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Agriculture
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Innovative Technology and Intelligent Equipment for Field Crop Mechanization Production
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Innovative Technology and Intelligent Equipment for Field Crop Mechanization Production

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Technology".

Deadline for manuscript submissions: closed (25 January 2024) | Viewed by 3688

Special Issue Editors

Dr. Wenqi Zhou

E-Mail Website
Guest Editor
College of Engineering, Northeast Agricultural University, Harbin 150030, China
Interests: field crops production; precision fertilization; remote sensing; drone monitoring; root and tuber vegetables harvesting; bionic technology in agricultural engineering
Prof. Dr. Jinwu Wang

E-Mail Website
Guest Editor
College of Engineering, Northeast Agricultural University, Harbin 150030, China
Interests: agricultural machinery; auto-control; simulation analysis; coupling analysis; intelligent paddy field agricultural equipment and technology; mechanized technology for protecting the quality of black soil farmland and improving fertilizer efficiency
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues，

With the continuous advancement and development of technology, particularly in the areas of artificial intelligence and machine learning, the agricultural industry is also undergoing a revolution. This Special Issue aims to focus on the application of innovative technology and intelligent equipment for field crops production, such as remote monitoring technologies using drones, intelligent seeding, fertilizing, and harvesting techniques and equipment. These advancements are aimed at ensuring higher agricultural production efficiency, reducing labor costs, and minimizing environmental pollution.

This Special Issue, titled "Innovative Technology and Intelligent Equipment for Field Crop Mechanization Production", covers a wide range of field crops, including food crops, vegetable crops, ornamental crops, and industrial crops. All types of research articles are welcome, including original research, opinions, and reviews. The goal of this Issue is to provide readers with a comprehensive understanding of the latest technological advancements in the field of agriculture.

Dr. Wenqi Zhou
Prof. Dr. Jinwu 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. Agriculture 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

  • intelligent agricultural machinery equipment
  • unmanned aerial vehicle remote sensing monitoring technology
  • agricultural machine vision technology

Published Papers (3 papers)

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Research

21 pages, 25424 KiB  
Article
Fast and Precise Detection of Dense Soybean Seedlings Images Based on Airborne Edge Device
by Zishang Yang, Jiawei Liu, Lele Wang, Yunhui Shi, Gongpei Cui, Li Ding and He Li
Agriculture 2024, 14(2), 208; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture14020208 - 28 Jan 2024
Viewed by 894
Abstract
During the growth stage of soybean seedlings, it is crucial to quickly and precisely identify them for emergence rate assessment and field management. Traditional manual counting methods have some limitations in scenarios with large-scale and high-efficiency requirements, such as being time-consuming, labor-intensive, and [...] Read more.
During the growth stage of soybean seedlings, it is crucial to quickly and precisely identify them for emergence rate assessment and field management. Traditional manual counting methods have some limitations in scenarios with large-scale and high-efficiency requirements, such as being time-consuming, labor-intensive, and prone to human error (such as subjective judgment and visual fatigue). To address these issues, this study proposes a rapid detection method suitable for airborne edge devices and large-scale dense soybean seedling field images. For the dense small target images captured by the Unmanned Aerial Vehicle (UAV), the YOLOv5s model is used as the improvement benchmark in the technical solution. GhostNetV2 is selected as the backbone feature extraction network. In the feature fusion stage, an attention mechanism—Efficient Channel Attention (ECA)—and a Bidirectional Feature Pyramid Network (BiFPN) have been introduced to ensure the model prioritizes the regions of interest. Addressing the challenge of small-scale soybean seedlings in UAV images, the model’s input size is set to 1280 × 1280 pixels. Simultaneously, Performance-aware Approximation of Global Channel Pruning for Multitask CNNs (PAGCP) pruning technology is employed to meet the requirements of mobile or embedded devices. The experimental results show that the identification accuracy of the improved YOLOv5s model reached 92.1%. Compared with the baseline model, its model size and total parameters were reduced by 76.65% and 79.55%, respectively. Beyond these quantitative evaluations, this study also conducted field experiments to verify the detection performance of the improved model in various scenarios. By introducing innovative model structures and technologies, the study aims to effectively detect dense small target features in UAV images and provide a feasible solution for assessing the number of soybean seedlings. In the future, this detection method can also be extended to similar crops. Full article
(This article belongs to the Special Issue Innovative Technology and Intelligent Equipment for Field Crop Mechanization Production)
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16 pages, 4441 KiB  
Article
Advancing Early Fault Diagnosis for Multi-Domain Agricultural Machinery Rolling Bearings through Data Enhancement
by Fengyun Xie, Gang Li, Hui Liu, Enguang Sun and Yang Wang
Agriculture 2024, 14(1), 112; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture14010112 - 10 Jan 2024
Viewed by 1211
Abstract
In the context of addressing the challenge posed by limited fault samples in agricultural machinery rolling bearings, especially when early fault characteristics are subtle, this study introduces a novel approach. The proposed multi-domain fault diagnosis method, anchored in data augmentation, aims to discern [...] Read more.
In the context of addressing the challenge posed by limited fault samples in agricultural machinery rolling bearings, especially when early fault characteristics are subtle, this study introduces a novel approach. The proposed multi-domain fault diagnosis method, anchored in data augmentation, aims to discern early faults in agricultural machinery rolling bearings, particularly within an imbalanced sample framework. The methodology involves determining early fault signals throughout the life cycle, constructing early fault datasets with varying imbalance rates for different fault types, and subsequently employing the Synthetic Minority Oversampling Technique (SMOTE) to balance the fault data. The study then extracts relative wavelet packet energy and time-domain sensitive features (variance, peak to peak) from the original and generated fault data to form a multi-domain fault feature vector. This vector is utilized for fault state recognition using a Support Vector Machine (SVM). Evaluation metrics such as accuracy, recall, and F1 values assess the recognition effectiveness for each rolling bearing state, with the overall model recognition evaluated based on accuracy. The proposed method is rigorously analyzed and validated using the XJTU-SY rolling bearing accelerated life test dataset. Comparative analysis is conducted with non-data enhanced fault feature vectors, specifically the relative energy of the wavelet packet, both with and without time-domain features. Experimental results underscore the superior performance of multi-domain fault features in providing a comprehensive description of signal information, leading to enhanced classification performance. Furthermore, the study demonstrates improved classification accuracy and recall rates for the balanced dataset compared to the imbalanced dataset. This research significantly contributes to an effective identification method for the early fault diagnosis of small sample rolling bearings in agricultural machinery. Full article
(This article belongs to the Special Issue Innovative Technology and Intelligent Equipment for Field Crop Mechanization Production)
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12 pages, 3147 KiB  
Article
Design of an Electronically Controlled Fertilization System for an Air-Assisted Side-Deep Fertilization Machine
by Qingzhen Zhu, Zhihao Zhu, Hengyuan Zhang, Yuanyuan Gao and Liping Chen
Agriculture 2023, 13(12), 2210; https://0-doi-org.brum.beds.ac.uk/10.3390/agriculture13122210 - 28 Nov 2023
Cited by 2 | Viewed by 1024
Abstract
The traditional air-assisted side-deep fertilization device has some problems, such as inaccurate control system parameters and poor precision in variable fertilization. It seriously affects the application and popularization of the device. Aiming at the above problems, this paper wanted to realize the precise [...] Read more.
The traditional air-assisted side-deep fertilization device has some problems, such as inaccurate control system parameters and poor precision in variable fertilization. It seriously affects the application and popularization of the device. Aiming at the above problems, this paper wanted to realize the precise fertilizer discharge control of an air-assisted side-deep fertilization device. This paper designs an electronically controlled fertilization system based on a PID controller from the past. The system model was constructed in MATLAB, and the mathematical model and transfer function model of a stepper motor, the mathematical model of fertilizer discharge, and the stepper motor rotational speed were established too. In order to improve the accuracy of precise fertilizer discharge control system parameters, the system parameters were optimized based on the particle swarm optimization algorithm and the control system tuner toolbox. We had established a validation test platform to test the performance of a precise fertilizer discharge control system. In the actual experiment, the maximum stability coefficient of variation was 0.91% at the target fertilizer discharge mass level of 350 g/min, and the maximum error of fertilizer discharge was 4.14% at 550 g/min of the target fertilizer discharge mass level. By analyzing the test results of the precise fertilizer discharge control system, the new precise fertilizer discharge control system had good fertilizer discharge stability and could also meet the technical specification for quality evaluation of fertilization machinery (NY/T 1003-2006). This research can improve the fertilizer discharge accuracy of the air-assisted side-deep fertilization control system. Full article
(This article belongs to the Special Issue Innovative Technology and Intelligent Equipment for Field Crop Mechanization Production)
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