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The realization of the value of ecological products has led to an economic means for reducing carbon emissions in China. Tea is one of the most important cash crops and one of the most popular beverages in the world. Due to the complex the tea industrial chain, it is considered to be an industry with high carbon emissions. Ecological tea products with low-carbon attributes can be considered a linkage of ecology, economy, and society. Based on this, this paper presents research on low-carbon ecological tea (LCT). Herein, we construct the formational logic of low-carbon ecological products, explore the connotations of LCT, and form a conceptual pathway for realizing LCT to contribute to climate change mitigation and adaptation. This paper starts from the upstream, midstream, and downstream of the industrial chain; it establishes three value realization pathways that keep, as a priority, the promotion of ecological industrialization, focus on restoration to improve the ecology of the industrial chain, and innovate technology to expand the industrial chain. The pathways are a set of low-emission production solutions that use techniques to enhance carbon sequestration in soil, reduce the use of fertilizers and pesticides, and help shift to clean energy from low-emission sources in the stages of plantation, processing, and distribution. In the process of realizing LCT, the government plays an important role, and its support and guidance are needed. Based on stakeholder theory, this paper builds an implementation mechanism that focuses on the micro perspective (users, organizations), integrates the mesoscopic perspective (industry), and relies on the macro perspective (government). Full article

The oxidative stability index (OSI) and fatty acid (FA) composition of extra virgin olive oils (EVOOs) are key parameters in the characterization of new varieties in breeding programs. Their determination through traditional methods (Rancimat and gas chromatography, respectively) is expensive and time-consuming. Therefore, there is a need to develop rapid and cost-effective analytical procedures. This study aimed to evaluate the potential use of near-infrared spectroscopy (NIRS) for analyzing OSI and FA composition in EVOOs. A total of 318 samples sourced from different origins were evaluated using both FT-NIR MPA and MicroNIR instruments in transmittance mode, with wavelengths ranging from 1100 to 2500 nm and 908 to 1676 nm, respectively. Different accuracies were obtained in the models developed for the different evaluated traits, with simpler models (using a lower number of latent variables) for the MPA analyzer in all cases. Additionally, consistent results between instruments for the partitioning of the variance and heritability estimation, and the reliable ranking of genotypes were obtained from one of the sample sets tested. In summary, models derived from PLS regression using spectroscopic data of both instruments demonstrated promising results in determining these EVOO traits, facilitating their evaluation and selection of genotypes, particularly in breeding programs. Full article

In animal husbandry applications, segmenting live pigs in complex farming environments faces many challenges, such as when pigs lick railings and defecate within the acquisition environment. The pig’s behavior makes point cloud segmentation more complex because dynamic animal behaviors and environmental changes must be considered. This further requires point cloud segmentation algorithms to improve the feature capture capability. In order to tackle the challenges associated with accurately segmenting point cloud data collected in complex real-world scenarios, such as pig occlusion and posture changes, this study utilizes PointNet++. The SoftPool pooling method is employed to implement a PointNet++ model that can achieve accurate point cloud segmentation for live pigs in complex environments. Firstly, the PointNet++ model is modified to make it more suitable for pigs by adjusting its parameters related to feature extraction and sensory fields. Then, the model’s ability to capture the details of point cloud features is further improved by using SoftPool as the point cloud feature pooling method. Finally, registration, filtering, and extraction are used to preprocess the point clouds before integrating them into a dataset for manual annotation. The improved PointNet++ model’s segmentation ability was validated and redefined with the pig point cloud dataset. Through experiments, it was shown that the improved model has better learning ability across 529 pig point cloud data sets. The optimal mean Intersection over Union (mIoU) was recorded at 96.52% and the accuracy at 98.33%. This study has achieved the automatic segmentation of highly overlapping pigs and pen point clouds. This advancement enables future animal husbandry applications, such as estimating body weight and size based on 3D point clouds. Full article

Microbial communities are not only an important indicator of soil status but also a determinant of plant nutrition and health levels. Loss of microbial community ecosystem control can directly lead to microbial disease occurrence. During the process of Yuanyang Nanqi wild imitation planting, root rot diseases frequently occur, seriously affecting their yield and quality. Via amplicon sequencing, this study mainly compared the microbial community composition between the rhizosphere soil and roots of healthy and diseased Yuanyang Nanqi with root rot. The α-diversity showed that the microbial community diversity and abundance in the roots of diseased Yuanyang Nanqi were much lower than those of those in healthy specimens, while no significant difference was found in the rhizosphere soil. The β－diversity showed that the bacterial community in the Gejiu region and the fungal community in the Honghe region were significantly different from those in other regions. The species relative abundance map showed that there was no obvious difference in microbial community composition between the rhizosphere soil and roots of healthy and diseased Nanqi, but in diseased specimens with root rot, the proportions of Pseudomonas and Fusarium increased. Based on a functional prediction analysis of FUNGuild, the results showed that the Nanqi roots were mainly pathological saprophytic type and that their rhizosphere soil was mainly saprophytic type. The microorganisms in the roots of Yuanyang Nanqi tubers with root rot were also isolated and identified through the use of the culture method. The possible pathogenic strains were tested via anti－inoculation, and Fusarium oxysporum was identified as one of the main pathogenic fungi of Nanqi root rot, which was consistent with the amplicon sequencing results. These results will help us understand the change trend of microbial communities in healthy and diseased plants and analyze the pathogens involved, the pathogenesis, and the beneficial microorganisms, which would provide a theoretical basis for effective biological control. Full article

Understanding soil loss pathways in karst regions is crucial for erosion control. Combining high-density measurements of grid points with runoff plot monitoring, we attempt to use a new indirect method to study the characteristics of soil loss in karst rocky desertification areas of Salaxi Town, Guizhou province. One cycle year monitoring data of 12640 grid points were applied in the soil loss analysis. This study identifies underground leakage as the primary pathway of soil loss, with an mean soil leakage of 21.51 kg in potential areas, accounting for 83.12%, and an average leakage of 22.69 kg in in mild karst rocky desertification areas accounting for 81.48%. Mixed vegetation types (forest, shrub, and grass) were better at preventing surface soil loss but increased underground leakage compared to single vegetation types. The rainy season significantly influences soil erosion, accounting for 67.88% of total loss, with slight variations among different karst rocky desertification grades and vegetation types. Mean underground leakage rates during the rainy and dry seasons are 63.34% and 36.66%, respectively. Although this method still has certain limitations, it plays a positive role in revealing the mechanism of soil erosion processes in karst regions. Full article

In this paper, a cotton precision seed-taking dibbler device was designed to address the problems of congestion and leakage of the hole-type dibbler during high-speed operation (more than 4 km/h). Firstly, the motion trajectory of the seed in the limited gear-shaped space was analyzed and a motion model was established to analyze the relationship between the motion trajectory and seed-filling performance. Secondly, a central combination test with four factors and five levels was implemented using the discrete element software EDEM2018, which simulated the seed-filling performance of the seed-holding space with different structural dimensions. The optimal parameters impacting the seed-filling behavior of the designed dibbler were derived via response surface optimization and multiple regression analyses. Under optimal conditions, three bench tests were repeatedly conducted, and the average qualified index was 93.67%, the leakage index Y₃ was 2.67%, and the multiple index Y₂ was 3.66%, which was close to the simulation results. Finally, for the speed adaptability test of the seed-holding space with optimal structural parameters, the qualified index was more than 90% when the rotating speed ranged from 1.0 to 2.0 r/s (the speed of the corresponding dibbler was 5.4 km/h to 7.2 km/h), indicating that the dibbler could meet the requirements of high-speed operation and had good speed adaptability. The results can not only provide a reference for the development of precision hole-type dibblers but also have theoretical significance for the quantitative separation of the individual from the population of irregularly rotating agricultural materials and ore materials such as cotton seeds. Full article

The current literature considers agritourism as a valid option for promoting the development of rural areas. This would be achieved by increasing agricultural incomes. However, numerous scientific studies have revealed the difficulty in reaching a consensus on the very concept of agritourism. In addition, the definition of agritourism is rarely related to the opinion of the demand. For this reason, this research aimed to understand the idea that tourists have about this variety. To this end, more than 500 surveys were carried out, from which the tourists’ conception of agritourism and the activities it entails were deduced. Other questions were also analyzed to determine whether the conception varies between those who have already performed this type of activity and those who have not yet had the opportunity to do so. From this, we can deduct the interest that visitors have in agritourism products, evidencing their potential. This interest is deduced through the visualization of different landscapes and activities of interest to tourists. Under these four central points, the aim was to understand the aims of agritourism in Extremadura (Spain). At the methodological level, a combination of descriptive statistics and spatial statistics was used, highlighting the use of cluster analysis. The results show a significant lack of knowledge of the meaning of agritourism, especially among those who have never practiced it, and of the activities associated with it. At the same time, the selection of landscapes preferred by tourists makes it possible to establish areas with potential for the development of these activities. Likewise, knowing which activities are of interest to tourists also helps to generate complementary activities and to better target the design of agrotourism products. Full article

Heavy metal soil pollution severely threatens human health and food safety. This study used PRISMA to systematically review heavy metal-contaminated soil remediation patents in the Derwent Patent Database from 2003 to 2023. A total of 1744 patents were selected. The results of the analysis show that related patent applications are growing around the world. Among them, China has the most significant number of patents, but the layout of transnational patents needs to be revised. Countries have different preferences in transnational patent technology. Technological development is generally balanced, and there is no apparent monopoly. However, the need for continuous in-depth research on inventors is an obstacle to technological development. In addition, the technology in this field is concentrated in chemistry and engineering. Currently, the mainstream technology is soil remediation agents, and thermal desorption technology has also attracted much attention. Future technologies will use new polymer materials and advanced machinery to improve efficiency and control repair costs. In addition, remediation has shifted from the total amount of heavy metals to the control of practical parts. This study summarizes the current status of heavy metal-contaminated soil remediation technology and analyzes future development trends, providing a reference for technology development. Full article

This study focused on downy mildew in hop caused by the pathogen Pseudoperonospora humuli. A systematic literature review was conducted using bibliometric analysis to explore trends in publishing, prominent research themes, and where research is being conducted on hop downy mildew. The databases Scopus, Web of Science, and ScienceDirect were used to identify publications spanning from 1928 to 2023. The analysis yielded 54 publications, with the most cited studies primarily focusing on disease management and host resistance. Additionally, these studies explored the genetic and pathogenic relationship between P. cubensis and P. humuli. A word co-occurrence map revealed that the main themes addressed in the publications included “hop”, “disease”, “downy”, “humuli”, “mildew”, and “Pseudoperonospora”. Notably, there was a particular emphasis on subtopics such as disease management, the disease reaction of hop cultivars, and the influence of weather factors on hop downy mildew. Notably, there was limited knowledge about the disease in regions with tropical climates. This study provides valuable information that can support and guide future research endeavors concerning downy mildew in hop cultivation. Full article

The application of ecological techniques by farmers is important for ensuring the environmentally sustainable advancement of the grain sector. Based on micro-level survey data from 921 Chinese wheat growers in the Hebei and Henan provinces, this study employed an endogenous switching probit model and counterfactual analysis to investigate the impact and mechanisms of digital information utilization on ecological production technology adoption. The results indicated that 43.87% of sample wheat farmers had a low level of adoption of ecological techniques. The utilization of digital information significantly promoted farmers’ adoption. If farmers who currently used digital information were to opt-out, the probability of their high adoption would decrease by 11.26%. The utilization of digital information significantly enhanced the adoption of ecological technologies through three mediating factors: technological cognition, production monitoring, and market channels. Therefore, it is imperative to encourage farmers to broaden their social networks and enhance their perception of the importance of digital information. Additionally, it is essential to promote the industrialization and scale operation of wheat production, direct policy subsidies towards new types of management entities, and ensure the accuracy of the supply of digital information for green production through multiple channels. Therefore, it is imperative to expand farmers’ social networks and leverage rural communities to increase their perceived importance of digital information. Governments should increase subsidies and promote the scale and industrialization of wheat production. Moreover, the accuracy of digital information supply for sustainable production should be promoted through digital learning platforms, production monitoring systems, and e-commerce networks. Full article

The pricing behavior of agricultural processing firms in input markets has large impacts on farmers’ and processors’ prosperity as well as the overall market structure. Despite analytical approaches to food processors’ pricing in agricultural input markets, the need for models to represent complex market features is urgent. Agent-based models (ABMs) serve as computational laboratories to understand complex markets emerging from autonomously interacting agents. Yet, individual agents within ABMs must be equipped with intelligent learning algorithms. In this paper, we propose supervised and unsupervised learning agents to simulate the pricing behavior of firms in agricultural markets’ ABMs. Supervised learning firms are pre-trained to accurately best respond to their competitors and are deemed to result in the market Nash equilibria. Unsupervised learning firms play a course of pricing interaction with their competitors without any pre-knowledge but based on deep reinforcement learning. The simulation results show that unsupervised deep learning firms are capable of approximating the pricing equilibria obtained by the supervised firms in different spatial market settings. Optimal discriminatory and uniform delivery pricing emerges in agricultural input markets with the high and intermediary importance placed on space. Free on board pricing emerges in agricultural input markets with small importance placed on space. Full article

Maize residue cover (MRC) is an important parameter to quantify the degree of crop residue cover in the field and its spatial distribution characteristics. It is also a key indicator of conservation tillage. Rapid and accurate estimation of maize residue cover (MRC) and spatial mapping are of great significance to increasing soil organic carbon, reducing wind and water erosion, and maintaining soil and water. Currently, the estimation of maize residue cover in large areas suffers from low modeling accuracy and poor working efficiency. Therefore, how to improve the accuracy and efficiency of maize residue cover estimation has become a research hotspot. In this study, adaptive threshold segmentation (Yen) and the CatBoost algorithm are integrated and fused to construct a residue coverage estimation method based on multispectral remote sensing images. The maize planting areas in and around Sihe Town in Jilin Province, China, were selected as typical experimental regions, and the unmanned aerial vehicle (UAV) was employed to capture maize residue cover images of sample plots within the area. The Yen algorithm was applied to calculate and analyze maize residue cover. The successive projections algorithm (SPA) was used to extract spectral feature indices from Sentinel-2A multispectral images. Subsequently, the CatBoost algorithm was used to construct a maize residue cover estimation model based on spectral feature indices, thereby plotting the spatial distribution map of maize residue cover in the experimental area. The results show that the image segmentation based on the Yen algorithm outperforms traditional segmentation methods, with the highest Dice coefficient reaching 81.71%, effectively improving the accuracy of maize residue cover recognition in sample plots. By combining the spectral index calculation with the SPA algorithm, the spectral features of the images are effectively extracted, and the spectral feature indices such as NDTI and STI are determined. These indices are significantly correlated with maize residue cover. The accuracy of the maize residue cover estimation model built using the CatBoost model surpasses that of traditional machine learning models, with a maximum determination coefficient (R²) of 0.83 in the validation set. The maize residue cover estimation model constructed based on the Yen and CatBoost algorithms effectively enhances the accuracy and reliability of estimating maize residue cover in large areas using multispectral imagery, providing accurate and reliable data support and services for precision agriculture and conservation tillage. Full article

The objective of this study was to verify the genetic dissimilarity and validate image phenotyping using RGB (red, green, and blue) sensors in tropical carrot germplasms. The experiment was conducted in the city of Carandaí-MG, Brazil, using 57 tropical carrot entries from Seminis and three commercial entries. The entries were evaluated agronomically and two flights with Remotely Piloted Aircraft (RPA) were conducted. Clustering was performed to validate the existence of genetic variability among the entries using an artificial neural network to produce a Kohonen’s self-organizing map. The genotype–ideotype distance index was used to verify the best entries. Genetic variability among the tropical carrot entries was evidenced by the formation of six groups. The Brightness Index (BI), Primary Colors Hue Index (HI), Overall Hue Index (HUE), Normalized Green Red Difference Index (NGRDI), Soil Color Index (SCI), and Visible Atmospherically Resistant Index (VARI), as well as the calculated areas of marketable, unmarketable, and total roots, were correlated with agronomic characters, including leaf blight severity and root yield. This indicates that tropical carrot materials can be indirectly evaluated via remote sensing. Ten entries were selected using the genotype–ideotype distance (2, 15, 16, 22, 34, 37, 39, 51, 52, and 53), confirming the superiority of the entries. Full article

The technology of seed coating with superabsorbent polymer (SAP) has the potential to mitigate the negative impact of drought on seed germination and crop establishment. However, their application on the seed surface can affect the effectiveness of pesticides used for seed treatment in the protection against phytopathogens. In our work, the influence of the Aquaholder^®Seed polymer coating on the effectiveness of fungicides in the protection of germinating seeds of spring barley cv. Bojos and Laudis against the fungal pathogen Bipolaris sorokiniana was studied. One-half of the seeds were first treated with fungicides, and then a polymer was applied. Fungicide efficacy was evaluated in a Petri dish test and pot test under the pathogen attack. Seed coating with SAP did not negatively affect fungicide efficacy. The percentage of germinated seeds, seedling emergence, plant height, and symptoms of the disease in the fungicide-treated variants were not significantly changed by the SAP application. Moreover, in cv. Laudis, the application of SAP alone partially protected germinating seeds against pathogen attack. The amount of pathogen DNA in plant tissues of cv. Laudis was not significantly different among seed treatments, while in cv. Bojos, the pathogen DNA increased in seeds coated with SAP alone but decreased in combined treatment with fungicides. These results demonstrated that SAP seed coating does not negatively affect the efficacy of fungicides used for seed protection against fungal pathogens. Full article

The accurate and rapid identification of rice diseases is crucial for enhancing rice yields. However, this task encounters several challenges: (1) Complex background problem: The rice background in a natural environment is complex, which interferes with rice disease recognition; (2) Disease region irregularity problem: Some rice diseases exhibit irregular shapes, and their target regions are small, making them difficult to detect; (3) Classification and localization problem: Rice disease recognition employs identical features for both classification and localization tasks, thereby affecting the training effect. To address the aforementioned problems, an enhanced rice disease recognition model leveraging the improved YOLOv7-Tiny is proposed. Specifically, in order to reduce the interference of complex background, the YOLOv7-Tiny model’s backbone network has been enhanced by incorporating the Convolutional Block Attention Module (CBAM); subsequently, to address the irregularity issue in the disease region, the RepGhost bottleneck module, which is based on structural reparameterization techniques, has been introduced; Finally, to resolve the classification and localization issue, a lightweight YOLOX decoupled head has been proposed. The experimental results have demonstrated that: (1) The enhanced YOLOv7-Tiny model demonstrated elevated F1 scores and [email protected], achieving 0.894 and 0.922, respectively, on the rice pest and disease dataset. These scores exceeded the original YOLOv7-Tiny model’s performance by margins of 3.1 and 2.2 percentage points, respectively. (2) In comparison to the YOLOv3-Tiny, YOLOv4-Tiny, YOLOv5-S, YOLOX-S, and YOLOv7-Tiny models, the enhanced YOLOv7-Tiny model achieved higher F1 scores and [email protected]. The improved YOLOv7-Tiny model boasts a single image inference time of 26.4 ms, satisfying the requirement for real-time identification of rice diseases and facilitating deployment in embedded devices. Full article