Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume

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31 July 2025

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30 September 2025

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Topic Information

Dear Colleagues,

As agriculture and forestry enter the digital era, the digital technology related to these topics has started to become widespread. This is referred to as smart agriculture and forestry, which is characterized by “information + knowledge + intelligent equipment”, realizing the deep cross-domain integration of modern information technology and agriculture and forestry. In the future, the development of smart agriculture and forestry will focus on information and intelligent technology to build an integrated platform for smart perception, wireless transmission, smart decision-making and intelligent control, so as to realize the intelligent supervision of the entire agricultural and forestry industry chain (production, processing, operation, management and service), and finally achieve cost reduction, quality improvements and increased efficiency. The rapid development of artificial intelligence, UAV, remote sensing, big data analysis and other technologies provides a substantial support for agriculture and forestry to move forward to the intelligent era.

We are pleased to announce the opening of Volume II, “Challenges, Development and Frontiers of Smart Agriculture and Forestry-II”, which will follow on from Volume I. In this second edition, we aim to fully explore frontier technologies, including the internet of things, multi-modal and multi-angle stereo observation, cloud computing, big data, machine learning, intelligent decision-making, etc., thereby exploring their application potential in smart agriculture and forestry and accelerating the development of precision agriculture and forestry.

This Topic welcomes high-quality works that focus on the innovation of five core technologies, including remote sensing, advanced sensors, big data and cloud services, precision operation technology and equipment, internet of things and robots, as well as the solutions, strategies, pilot cases and examples of these frontier technologies applied in the fields of forest and farmland investigation and change monitoring, plant protection, disaster prevention, precision cultivation, visual management, ecological protection and reconstruction, and evaluation of ecological service value. Relevant themes include, but are not limited to, the following:

Intelligent sensing technology and equipment for agriculture and forestry

• Advanced agroforestry sensors and data analysis;
• Intelligent robot integrating cloud computing, big data and machine learning technology;
• Intelligent agricultural and forestry machinery equipment and technology;
• Development and test of agricultural and forestry intelligent equipment.

New methods and technologies for precision forestry investigation, monitoring and evaluation

• New methods and technology of forest resources investigation based on multi-source remote sensing;
• Forest change and disturbance detection using multi-modal remote sensing technology;
• Forest degradation-restoration monitoring combining remote sensing and geographic big data;
• Forest disaster monitoring and prediction integrating remote sensing and geographic big data;
• Precision estimation and modelling of forest structure and function parameters;
• Forest visualization and management.

Technological innovation in agriculture 4.0 era

• Innovative sensors and technologies for smart agriculture;
• Application of deep learning and geographic big data in smart breeding and germplasm resources;
• Smart technologies and decision support for precision orchard production;
• Multi technology integration for high-throughput plant phenotyping;
• Intelligent in situ monitoring and decision-making of crop typical stress.

Sustainable development of agriculture and forestry ecology

• Assessment of ecosystem service function based on geographic big data analysis;
• Precise estimation of agricultural and forestry carbon storage;
• Vegetation species diversity evaluation based on remote sensing and machine learning;
• Agriculture and forestry approaches and contributions to achieve carbon neutralization;
• Challenges and opportunities to the construction of smart agriculture and forestry.

Prof. Dr. Xiaoli Zhang
Prof. Dr. Dengsheng Lu
Prof. Dr. Xiujuan Chai
Prof. Dr. Guijun Yang
Dr. Langning Huo
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agriculture agriculture	3.3	4.9	2011	19.2 Days	CHF 2600	Submit
Agronomy agronomy	3.3	6.2	2011	17.6 Days	CHF 2600	Submit
Forests forests	2.4	4.4	2010	16.2 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700	Submit
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400	Submit

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Published Papers (19 papers)

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26 pages, 17568 KiB  

Open AccessArticle

Research on Apple Detection and Tracking Count in Complex Scenes Based on the Improved YOLOv7-Tiny-PDE

by Dongxuan Cao, Wei Luo, Ruiyin Tang, Yuyan Liu, Jiasen Zhao, Xuqing Li and Lihua Yuan

Agriculture 2025, 15(5), 483; https://doi.org/10.3390/agriculture15050483 - 24 Feb 2025

Cited by 1 | Viewed by 487

Abstract

Accurately detecting apple fruit can crucially assist in estimating the fruit yield in apple orchards in complex scenarios. In such environments, the factors of density, leaf occlusion, and fruit overlap can affect the detection and counting accuracy. This paper proposes an improved YOLOv7-Tiny-PDE [...] Read more.

Accurately detecting apple fruit can crucially assist in estimating the fruit yield in apple orchards in complex scenarios. In such environments, the factors of density, leaf occlusion, and fruit overlap can affect the detection and counting accuracy. This paper proposes an improved YOLOv7-Tiny-PDE network model based on the YOLOv7-Tiny model to detect and count apples from data collected by drones, considering various occlusion and lighting conditions. First, within the backbone network, we replaced the simplified efficient layer aggregation network (ELAN) with partial convolution (PConv), reducing the network parameters and computational redundancy while maintaining the detection accuracy. Second, in the neck network, we used a dynamic detection head to replace the original detection head, effectively suppressing the background interference and capturing the background information more comprehensively, thus enhancing the detection accuracy for occluded targets and improving the fruit feature extraction. To further optimize the model, we replaced the boundary box loss function from CIOU to EIOU. For fruit counting across video frames in complex occlusion scenes, we integrated the improved model with the DeepSort tracking algorithm based on Kalman filtering and motion trajectory prediction with a cascading matching algorithm. According to experimental results, compared with the baseline YOLOv7-Tiny, the improved model reduced the total parameters by 22.2% and computation complexity by 18.3%. Additionally, in data testing, the p-value improved by 0.5%; the R-value rose by 2.7%; the mAP and F1 scores rose by 4% and 1.7%, respectively; and the MOTA value improved by 2%. The improved model is more lightweight and can preserve a high detection accuracy well, and hence, it can be applied to detection and counting tasks in complex orchards and provides a new solution for fruit yield estimation using lightweight devices. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

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20 pages, 6378 KiB  

Open AccessArticle

Edge Computing-Based Machine Vision for Non-Invasive and Rapid Soft Sensing of Mushroom Liquid Strain Biomass

by Libin Wu, Guimiao Xiao, Deyao Huang, Xiandong Zhang, Dapeng Ye and Haiyong Weng

Agronomy 2025, 15(1), 242; https://doi.org/10.3390/agronomy15010242 - 20 Jan 2025

Viewed by 1236

Abstract

Biomass monitoring of mushroom liquid strains during the fermentation process demands real-time analysis with minimal manual intervention, highlighting the urgent need for intelligent surveillance. This study introduced a soft sensor method based on edge computing machine vision, termed Edge CV, for in situ [...] Read more.

Biomass monitoring of mushroom liquid strains during the fermentation process demands real-time analysis with minimal manual intervention, highlighting the urgent need for intelligent surveillance. This study introduced a soft sensor method based on edge computing machine vision, termed Edge CV, for in situ non-invasive estimation of biomass. In our experiment, the hardware of the Edge CV system includes the Jetson Nano with 4 GB RAM, 64 GB ROM, and a 128-core Maxwell GPU for executing intelligent machine vision tasks, along with embedded cameras for image data acquisition. Furthermore, a cascaded machine vision model was developed to enable biomass evaluation on the Edge CV system. The cascaded machine vision model mainly consists of three steps: first, the object detection task to locate the observation window, achieving a mean Average Precision (mAP50:95) of 82.3% with 78.7 GFLOPs; then, the segmentation task to extract liquid strain data within the observation window, yielding a mean intersection over union (MIoU) of 85.9% with 110.4 GFLOPs; and finally, calculating mycelium biomass indices via the morphological image processing task. The correlation between Edge CV inference and manual measurement showed an R² of 0.963 and an RMSE of 0.027 for normalized biomass indices, demonstrating a robust and consistent trend. Therefore, this study illustrates the practical application of edge computing-based machine vision for biomass soft sensing during the fermentation process. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

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26 pages, 16808 KiB  

Open AccessArticle

Design and Experimental Evaluation of a Smart Intra-Row Weed Control System for Open-Field Cabbage

by Shenyu Zheng, Xueguan Zhao, Hao Fu, Haoran Tan, Changyuan Zhai and Liping Chen

Agronomy 2025, 15(1), 112; https://doi.org/10.3390/agronomy15010112 - 4 Jan 2025

Cited by 2 | Viewed by 1038

Abstract

Addressing the challenges of complex structure, limited modularization capability, and insufficient responsiveness in traditional hydraulically driven inter-plant mechanical weeding equipment, this study designed and developed an electric swing-type opening and closing intra-row weeding control system. The system integrates deep learning technology for accurate [...] Read more.

Addressing the challenges of complex structure, limited modularization capability, and insufficient responsiveness in traditional hydraulically driven inter-plant mechanical weeding equipment, this study designed and developed an electric swing-type opening and closing intra-row weeding control system. The system integrates deep learning technology for accurate identification and localization of cabbage, enabling precise control and dynamic obstacle avoidance for the weeding knives. The system’s performance was comprehensively evaluated through laboratory and field experiments. Laboratory experiments demonstrated that, under conditions of low speed and large plant spacing, the system achieved a weeding accuracy of 96.67%, with a minimum crop injury rate of 0.83%. However, as the operational speed increased, the weeding accuracy decreased while the crop injury rate increased. Two-way ANOVA results indicated that operational speed significantly affected both weeding accuracy and crop injury rate, whereas plant spacing had a significant effect on weeding accuracy but no significant effect on crop injury rate. Field experiment results further confirmed that the system maintained high weeding accuracy and crop protection under varying speed conditions. At a low speed of 0.1 m/s, the weeding accuracy was 96.00%, with a crop injury rate of 1.57%. However, as the speed increased to 0.5 m/s, the weeding accuracy dropped to 81.79%, while the crop injury rate rose to 5.49%. These experimental results verified the system’s adaptability and reliability in complex field environments, providing technical support for the adoption of intelligent mechanical weeding systems. Future research will focus on optimizing control algorithms and feedback mechanisms to enhance the system’s dynamic response capability and adaptability, thereby advancing the development of sustainable agriculture and precision field management. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

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24 pages, 8042 KiB  

Open AccessArticle

Quantitative Genetic Aspects of Accuracy of Tree Biomass Measurement Using LiDAR

by Haruka Sano, Naoko Miura, Minoru Inamori, Yamato Unno, Wei Guo, Sachiko Isobe, Kazutaka Kusunoki and Hiroyoshi Iwata

Remote Sens. 2024, 16(24), 4790; https://doi.org/10.3390/rs16244790 - 22 Dec 2024

Viewed by 1113

Abstract

The growing focus on the role of forests in carbon sequestration highlights the importance of accurately and efficiently measuring biophysical traits, such as diameter at breast height (DBH) and tree height. Understanding genetic contributions to trait variation is crucial for enhancing carbon storage [...] Read more.

The growing focus on the role of forests in carbon sequestration highlights the importance of accurately and efficiently measuring biophysical traits, such as diameter at breast height (DBH) and tree height. Understanding genetic contributions to trait variation is crucial for enhancing carbon storage through the genetic improvement of forest trees. Light detection and ranging (LiDAR) has been used to estimate DBH and tree height; however, few studies have explored the heritability of these traits or assessed the accuracy of biomass increment selection based on them. Therefore, this study aimed to leverage LiDAR to measure DBH and tree height, estimate tree heritability, and evaluate the accuracy of timber volume selection based on these traits, using 60-year-old larch as the study material. Unmanned aerial vehicle laser scanning (ULS) and backpack laser scanning (BLS) were compared against hand-measured values. The accuracy of DBH estimations using BLS resulted in a root mean square error (RMSE) of 2.7 cm and a coefficient of determination of 0.67. Conversely, the accuracy achieved with ULS was 4.0 cm in RMSE and a 0.24 coefficient of determination. The heritability of DBH was higher with BLS than with ULS and even exceeded that of hand measurements. Comparisons of timber volume selection accuracy based on the measured traits demonstrated comparable performance between BLS and ULS. These findings underscore the potential of using LiDAR remote sensing to quantitatively measure forest tree biomass and facilitate their genetic improvement of carbon-sequestration ability based on these measurements. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

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20 pages, 6877 KiB  

Open AccessArticle

Improved Prototypical Network Model for Classification of Farmland Shelterbelt Using Sentinel-2 Imagery

by Yueting Wang, Qiangzi Li, Hongyan Wang, Yuan Zhang, Xin Du, Yunqi Shen and Yong Dong

Forests 2024, 15(11), 1995; https://doi.org/10.3390/f15111995 - 12 Nov 2024

Viewed by 870

Abstract

Farmland shelterbelt plays an important role in protecting farmland and ensuring stable crop yields, and it is mainly distributed in the form of bands and patches; different forms of distribution have different impacts on farmland, which is an important factor affecting crop yields. [...] Read more.

Farmland shelterbelt plays an important role in protecting farmland and ensuring stable crop yields, and it is mainly distributed in the form of bands and patches; different forms of distribution have different impacts on farmland, which is an important factor affecting crop yields. Therefore, high-precision classification of banded and patch farmland shelterbelt is a prerequisite for analyzing its impact on crop yield. In this study, we explored the effectiveness and transferability of an improved Prototypical Network model incorporating data augmentation and a convolutional block attention module for extracting banded and patch farmland shelterbelt in Northeast China, and we analyzed the potential of applying it to the production of large-scale farmland shelterbelt products. Firstly, we classified banded and patch farmland shelterbelt under different sample window sizes using the improved Prototypical Network in the source domain study area to obtain the optimal sample window size and the optimal classification model. Secondly, fine-tuning transfer learning and learning from scratch directly were used to classify the banded and patch farmland shelterbelt in the target domain study area, respectively, to evaluate the extraction model’s migratability. The results showed that classification of farmland shelterbelt using the improved Prototypical Network is very effective, with the highest extraction accuracy under the 5 × 5 sample window; the accuracies of the banded and patch farmland shelterbelt are 92.16% and 90.91%, respectively. Using the fine-tuning transfer learning method in the target domain can classify the banded and patch farmland shelterbelt with high accuracy, above 95% and 89%, respectively. The proposed approach can provide new insight into farmland shelterbelt classification and farmland shelterbelt products obtained from freely accessible Sentinel-2 multispectral images. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

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22 pages, 2472 KiB  

Open AccessArticle

DASR-Net: Land Cover Classification Methods for Hybrid Multiattention Multispectral High Spectral Resolution Remote Sensing Imagery

by Xuyang Li, Xiangsuo Fan, Jinlong Fan, Qi Li, Yuan Gao and Xueqiang Zhao

Forests 2024, 15(10), 1826; https://doi.org/10.3390/f15101826 - 19 Oct 2024

Cited by 1 | Viewed by 1583

Abstract

The prompt acquisition of precise land cover categorization data is indispensable for the strategic development of contemporary farming practices, especially within the realm of forestry oversight and preservation. Forests are complex ecosystems that require precise monitoring to assess their health, biodiversity, and response [...] Read more.

The prompt acquisition of precise land cover categorization data is indispensable for the strategic development of contemporary farming practices, especially within the realm of forestry oversight and preservation. Forests are complex ecosystems that require precise monitoring to assess their health, biodiversity, and response to environmental changes. The existing methods for classifying remotely sensed imagery often encounter challenges due to the intricate spacing of feature classes, intraclass diversity, and interclass similarity, which can lead to weak perceptual ability, insufficient feature expression, and a lack of distinction when classifying forested areas at various scales. In this study, we introduce the DASR-Net algorithm, which integrates a dual attention network (DAN) in parallel with the Residual Network (ResNet) to enhance land cover classification, specifically focusing on improving the classification of forested regions. The dual attention mechanism within DASR-Net is designed to address the complexities inherent in forested landscapes by effectively capturing multiscale semantic information. This is achieved through multiscale null attention, which allows for the detailed examination of forest structures across different scales, and channel attention, which assigns weights to each channel to enhance feature expression using an improved BSE-ResNet bilinear approach. The two-channel parallel architecture of DASR-Net is particularly adept at resolving structural differences within forested areas, thereby avoiding information loss and the excessive fusion of features that can occur with traditional methods. This results in a more discriminative classification of remote sensing imagery, which is essential for accurate forest monitoring and management. To assess the efficacy of DASR-Net, we carried out tests with 10m Sentinel-2 multispectral remote sensing images over the Heshan District, which is renowned for its varied forestry. The findings reveal that the DASR-Net algorithm attains an accuracy rate of 96.36%, outperforming classical neural network models and the transformer (ViT) model. This demonstrates the scientific robustness and promise of the DASR-Net model in assisting with automatic object recognition for precise forest classification. Furthermore, we emphasize the relevance of our proposed model to hyperspectral datasets, which are frequently utilized in agricultural and forest classification tasks. DASR-Net’s enhanced feature extraction and classification capabilities are particularly advantageous for hyperspectral data, where the rich spectral information can be effectively harnessed to differentiate between various forest types and conditions. By doing so, DASR-Net contributes to advancing remote sensing applications in forest monitoring, supporting sustainable forestry practices and environmental conservation efforts. The findings of this study have significant practical implications for urban forestry management. The DASR-Net algorithm can enhance the accuracy of forest cover classification, aiding urban planners in better understanding and monitoring the status of urban forests. This, in turn, facilitates the development of effective forest conservation and restoration strategies, promoting the sustainable development of the urban ecological environment. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

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23 pages, 15886 KiB  

Open AccessReview

Key Technologies for Autonomous Fruit- and Vegetable-Picking Robots: A Review

by Zhiqiang Chen, Xiaohui Lei, Quanchun Yuan, Yannan Qi, Zhengbao Ma, Shicheng Qian and Xiaolan Lyu

Agronomy 2024, 14(10), 2233; https://doi.org/10.3390/agronomy14102233 - 27 Sep 2024

Cited by 5 | Viewed by 3715

Abstract

With the rapid pace of urbanization, a significant number of rural laborers are migrating to cities, leading to a severe shortage of agricultural labor. Consequently, the modernization of agriculture has become a priority. Autonomous picking robots represent a crucial component of agricultural technological [...] Read more.

With the rapid pace of urbanization, a significant number of rural laborers are migrating to cities, leading to a severe shortage of agricultural labor. Consequently, the modernization of agriculture has become a priority. Autonomous picking robots represent a crucial component of agricultural technological innovation, and their development drives progress across the entire agricultural sector. This paper reviews the current state of research on fruit- and vegetable-picking robots, focusing on key aspects such as the vision system sensors, target detection, localization, and the design of end-effectors. Commonly used target recognition algorithms, including image segmentation and deep learning-based neural networks, are introduced. The challenges of target recognition and localization in complex environments, such as those caused by branch and leaf obstruction, fruit overlap, and oscillation in natural settings, are analyzed. Additionally, the characteristics of the three main types of end-effectors—clamping, suction, and cutting—are discussed, along with an analysis of the advantages and disadvantages of each design. The limitations of current agricultural picking robots are summarized, taking into account the complexity of operation, research and development costs, as well as the efficiency and speed of picking. Finally, the paper offers a perspective on the future of picking robots, addressing aspects such as environmental adaptability, functional diversity, innovation and technological convergence, as well as policy and farm management. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

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19 pages, 12489 KiB  

Open AccessArticle

Assessing the Potential of UAV for Large-Scale Fractional Vegetation Cover Mapping with Satellite Data and Machine Learning

by Xunlong Chen, Yiming Sun, Xinyue Qin, Jianwei Cai, Minghui Cai, Xiaolong Hou, Kaijie Yang and Houxi Zhang

Remote Sens. 2024, 16(19), 3587; https://doi.org/10.3390/rs16193587 - 26 Sep 2024

Cited by 3 | Viewed by 1470

Abstract

Fractional vegetation cover (FVC) is an essential metric for valuating ecosystem health and soil erosion. Traditional ground-measuring methods are inadequate for large-scale FVC monitoring, while remote sensing-based estimation approaches face issues such as spatial scale discrepancies between ground truth data and image pixels, [...] Read more.

Fractional vegetation cover (FVC) is an essential metric for valuating ecosystem health and soil erosion. Traditional ground-measuring methods are inadequate for large-scale FVC monitoring, while remote sensing-based estimation approaches face issues such as spatial scale discrepancies between ground truth data and image pixels, as well as limited sample representativeness. This study proposes a method for FVC estimation integrating uncrewed aerial vehicle (UAV) and satellite imagery using machine learning (ML) models. First, we assess the vegetation extraction performance of three classification methods (OBIA-RF, threshold, and K-means) under UAV imagery. The optimal method is then selected for binary classification and aggregated to generate high-accuracy FVC reference data matching the spatial resolutions of different satellite images. Subsequently, we construct FVC estimation models using four ML algorithms (KNN, MLP, RF, and XGBoost) and utilize the SHapley Additive exPlanation (SHAP) method to assess the impact of spectral features and vegetation indices (VIs) on model predictions. Finally, the best model is used to map FVC in the study region. Our results indicate that the OBIA-RF method effectively extract vegetation information from UAV images, achieving an average precision and recall of 0.906 and 0.929, respectively. This method effectively generates high-accuracy FVC reference data. With the improvement in the spatial resolution of satellite images, the variability of FVC data decreases and spatial continuity increases. The RF model outperforms others in FVC estimation at 10 m and 20 m resolutions, with R² values of 0.827 and 0.929, respectively. Conversely, the XGBoost model achieves the highest accuracy at a 30 m resolution, with an R² of 0.847. This study also found that FVC was significantly related to a number of satellite image VIs (including red edge and near-infrared bands), and this correlation was enhanced in coarser resolution images. The method proposed in this study effectively addresses the shortcomings of conventional FVC estimation methods, improves the accuracy of FVC monitoring in soil erosion areas, and serves as a reference for large-scale ecological environment monitoring using UAV technology. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

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21 pages, 15871 KiB  

Open AccessArticle

Tracking Forest Disturbance in Northeast China’s Cold-Temperate Forests Using a Temporal Sequence of Landsat Data

by Yueting Wang, Xiang Jia, Xiaoli Zhang, Lingting Lei, Guoqi Chai, Zongqi Yao, Shike Qiu, Jun Du, Jingxu Wang, Zheng Wang and Ran Wang

Remote Sens. 2024, 16(17), 3238; https://doi.org/10.3390/rs16173238 - 1 Sep 2024

Cited by 5 | Viewed by 1747

Abstract

Cold-temperate forests (CTFs) are not only an important source of wood but also provide significant carbon storage in China. However, under the increasing pressure of human activities and climate change, CTFs are experiencing severe disturbances, such as logging, fires, and pest infestations, leading [...] Read more.

Cold-temperate forests (CTFs) are not only an important source of wood but also provide significant carbon storage in China. However, under the increasing pressure of human activities and climate change, CTFs are experiencing severe disturbances, such as logging, fires, and pest infestations, leading to evident degradation trends. Though these disturbances impact both regional and global carbon budgets and their assessments, the disturbance patterns in CTFs in northern China remain poorly understood. In this paper, the Genhe forest area, which is a typical CTF region located in the Inner Mongolia Autonomous Region, Northeast China (with an area of about 2.001 × 10⁴ km²), was selected as the study area. Based on Landsat historical archived data on the Google Earth Engine (GEE) platform, we used the continuous change detection and classification (CCDC) algorithm and considered seasonal features to detect forest disturbances over nearly 30 years. First, we created six inter-annual time series seasonal vegetation index datasets to map forest coverage using the maximum between-class variance algorithm (OTSU). Second, we used the CCDC algorithm to extract disturbance information. Finally, by using the ECMWF climate reanalysis dataset, MODIS C6, the snow phenology dataset, and forestry department records, we evaluated how disturbances relate to climate and human activities. The results showed that the disturbance map generated using summer (June–August) imagery and the enhanced vegetation index (EVI) had the highest overall accuracy (88%). Forests have been disturbed to the extent of 12.65% (2137.31 km²) over the last 30 years, and the disturbed area generally showed a trend toward reduction, especially after commercial logging activities were banned in 2015. However, there was an unusual increase in the number of disturbed areas in 2002 and 2003 due to large fires. The monitoring of potential widespread forest disturbance due to extreme drought and fire events in the context of climate change should be strengthened in the future, and preventive and salvage measures should be taken in a timely manner. Our results demonstrate that CTF disturbance can be robustly mapped by using the CCDC algorithm based on Landsat time series seasonal imagery in areas with complex meteorological conditions and spatial heterogeneity, which is essential for understanding forest change processes. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

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25 pages, 1821 KiB  

Open AccessArticle

Assessing Agri-Food Waste Valorization Challenges and Solutions Considering Smart Technologies: An Integrated Fermatean Fuzzy Multi-Criteria Decision-Making Approach

by Qing Zhang and Hongjuan Zhang

Sustainability 2024, 16(14), 6169; https://doi.org/10.3390/su16146169 - 18 Jul 2024

Cited by 6 | Viewed by 2418

Abstract

With the growth of the worldwide population and depletion of natural resources, the sustainable development of food systems cannot be ignored. The demand for agri-food waste valorization practices like high-value compounds production has received widespread attention; however, numerous challenges still exist. The present [...] Read more.

With the growth of the worldwide population and depletion of natural resources, the sustainable development of food systems cannot be ignored. The demand for agri-food waste valorization practices like high-value compounds production has received widespread attention; however, numerous challenges still exist. The present study aims to identify those challenges of agri-food waste valorization and propose effective solutions based on smart technologies. Based on a systematic review of the literature, the study combs existing challenges of agri-food waste valorization and constructs a six-dimension conceptual model of agri-food waste valorization challenges. Moreover, the study integrates a Fermatean fuzzy set (FFS) with multi-criteria decision-making (MCDM) methods including stepwise weight assessment ratio analysis (SWARA), decision-making trial and evaluation laboratory-interpretative structural modeling method (DEMATEL-ISM), and quality function deployment (QFD) to evaluate the weights of each dimension, find causal interrelationships among the challenges and fundamental ones, and rank the potential smart solutions. Finally, the results indicate that the “Government” dimension is the severest challenge and point out five primary challenges in agri-food waste valorization. The most potential smart solution is the “Facilitating connectivity and information sharing between supply chain members (S8)”, which may help government and related practitioners manage agri-food waste efficiently and also facilitate circular economy. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

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14 pages, 2128 KiB  

Open AccessArticle

Study on Utilizing Mask R-CNN for Phenotypic Estimation of Lettuce’s Growth Status and Optimal Harvest Timing

by Lixin Hou, Yuxia Zhu, Ning Wei, Zeye Liu, Jixuan You, Jing Zhou and Jian Zhang

Agronomy 2024, 14(6), 1271; https://doi.org/10.3390/agronomy14061271 - 12 Jun 2024

Cited by 4 | Viewed by 1578

Abstract

Lettuce is an annual plant of the family Asteraceae. It is most often grown as a leaf vegetable, but sometimes for its stem and seeds, and its growth status and quality are evaluated based on its morphological phenotypic traits. However, traditional measurement methods [...] Read more.

Lettuce is an annual plant of the family Asteraceae. It is most often grown as a leaf vegetable, but sometimes for its stem and seeds, and its growth status and quality are evaluated based on its morphological phenotypic traits. However, traditional measurement methods are often labor-intensive and time-consuming due to manual measurements and may result in less accuracy. In this study, we proposed a new method utilizing RGB images and Mask R-Convolutional Neural Network (CNN) for estimating lettuce critical phenotypic traits. Leveraging publicly available datasets, we employed an improved Mask R-CNN model to perform a phenotypic analysis of lettuce images. This allowed us to estimate five phenotypic traits simultaneously, which include fresh weight, dry weight, plant height, canopy diameter, and leaf area. The enhanced Mask R-CNN model involved two key aspects: (1) replacing the backbone network from ResNet to RepVGG to enhance computational efficiency and performance; (2) adding phenotypic branches and constructing a multi-task regression model to achieve end-to-end estimation of lettuce phenotypic traits. Experimental results demonstrated that the present method achieved high accuracy and stable results in lettuce image segmentation, detection, and phenotypic estimation tasks, with APs for detection and segmentation being 0.8684 and 0.8803, respectively. Additionally, the R² values for the five phenotypic traits are 0.96, 0.9596, 0.9329, 0.9136, and 0.9592, with corresponding mean absolute percentage errors (MAPEs) of 0.1072, 0.1522, 0.0757, 0.0548, and 0.0899, respectively. This study presents a novel technical advancement based on digital knowledge for phenotypic analysis and evaluation of lettuce quality, which could lay the foundation for artificial intelligence expiation in fresh vegetable production. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

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18 pages, 9341 KiB  

Open AccessArticle

Evaluation of Soybean Drought Tolerance Using Multimodal Data from an Unmanned Aerial Vehicle and Machine Learning

by Heng Liang, Yonggang Zhou, Yuwei Lu, Shuangkang Pei, Dong Xu, Zhen Lu, Wenbo Yao, Qian Liu, Lejun Yu and Haiyan Li

Remote Sens. 2024, 16(11), 2043; https://doi.org/10.3390/rs16112043 - 6 Jun 2024

Cited by 1 | Viewed by 2305

Abstract

Drought stress is a significant factor affecting soybean growth and yield. A lack of suitable high-throughput phenotyping techniques hinders the drought tolerance evaluation of multi-genotype samples. A method for evaluating drought tolerance in soybeans is proposed based on multimodal remote sensing data from [...] Read more.

Drought stress is a significant factor affecting soybean growth and yield. A lack of suitable high-throughput phenotyping techniques hinders the drought tolerance evaluation of multi-genotype samples. A method for evaluating drought tolerance in soybeans is proposed based on multimodal remote sensing data from an unmanned aerial vehicle (UAV) and machine learning. Hundreds of soybean genotypes were repeatedly planted under well water (WW) and drought stress (DS) in different years and locations (Jiyang and Yazhou, Sanya, China), and UAV multimodal data were obtained in multiple fertility stages. Notably, data from Yazhou were repeatedly obtained during five significant fertility stages, which were selected based on days after sowing. The geometric mean productivity (GMP) index was selected to evaluate the drought tolerance of soybeans. Compared with the results of manual measurement after harvesting, support vector regression (SVR) provided better results (N = 356, R² = 0.75, RMSE = 29.84 g/m²). The model was also migrated to the Jiyang dataset (N = 427, R² = 0.68, RMSE = 15.36 g/m²). Soybean varieties were categorized into five Drought Injury Scores (DISs) based on the manually measured GMP. Compared with the results of the manual DIS, the accuracy of the predicted DIS gradually increased with the soybean growth period, reaching a maximum of 77.12% at maturity. This study proposes a UAV-based method for the rapid high-throughput evaluation of drought tolerance in multi-genotype soybean at multiple fertility stages, which provides a new method for the early judgment of drought tolerance in individual varieties, improving the efficiency of soybean breeding, and has the potential to be extended to other crops. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

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20 pages, 37741 KiB  

Open AccessArticle

SA-Pmnet: Utilizing Close-Range Photogrammetry Combined with Image Enhancement and Self-Attention Mechanisms for 3D Reconstruction of Forests

by Xuanhao Yan, Guoqi Chai, Xinyi Han, Lingting Lei, Geng Wang, Xiang Jia and Xiaoli Zhang

Remote Sens. 2024, 16(2), 416; https://doi.org/10.3390/rs16020416 - 21 Jan 2024

Cited by 6 | Viewed by 2635

Abstract

Efficient and precise forest surveys are crucial for in-depth understanding of the present state of forest resources and conducting scientific forest management. Close-range photogrammetry (CRP) technology enables the convenient and fast collection of highly overlapping sequential images, facilitating the reconstruction of 3D models [...] Read more.

Efficient and precise forest surveys are crucial for in-depth understanding of the present state of forest resources and conducting scientific forest management. Close-range photogrammetry (CRP) technology enables the convenient and fast collection of highly overlapping sequential images, facilitating the reconstruction of 3D models of forest scenes, which significantly improves the efficiency of forest surveys and holds great potential for forestry visualization management. However, in practical forestry applications, CRP technology still presents challenges, such as low image quality and low reconstruction rates when dealing with complex undergrowth vegetation or forest terrain scenes. In this study, we utilized an iPad Pro device equipped with high-resolution cameras to collect sequential images of four plots in Gaofeng Forest Farm in Guangxi and Genhe Nature Reserve in Inner Mongolia, China. First, we compared the image enhancement effects of two algorithms: histogram equalization (HE) and median–Gaussian filtering (MG). Then, we proposed a deep learning network model called SA-Pmnet based on self-attention mechanisms for 3D reconstruction of forest scenes. The performance of the SA-Pmnet model was compared with that of the traditional SfM+MVS algorithm and the Patchmatchnet network model. The results show that histogram equalization significantly increases the number of matched feature points in the images and improves the uneven distribution of lighting. The deep learning networks demonstrate better performance in complex environmental forest scenes. The SA-Pmnet network, which employs self-attention mechanisms, improves the 3D reconstruction rate in the four plots to 94%, 92%, 94%, and 96% by capturing more details and achieves higher extraction accuracy of diameter at breast height (DBH) with values of 91.8%, 94.1%, 94.7%, and 91.2% respectively. These findings demonstrate the potential of combining of the image enhancement algorithm with deep learning models based on self-attention mechanisms for 3D reconstruction of forests, providing effective support for forest resource surveys and visualization management. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

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19 pages, 5108 KiB  

Open AccessArticle

Individual Tree Species Identification and Crown Parameters Extraction Based on Mask R-CNN: Assessing the Applicability of Unmanned Aerial Vehicle Optical Images

by Zongqi Yao, Guoqi Chai, Lingting Lei, Xiang Jia and Xiaoli Zhang

Remote Sens. 2023, 15(21), 5164; https://doi.org/10.3390/rs15215164 - 29 Oct 2023

Cited by 4 | Viewed by 2427

Abstract

Automatic, efficient, and accurate individual tree species identification and crown parameters extraction is of great significance for biodiversity conservation and ecosystem function assessment. UAV multispectral data have the advantage of low cost and easy access, and hyperspectral data can finely characterize spatial and [...] Read more.

Automatic, efficient, and accurate individual tree species identification and crown parameters extraction is of great significance for biodiversity conservation and ecosystem function assessment. UAV multispectral data have the advantage of low cost and easy access, and hyperspectral data can finely characterize spatial and spectral features. As such, they have attracted extensive attention in the field of forest resource investigation, but their applicability for end-to-end individual tree species identification is unclear. Based on the Mask R-CNN instance segmentation model, this study utilized UAV hyperspectral images to generate spectral thinning data, spectral dimensionality reduction data, and simulated multispectral data, thereby evaluating the importance of high-resolution spectral information, the effectiveness of PCA dimensionality reduction processing of hyperspectral data, and the feasibility of multispectral data for individual tree identification. The results showed that the individual tree species identification accuracy of spectral thinning data was positively correlated with the number of bands, and full-band hyperspectral data were better than other hyperspectral thinning data and PCA dimensionality reduction data, with Precision, Recall, and F1-score of 0.785, 0.825, and 0.802, respectively. The simulated multispectral data are also effective in identifying individual tree species, among which the best result is realized through the combination of Green, Red, and NIR bands, with Precision, Recall, and F1-score of 0.797, 0.836, and 0.814, respectively. Furthermore, by using Green–Red–NIR data as input, the tree crown area and width are predicted with an RMSE of 3.16m² and 0.51m, respectively, along with an rRMSE of 0.26 and 0.12. This study indicates that the Mask R-CNN model with UAV optical images is a novel solution for identifying individual tree species and extracting crown parameters, which can provide practical technical support for sustainable forest management and ecological diversity monitoring. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

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18 pages, 9604 KiB  

Open AccessArticle

Integrating Agricultural and Ecotourism Development: A Crop Cultivation Suitability Framework Considering Tourists’ Landscape Preferences in Qinghai Province, China

by Huihui Wang, Jinyan Zhan, Chao Wang, Oleg Anatolyevich Blinov, Michael Asiedu Kumi, Wei Liu, Xi Chu, Yanmin Teng, Huizi Liu, Zheng Yang and Chunyue Bai

Remote Sens. 2023, 15(19), 4685; https://doi.org/10.3390/rs15194685 - 25 Sep 2023

Cited by 5 | Viewed by 3352

Abstract

Ecotourism and agricultural development have been proven to have synergistic effects, although few studies have employed a spatial planning approach to incorporate tourism growth into crop cultivation planning. This study constructed a theoretical framework of environmental suitability—farmland accessibility—tourist’s landscape preferences for crop cultivation [...] Read more.

Ecotourism and agricultural development have been proven to have synergistic effects, although few studies have employed a spatial planning approach to incorporate tourism growth into crop cultivation planning. This study constructed a theoretical framework of environmental suitability—farmland accessibility—tourist’s landscape preferences for crop cultivation planning to link regional agriculture and ecotourism development. The spatial planning of rapeseed cultivation in Qinghai Province was chosen as a case study. The main research methods include an environmental suitability analysis based on remote sensing and Maxent modeling, a farmland accessibility analysis based on a GIS platform, and a landscape preference questionnaire survey of tourists. According to the survey’s findings, almost 80% of tourists thought rapeseed flowers enhanced the beauty of natural landscapes. This demonstrated the enormous potential of rapeseed fields for fostering ecotourism. Based on environmental factors, the optimum region for rapeseed cultivation covered 5.38% of the study area, or roughly 6327 km². The comprehensive optimum zone, which encompassed both agricultural accessibility and environmental suitability, was equal to 12.63% of the study area’s farming area, or around 929 km². This study’s crop cultivation suitability framework can integrate agricultural and ecotourism development, with substantial implications for achieving coordinated economic, social, and environmental development. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

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23 pages, 3329 KiB  

Open AccessArticle

The Impact of Digital Economy Empowerment on Green Total Factor Productivity in Forestry

by Hanting Chen, Zhuoya Ma, Hui Xiao, Jing Li and Wenhui Chen

Forests 2023, 14(9), 1729; https://doi.org/10.3390/f14091729 - 27 Aug 2023

Cited by 17 | Viewed by 2412

Abstract

The digital economy is an important engine for promoting green economic development, and the integration of the digital and real economies can accelerate the transformation of the real economy. In order to explore the multifaceted influence of digital economy on forestry green total [...] Read more.

The digital economy is an important engine for promoting green economic development, and the integration of the digital and real economies can accelerate the transformation of the real economy. In order to explore the multifaceted influence of digital economy on forestry green total factor productivity and its specific presentation form, based on the panel data of 277 cities in China from 2013 to 2019, this paper first used the super SBM model to measure the level of forestry green total factor productivity and adopted the entropy method to measure the level of the digital economy in each region. Secondly, the influence and mechanism of the digital economy on green total factor productivity in forestry were explored by using fixed-effect and intermediate-effect models, and the heterogeneity of the digital economy on forestry green total factor productivity was analyzed based on different regional classification methods. Finally, the spatial spillover effect of the digital economy was explored in depth by the spatial Durbin model. The results are as follows: firstly, there is a significant inverted U-shaped relationship between the digital economy and forestry green total factor productivity, which first promotes and then inhibits. Secondly, the relationship between the digital economy and the level of urban green innovation shows a positive U-shaped relationship, first inhibiting and then promoting, and can have an indirect impact on forestry green total factor productivity by promoting the level of green innovation. Third, China is still on the left side of the inverted U-shaped relationship between the digital economy and forestry green total factor productivity, i.e., it is at a stage where the digital economy can significantly contribute to forestry green total factor productivity. Fourth, the effect of the digital economy on green total factor productivity in forestry is heterogeneous in the east, central, and west and is more pronounced in regions with faster economic development or rich natural resources. Fifth, the impact of the digital economy on forestry green total factor productivity has a significant positive spatial spillover effect. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

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24 pages, 8664 KiB  

Open AccessArticle

Seasonal Spatiotemporal Changes in the NDVI and Its Driving Forces in Wuliangsu Lake Basin, Northern China from 1990 to 2020

by Caixia Li, Xiang Jia, Ruoning Zhu, Xiaoli Mei, Dong Wang and Xiaoli Zhang

Remote Sens. 2023, 15(12), 2965; https://doi.org/10.3390/rs15122965 - 7 Jun 2023

Cited by 13 | Viewed by 2956

Abstract

In the context of global climate change, many studies have focused on the interannual vegetation variation trends and their response to precipitation and temperature, but ignored the effects of seasonal variability. This study explored the relationship between normalized difference vegetation index (NDVI) and [...] Read more.

In the context of global climate change, many studies have focused on the interannual vegetation variation trends and their response to precipitation and temperature, but ignored the effects of seasonal variability. This study explored the relationship between normalized difference vegetation index (NDVI) and seasonal climate elements in the Wuliangsu Lake Basin area from 1990 to 2020, and quantified the impacts of human activities on vegetation dynamics. We used Landsat series data to analyze the spatial and temporal variation of the NDVI using the trend analysis method, the Theil–Sen median, the Mann–Kendall test, and the Hurst index. Then, we used meteorological data and land use data to quantify the effects of human activities using residual analysis, and correlation methods to determine the driving forces of NDVI variations. The results showed that the NDVI changes presented obvious regional characteristics, with a decreasing trend from southeast to northwest in Wuliangsu Lake Basin. Due to global warming, the start of the growing season (SOS) is 4.3 days (2001 to 2010) and 6.8 days (2011 to 2020) earlier compared with 1990 to 2000. The end of the season (EOS) is advanced by 3.6 days (2001 to 2010), and delayed by 8.9 days (2011 to 2020). Seasonal (spring, summer, autumn, and winter) NDVIs with precipitation and temperature show spatial heterogeneity. Further, changes in grasslands and woodlands were vulnerable to climate change and human activities. Since the beginning of the 21st century, human activity was the driving force for vegetation improvement in the Dengkou, west-central, north and southwest regions, where ecological instability is weak. This finding can provide a theoretical basis for the implementation of the same type of ecological restoration projects and the construction of ecological civilization, and contribute to the regional green and sustainable development. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

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18 pages, 1993 KiB  

Open AccessArticle

Assessing Forest Quality through Forest Growth Potential, an Index Based on Improved CatBoost Machine Learning

by Lianjun Cao, Xiaobing He, Sheng Chen and Luming Fang

Sustainability 2023, 15(11), 8888; https://doi.org/10.3390/su15118888 - 31 May 2023

Cited by 6 | Viewed by 2995

Abstract

Human activities have always depended on nature, and forests are an important part of this; the determination and improvement of forest quality is therefore highly significant. Currently, domestic and foreign research on forest quality focuses on the current states of forests. We propose [...] Read more.

Human activities have always depended on nature, and forests are an important part of this; the determination and improvement of forest quality is therefore highly significant. Currently, domestic and foreign research on forest quality focuses on the current states of forests. We propose a new research direction based on the future states. By referencing and analyzing the forest quality standards of domestic and foreign experts and institutions, the concept and model for calculating forest growth potential were constructed. Forest growth potential is a new forest quality indicator. Based on the data of 110,000 subcompartments of forest resources from the Lin’an and Landsat8 satellites’ remote sensing data, the unit volume was predicted using three machine-learning algorithms: random gradient descent SGD, the integrated machine learning algorithm CatBoost, and deep learning CNN. The CatBoost algorithm model was improved based on Optuna; then the improved CatBoost algorithm was selected through evaluation indicators for the prediction of forest volume and finally incorporated into the calculation model for forest growth-potential value. The forest growth-potential value was calculated, and an accurate forest quality improvement scheme based on the subcompartments is preliminarily discussed. The successful calculation of forest growth potential values has a certain reference significance, providing guidance for accurately improving forest quality and forest management. The improved CatBoost calculation model is effective in the prediction of forest growth potential, and the determination coefficient R² reaches 0.89, a value that compares favorably with those in other studies. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

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22 pages, 4726 KiB  

Open AccessArticle

Spatiotemporal Distribution and Influencing Factors of Coupling Coordination between Digital Village and Green and High-Quality Agricultural Development—Evidence from China

by Heng Wang and Yuting Tang

Sustainability 2023, 15(10), 8079; https://doi.org/10.3390/su15108079 - 16 May 2023

Cited by 20 | Viewed by 2705

Abstract

With digital technologies injecting a strong impetus into China’s sustainable agricultural development, the digital village is a new path for Chinese agricultural development. This paper focuses on digital village and sustainable agricultural development, in which the situation of digital village and green and [...] Read more.

With digital technologies injecting a strong impetus into China’s sustainable agricultural development, the digital village is a new path for Chinese agricultural development. This paper focuses on digital village and sustainable agricultural development, in which the situation of digital village and green and high-quality agricultural development in China from 2010 to 2019 is measured based on the entropy weight–TOPSIS method and coupling coordination model and further explores the spatiotemporal evolution of their coupling coordination. In addition, it studies the factors which influence the coupling coordination of digital village and green and high-quality agricultural development using a geographical detector. The study shows that both digital villages and green and high-quality agricultural development in China show good momentum. In terms of the spatial pattern, cities on the southeast coast witness better development of digital villages, and the southern regions enjoy a higher degree of green and high-quality development in agriculture. The coupling coordination between digital villages and green and high-quality agricultural development shows a fluctuating upward trend in the eastern regions. Some of the influence factors play a significant role in the coupling coordination of digital villages and green and high-quality agricultural development, such as e-commerce, per capita income, innovation, and levels of income and education. On this basis, we suggest that the government should continuously promote the development of digital villages and improve rural governance to bridge the digital divide. In this case, policies to promote green and high-quality agricultural development through digitalization can be introduced according to local conditions, thus enabling sustainable agricultural development with the empowerment of digitalization. Full article

(This article belongs to the Topic Challenges, Development and Frontiers of Smart Agriculture and Forestry—2nd Volume)

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