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Agronomy
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Recent Advances in Data-Driven Farming
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Recent Advances in Data-Driven Farming

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 1 January 2025 | Viewed by 12178

Special Issue Editors

Dr. Han Li

E-Mail Website
Guest Editor
Key Laboratory of Smart Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
Interests: high-throughput phenotyping; robotics application in agriculture; crop diseases and insect pests detection
Dr. Ruicheng Qiu

E-Mail Website
Guest Editor
Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China
Interests: high-throughput phenotyping; machine vision; image processing; spectroscopy and imaging spectroscopy

Special Issue Information

Dear Colleagues,

The development of digital agriculture is driving a new revolution in agricultural technology and profoundly changing the traditional way of agricultural production. One of the characteristics of digital agriculture is that it is digitally driven. Digital agriculture combines remote sensing (RS), geographic information systems (GIS), global positioning systems (GPS), computer technology, communication and network technology, automation technology, and basic disciplines such as geography, agronomy, ecology, plant physiology, soil science, etc., to achieve the purpose of optimizing the use of agricultural resources, reducing production costs, improving the ecological environment, and improving crop product quality.

The purpose of this Special Issue is to publish the latest advances in data-driven farming or data-driven agriculture. The development of data-driven agriculture significantly relies on technologies such as smart sensing, smart decision making, smart driving, and variable operations. Therefore, the topics of this Special Issue include, but are not limited to, agricultural crop information sensing technology, IoT technology, big data technology, cloud computing technology, automatic navigation technology, etc.

Dr. Han Li
Dr. Ruicheng Qiu
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

  • agricultural sensors
  • internet of things
  • big data
  • agricultural cloud computing
  • cloud control platform
  • deep learning
  • data analytics

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Published Papers (6 papers)

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Research

19 pages, 7519 KiB  
Article
Sentinel-2 Multispectral Satellite Remote Sensing Retrieval of Soil Cu Content Changes at Different pH Levels
by Hongxu Guo, Fan Wu, Kai Yang, Ziyan Yang, Zeyu Chen, Dongbin Chen and Rongbo Xiao
Agronomy 2024, 14(10), 2182; https://doi.org/10.3390/agronomy14102182 - 24 Sep 2024
Viewed by 781
Abstract
With the development of multispectral imaging technology, retrieving soil heavy metal content using multispectral remote sensing images has become possible. However, factors such as soil pH and spectral resolution affect the accuracy of model inversion, leading to low precision. In this study, 242 [...] Read more.
With the development of multispectral imaging technology, retrieving soil heavy metal content using multispectral remote sensing images has become possible. However, factors such as soil pH and spectral resolution affect the accuracy of model inversion, leading to low precision. In this study, 242 soil samples were collected from a typical area of the Pearl River Delta, and the Cu content in the soil was detected in the laboratory. Simultaneously, Sentinel-2 remote sensing image data were collected, and two-dimensional and three-dimensional spectral indices were established. Constructing independent decision trees based on pH values, using the Successive Projections Algorithm (SPA) combined with the Boruta algorithm to select the characteristic bands for soil Cu content, and this was combined with Optuna automatic hyperparameter optimization for ensemble learning models to establish a model for estimating Cu content in soil. The research results indicated that in the SPA combined with the Boruta feature selection algorithm, the characteristic spectral indices were mainly concentrated in the spectral transformation forms of TBI2 and TBI4. Full-sample modeling lacked predictive ability, but after classifying the samples based on soil pH value, the R2 of the RF and XGBoost models constructed with the samples with pH values between 5.85 and 7.75 was 0.54 and 0.76, respectively, with corresponding RMSE values of 22.48 and 16.12 and RPD values of 1.51 and 2.11. This study shows that the inversion of soil Cu content under different pH conditions exhibits significant differences, and determining the optimal pH range can effectively improve inversion accuracy. This research provides a reference for further achieving the efficient and accurate remote sensing of heavy metal pollution in agricultural soil. Full article
(This article belongs to the Special Issue Recent Advances in Data-Driven Farming)
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14 pages, 1207 KiB  
Article
Efficient Adaptive Incremental Learning for Fruit and Vegetable Classification
by Kaitai Guo, Hongliang Chen, Yang Zheng, Qixin Liu, Shenghan Ren, Haihong Hu and Jimin Liang
Agronomy 2024, 14(6), 1275; https://doi.org/10.3390/agronomy14061275 - 12 Jun 2024
Viewed by 907
Abstract
Traditional deep learning models for fruit and vegetable classification are usually implemented via training on an unchanged dataset. However, changing fruit and vegetable categories is a very common occurrence in the context of real agricultural sales. When dealing with changes related to variety, [...] Read more.
Traditional deep learning models for fruit and vegetable classification are usually implemented via training on an unchanged dataset. However, changing fruit and vegetable categories is a very common occurrence in the context of real agricultural sales. When dealing with changes related to variety, deep learning models need to be retrained on the entire updated dataset. The retraining process is time-consuming and inefficient, and it may even cause the ‘catastrophic forgetting’ problem. In response to this challenge, the Adversarial Domain Adaptation Class Incremental Learning (ADA-CIL) method is introduced. This approach employs adversarial domain adaptation techniques combined with core-set selection strategies to effectively extract and integrate cross-domain features. We utilize the ResNet34 architecture as the backbone for feature extraction due to its deep residual learning framework, which is robust in handling the complexities of large and varied image datasets. It achieves a dynamic balance in learning between new and existing categories, significantly enhancing the model’s generalization capabilities and information retention efficiency. The FruVeg dataset, composed of three sub-datasets, includes over 120,000 color images, covering more than 100 different categories of fruits and vegetables collected from various domains and backgrounds. The experimental results on the FruVeg dataset show that the ADA-CIL method achieves an average accuracy of 96.30%, a forgetting rate of 2.96%, a cumulative accuracy of 96.26%, and a current accuracy of 98.60%. The ADA-CIL method improves the average accuracy by 1.65% and 1.82% compared to iCaRL and BiC, respectively, and it reduces the forgetting rate by 2.69% and 2.76%. These performance metrics demonstrate the ADA-CIL method’s impressive ability to handle incremental category and domain changes, highlighting its capability to effectively maintain the intra-class stability and exhibit exceptional adaptability in dynamic learning environments. Full article
(This article belongs to the Special Issue Recent Advances in Data-Driven Farming)
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16 pages, 11393 KiB  
Article
Detection of Broken Hongshan Buckwheat Seeds Based on Improved YOLOv5s Model
by Xin Li, Wendong Niu, Yinxing Yan, Shixing Ma, Jianxun Huang, Yingmei Wang, Renjie Chang and Haiyan Song
Agronomy 2024, 14(1), 37; https://doi.org/10.3390/agronomy14010037 - 22 Dec 2023
Viewed by 1277
Abstract
Breeding technology is one of the necessary means for agricultural development, and the automatic identification of poor seeds has become a trend in modern breeding. China is one of the main producers of buckwheat, and the cultivation of Hongshan buckwheat plays an important [...] Read more.
Breeding technology is one of the necessary means for agricultural development, and the automatic identification of poor seeds has become a trend in modern breeding. China is one of the main producers of buckwheat, and the cultivation of Hongshan buckwheat plays an important role in agricultural production. The quality of seeds affects the final yield, and improving buckwheat breeding technology is particularly important. In order to quickly and accurately identify broken Hongshan buckwheat seeds, an identification algorithm based on an improved YOLOv5s model is proposed. Firstly, this study added the Ghost module to the YOLOv5s model, which improved the model’s inference speed. Secondly, we introduced the bidirectional feature pyramid network (BiFPN) to the neck of the YOLOv5s model, which facilitates multi-scale fusion of Hongshan buckwheat seeds. Finally, we fused the Ghost module and BiFPN to form the YOLOV5s+Ghost+BiFPN model for identifying broken Hongshan buckwheat seeds. The results show that the precision of the YOLOV5s+Ghost+BiFPN model is 99.7%, which is 11.7% higher than the YOLOv5s model, 1.3% higher than the YOLOv5+Ghost model, and 0.7% higher than the YOLOv5+BiFPN model. Then, we compared the FLOPs value, model size, and confidence. Compared to the YOLOv5s model, the FLOPs value decreased by 6.8 G, and the model size decreased by 5.2 MB. Compared to the YOLOv5+BiFPN model, the FLOPs value decreased by 8.1 G, and the model size decreased by 7.3MB. Compared to the YOLOv5+Ghost model, the FLOPs value increased by only 0.9 G, and the model size increased by 1.4 MB, with minimal numerical fluctuations. The YOLOv5s+Ghost+BiFPN model has more concentrated confidence. The YOLOv5s+Ghost+BiFPN model is capable of fast and accurate recognition of broken Hongshan buckwheat seeds, meeting the requirements of lightweight applications. Finally, based on the improved YOLOv5s model, a system for recognizing broken Hongshan buckwheat seeds was designed. The results demonstrate that the system can effectively recognize seed features and provide technical support for the intelligent selection of Hongshan buckwheat seeds. Full article
(This article belongs to the Special Issue Recent Advances in Data-Driven Farming)
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17 pages, 9020 KiB  
Article
Real-Time Lightweight Detection of Lychee Diseases with Enhanced YOLOv7 and Edge Computing
by Jiayi Xiao, Gaobi Kang, Linhui Wang, Yongda Lin, Fanguo Zeng, Jianyu Zheng, Rong Zhang and Xuejun Yue
Agronomy 2023, 13(12), 2866; https://doi.org/10.3390/agronomy13122866 - 22 Nov 2023
Cited by 2 | Viewed by 1595
Abstract
Lychee is an economically important crop with widespread popularity. However, lychee diseases significantly impact both the yield and fruit quality of lychee. Existing lychee disease detection models face challenges such as large parameter sizes, slow processing speeds, and deployment complexities. To address these [...] Read more.
Lychee is an economically important crop with widespread popularity. However, lychee diseases significantly impact both the yield and fruit quality of lychee. Existing lychee disease detection models face challenges such as large parameter sizes, slow processing speeds, and deployment complexities. To address these challenges, this paper proposes an improved lightweight network, named YOLOv7-MGPC (YOLOv7-Mosaic-GhostNet-Pruning-CBAM), that enables real-time lychee disease detection. In this study, we collected datasets of lychee diseases, covering four types of leaf diseases, and employed Mosaic data augmentation for data preprocessing. Building upon the YOLOv7 framework, we replaced the original backbone network with the lightweight GhostNetV1 and applied channel pruning to effectively reduce the parameter overhead. Subsequently, an attention mechanism called CBAM was incorporated to enhance the detection accuracy. The resultant model was then deployed to edge devices (Nvidia Jetson Nano) for real-world applications. Our experiments showed that our enhanced YOLOv7 variant outperforms the original model by a large margin, achieving a speed increase from 120 frames/s to 217 frames/s while maintaining an accuracy of 88.6%. Furthermore, the parameter size was substantially reduced from 36.5 M to 7.8 M, which firmly demonstrates the effectiveness of our methods in enabling model deployment on edge devices for lychee disease detection. Full article
(This article belongs to the Special Issue Recent Advances in Data-Driven Farming)
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16 pages, 22699 KiB  
Article
Deep-Learning-Based Trunk Perception with Depth Estimation and DWA for Robust Navigation of Robotics in Orchards
by Peichen Huang, Peikui Huang, Zihong Wang, Xiao Wu, Jie Liu and Lixue Zhu
Agronomy 2023, 13(4), 1084; https://doi.org/10.3390/agronomy13041084 - 10 Apr 2023
Cited by 7 | Viewed by 1780
Abstract
Agricultural robotics is a complex, challenging, and exciting research topic nowadays. However, orchard environments present harsh conditions for robotics operability, such as terrain irregularities, illumination, and inaccuracies in GPS signals. To overcome these challenges, reliable landmarks must be extracted from the environment. This [...] Read more.
Agricultural robotics is a complex, challenging, and exciting research topic nowadays. However, orchard environments present harsh conditions for robotics operability, such as terrain irregularities, illumination, and inaccuracies in GPS signals. To overcome these challenges, reliable landmarks must be extracted from the environment. This study addresses the challenge of accurate, low-cost, and efficient landmark identification in orchards to enable robot row-following. First, deep learning, integrated with depth information, is used for real-time trunk detection and location. The in-house dataset used to train the models includes a total of 2453 manually annotated trunks. The results show that the trunk detection achieves an overall mAP of 81.6%, an inference time of 60 ms, and a location accuracy error of 9 mm at 2.8 m. Secondly, the environmental features obtained in the first step are fed into the DWA. The DWA performs reactive obstacle avoidance while attempting to reach the row-end destination. The final solution considers the limitations of the robot’s kinematics and dynamics, enabling it to maintain the row path and avoid obstacles. Simulations and field tests demonstrated that even with a certain initial deviation, the robot could automatically adjust its position and drive through the rows in the real orchard. Full article
(This article belongs to the Special Issue Recent Advances in Data-Driven Farming)
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23 pages, 4659 KiB  
Article
BMAE-Net: A Data-Driven Weather Prediction Network for Smart Agriculture
by Jian-Lei Kong, Xiao-Meng Fan, Xue-Bo Jin, Ting-Li Su, Yu-Ting Bai, Hui-Jun Ma and Min Zuo
Agronomy 2023, 13(3), 625; https://doi.org/10.3390/agronomy13030625 - 22 Feb 2023
Cited by 23 | Viewed by 4055
Abstract
Weather is an essential component of natural resources that affects agricultural production and plays a decisive role in deciding the type of agricultural production, planting structure, crop quality, etc. In field agriculture, medium- and long-term predictions of temperature and humidity are vital for [...] Read more.
Weather is an essential component of natural resources that affects agricultural production and plays a decisive role in deciding the type of agricultural production, planting structure, crop quality, etc. In field agriculture, medium- and long-term predictions of temperature and humidity are vital for guiding agricultural activities and improving crop yield and quality. However, existing intelligent models still have difficulties dealing with big weather data in predicting applications, such as striking a balance between prediction accuracy and learning efficiency. Therefore, a multi-head attention encoder-decoder neural network optimized via Bayesian inference strategy (BMAE-Net) is proposed herein to predict weather time series changes accurately. Firstly, we incorporate Bayesian inference into the gated recurrent unit to construct a Bayesian-gated recurrent units (Bayesian-GRU) module. Then, a multi-head attention mechanism is introduced to design the network structure of each Bayesian layer, improving the prediction applicability to time-length changes. Subsequently, an encoder-decoder framework with Bayesian hyperparameter optimization is designed to infer intrinsic relationships among big time-series data for high prediction accuracy. For example, the R-evaluation metrics for temperature prediction in the three locations are 0.9, 0.804, and 0.892, respectively, while the RMSE is reduced to 2.899, 3.011, and 1.476, as seen in Case 1 of the temperature data. Extensive experiments subsequently demonstrated that the proposed BMAE-Net has overperformed on three location weather datasets, which provides an effective solution for prediction applications in the smart agriculture system. Full article
(This article belongs to the Special Issue Recent Advances in Data-Driven Farming)
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