effects of Integrated Environment Management on Crop Photosynthesis)
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13 pages, 5571 KiB  
Article
Cabbage Transplantation State Recognition Model Based on Modified YOLOv5-GFD
by Xiang Sun, Yisheng Miao, Xiaoyan Wu, Yuansheng Wang, Qingxue Li, Huaji Zhu and Huarui Wu
Agronomy 2024, 14(4), 760; https://doi.org/10.3390/agronomy14040760 - 8 Apr 2024
Cited by 2 | Viewed by 972
Abstract
To enhance the transplantation effectiveness of vegetables and promptly formulate subsequent work strategies, it is imperative to study the recognition approach for transplanted seedlings. In the natural and complex environment, factors like background and sunlight often hinder accurate target recognition. To overcome these [...] Read more.
To enhance the transplantation effectiveness of vegetables and promptly formulate subsequent work strategies, it is imperative to study the recognition approach for transplanted seedlings. In the natural and complex environment, factors like background and sunlight often hinder accurate target recognition. To overcome these challenges, this study explores a lightweight yet efficient algorithm for recognizing cabbage transplantation states in natural settings. Initially, FasterNet is integrated as the backbone network in the YOLOv5s model, aiming to expedite convergence speed and bolster feature extraction capabilities. Secondly, the introduction of the GAM attention mechanism enhances the algorithm’s focus on cabbage seedlings. EIoU loss is incorporated to improve both network convergence speed and localization precision. Lastly, the model incorporates deformable convolution DCNV3, which further optimizes model parameters and attains a superior balance between accuracy and speed. Upon testing the refined YOLOv5s target detection algorithm, improvements were evident. When compared to the original model, the mean average precision (mAP) rose by 3.5 percentage points, recall increased by 1.7 percentage points, and detection speed witnessed an impressive boost of 52 FPS. This enhanced algorithm not only reduces model complexity but also elevates network performance. The method is expected to streamline transplantation quality measurements, minimize time and labor inputs, and elevate field transplantation quality surveys’ automation levels. Full article
(This article belongs to the Special Issue Effects of Integrated Environment Management on Crop Photosynthesis)
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