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This paper presents an analysis of the flight endurance of solar-powered unmanned aerial vehicles (UAVs). Flight endurance is usually only analyzed under the operating conditions for the location where the UAV was constructed. The fact that these conditions change in a different environment of its operation has been missed. This can be disastrous for those looking to operate such a system under different geographical conditions. This work provides critical insights into the design and operation of solar-powered UAVs for various latitudes, highlighting strategies to maximize their performance and energy efficiency. This work analyzes the endurance of small UAVs designed for practical applications such as shoreline monitoring, agricultural pest detection, and search and rescue operations. The study uses TRNSYS 18 software to employ solar radiation in the power system performance at different latitudes. The results show that flight endurance is highly dependent on solar irradiance. This study confirms that the differences between low latitudes in summer and high latitudes in winter are significant, and this parameter cannot be ignored in terms of planning the use of such vehicles. The findings emphasize the importance of optimizing the balance between UAV mass, solar energy harvesting, and endurance. While the addition of battery mass can enhance endurance, the structural reinforcements required for increased weight may impose practical limitations. The scientific contribution of this work may be useful for both future designers and stakeholders in the operation of such unmanned systems. Full article

Automating agricultural machinery presents a significant opportunity to lower costs and enhance efficiency in both current and future field operations. The detection and destruction of weeds in agricultural areas via robots can be given as an example of this process. Deep learning algorithms can accurately detect weeds in agricultural fields. Additionally, robotic systems can effectively eliminate these weeds. However, the high computational demands of deep learning-based weed detection algorithms pose challenges for their use in real-time applications. This study proposes a vision-based autonomous agricultural robot that leverages the YOLOv8 model in combination with ByteTrack to achieve effective real-time weed detection. A dataset of 4126 images was used to create YOLO models, with 80% of the images designated for training, 10% for validation, and 10% for testing. Six different YOLO object detectors were trained and tested for weed detection. Among these models, YOLOv8 stands out, achieving a precision of 93.8%, a recall of 86.5%, and a [email protected] detection accuracy of 92.1%. With an object detection speed of 18 FPS and the advantages of the ByteTrack integrated object tracking algorithm, YOLOv8 was selected as the most suitable model. Additionally, the YOLOv8-ByteTrack model, developed for weed detection, was deployed on an agricultural robot with autonomous driving capabilities integrated with ROS. This system facilitates real-time weed detection and destruction, enhancing the efficiency of weed management in agricultural practices. Full article

This study provides a comparative evaluation of several ensemble model constructions for the prediction of specific methane yield (SMY) from anaerobic digestion. From the authors’ knowledge based on existing research, present knowledge of their prediction accuracy and utilization in anaerobic digestion modeling relative to individual machine learning methods is incomplete. Three input datasets from compiled anaerobic digestion samples using agricultural and forestry lignocellulosic residues from previous studies were used in this study. A total of six individual machine learning methods and five ensemble constructions were evaluated per dataset, whose prediction accuracy was assessed using a robust 10-fold cross-validation in 100 repetitions. Ensemble models outperformed individual methods in one out of three datasets in terms of prediction accuracy. They also produced notably lower coefficients of variation in root-mean-square error (RMSE) than most accurate individual methods (0.031 to 0.393 for dataset A, 0.026 to 0.272 for dataset B, and 0.021 to 0.217 for dataset AB), being much less prone to randomness in the training and test data split. The optimal ensemble constructions generally benefited from the higher number of individual methods included, as well as from their diversity in terms of prediction principles. Since the reporting of prediction accuracy based on final model fitting and the single split-sample approach is highly prone to randomness, the adoption of a cross-validation in multiple repetitions is proposed as a standard in future studies. Full article

The decrease in grain plantation areas poses a growing concern for global food security. China, with its large population, increasingly diversified food demands, and relatively small cultivated lands, has suffered deeply from this phenomenon (non-grain production, NGP) in recent years. Since 2020, the central government of China has claimed to deal with this problem by attracting agriculturalists and organizations involved in grain plantation. In this context, understanding the global NGP of the national situation is vital for policy making. Remote sensing is regarded as the most effective and accurate method for this purpose, but existing studies have mainly focused on algorithms operating at the local scale or exploring grain-producing capability from the perspective of agricultural space. As such, the characterization of NGP on a national scale remains deficient. In this study, we tried to bridge the gap through spatio-analysis with a newly published nationwide crop pattern and land use geo-datasets; the quantitative, spatial, and structural features, as well as the utilization of NGP cropland in the year 2019, were observed. The results showed that about 60% of the cropland was used for non-grain plantation. About 15% of the NGP parcels were cultivated with grains at least three times in the past 4 years, and of these 60% and 40% were parcels with double- or single-season plantation, respectively, which could result in a 16–22% increase in the grain-sown area compared with 2019. Forest and grassland were the dominant non-cropping categories which NGP cropland transferred into, indicating more time and economic cost for regaining grains. NGP parcels also presented spatio-heterogeneity regarding cropping intensity and transformation. Parcels with double-season plantation mostly emerged in northern, central, and southern provinces, while those with single-season plantation were always located in northeastern and western provinces. The parcels that were transferred into forest or grassland mainly appeared in southern and Inner Mongolia, respectively, while the parcels in northern and central areas mostly continued cropping. According to these results, we propose remediation policies focusing on raising the cropping intensity of cultivated land in central and northern provinces due to their advantages of water, heat, terrain, and land use change features. Future work is warranted based on this study’s deficiencies and uncertainties. As a forerunner, this study provides a holistic observation of the NGP phenomenon in mainland China on a national scale, and the findings can inform improvements in land use policies concerning grain production and food security in China. Full article

Nitrate pollution due to agricultural activities challenges the management of groundwater resources. The most popular technique used for groundwater vulnerability assessments is the DRASTIC. The subjectivity introduced by the DRASTIC has always been questioned. Therefore, the determination of rating scores and weights of parameters has become the main difficulty in DRASTIC applications. In this paper, a new data-driven weighting method based on Monte Carlo or genetic algorithm was developed. The new method considers both single factors and the relationship among factors, overcomes the subjectivity of weight determination, and is theoretically applicable to various hydrogeological environments and as a general weight determination method. In addition, a new method for the verification of the evaluation results on a temporal scale was established, which is based on changes in the nitrate concentration over the past 20 years. To verify and test these methods, they were used for the evaluation of groundwater vulnerability to nitrate in the plain area of the Baiyangdian watershed in the North China Plain and compared with other commonly used methods. The Pearson correlation coefficient increased by 15%. From a time perspective, the changes in nitrate concentration confirmed that the correctness of the assessment is 88%. In this study, the effect of the revision of the rating ranges on the improvement of the evaluation results is very obvious. Therefore, the focus of future work should be on determining the rating ranges and their rating scores, and whether the corresponding weights based on the data-driven method will yield more reliable results. Full article

Smart agricultural machinery is built upon traditional agricultural equipment, further integrating modern information technologies to achieve automation, precision, and intelligence in agricultural production. Currently, significant progress has been made in the autonomous operation and monitoring technologies of smart agricultural machinery in China. However, challenges remain, including poor adaptability to complex environments, high equipment costs, and issues with system implementation and standardization integration. To help industry professionals quickly understand the current state and promote the rapid development of smart agricultural machinery, this paper provides an overview of the key technologies related to autonomous operation and monitoring in China’s smart agricultural equipment. These technologies include environmental perception, positioning and navigation, autonomous operation and path planning, agricultural machinery status monitoring and fault diagnosis, and field operation monitoring. Each of these key technologies is discussed in depth with examples and analyses. On this basis, the paper analyzes the main challenges faced by the development of autonomous operation and monitoring technologies in China’s smart agricultural machinery sector. Furthermore, it explores the future directions for the development of autonomous operation and monitoring technologies in smart agricultural machinery. This research is of great importance for promoting the transition of China’s agricultural production towards automation and intelligence, improving agricultural production efficiency, and reducing reliance on human labor. Full article

The anthropogenic origin of climate change is well-documented in the scientific literature, with agriculture recognized as both a significant contributor and a sector highly vulnerable to its impacts. This dynamic creates a vicious circle, where farming activities exacerbate climate change, while farmers simultaneously bear its adverse consequences. As a result, they play a pivotal role in both mitigation and adaptation efforts. Using this as a starting point, the overarching aim of the present study is to investigate farmers’ climate change views and to indicate how farmers envisage their role, responsibilities, and possibilities to mitigate and adapt to climate change. To this end, a primary questionnaire survey was conducted based on a sample of 150 farmers in the region of Eastern Macedonia and Thrace in Greece. Principal component analysis (PCA) was conducted in order to identify the key views and attitudes of farmers towards their role and responsibilities about the impact of climate change. Additionally, clustering techniques were employed to classify farmers with similar attitudes, providing a typology regarding their behavior toward climate adaptation and mitigation issues. Lastly, a series of non-parametric statistical tests were performed to profile the identified groups of farmers and additionally to define differences among farmers’ features, agricultural holdings’ features, and cluster solution groups. The results of this process provide a comprehensive understanding of Greek farmers’ views and attitudes towards climate change. Acknowledging farmers’ views and attitudes towards climate change at the national level is crucial for the national and regional authorities in their effort to plan successful future climate policies for the agricultural sector and to ensure success in farm-scale implementation. Full article

Sorghum (Sorghum bicolor L.) is a globally important energy and food crop that is becoming increasingly integral to food security and the environment. However, its production is significantly hampered by various fungal phytopathogens that affect its yield and quality. This review aimed to provide a comprehensive overview of the major fungal phytopathogens affecting sorghum, their impact, current management strategies, and potential future directions. The major diseases covered include anthracnose, grain mold complex, charcoal rot, downy mildew, and rust, with an emphasis on their pathogenesis, symptomatology, and overall economic, social, and environmental impacts. From the initial use of fungicides to the shift to biocontrol, crop rotation, intercropping, and modern tactics of breeding resistant cultivars against mentioned diseases are discussed. In addition, this review explores the future of disease management, with a particular focus on the role of technology, including digital agriculture, predictive modeling, remote sensing, and IoT devices, in early warning, detection, and disease management. It also provide key policy recommendations to support farmers and advance research on disease management, thus emphasizing the need for increased investment in research, strengthening extension services, facilitating access to necessary inputs, and implementing effective regulatory policies. The review concluded that although fungal phytopathogens pose significant challenges, a combined effort of technology, research, innovative disease management, and effective policies can significantly mitigate these issues, enhance the resilience of sorghum production to facilitate global food security issues. Full article

Climate change significantly impacts agriculture by increasing the frequency and intensity of environmental stresses, which can severely reduce agricultural yields. Adopting sustainable practices is crucial to mitigating these risks and enhancing crop resilience. Applying natural compounds and microorganisms as biostimulants has gained popularity as an eco-friendly approach to alleviating abiotic stress in agricultural plants. This study reviews the current research on applying biostimulants in horticulturally significant crops to boost their resistance to abiotic stressors such as salinity, drought, and high temperatures. It explores the mechanisms through which these stimulants offer protection, focusing on the roles of key bioactive substances in regulating physiological and molecular processes for stress adaptation. The study addresses biostimulant formulation, regulation, and application challenges. Future research directions are suggested to harness biostimulants’ potential fully, aiming to develop climate-resilient horticultural systems that follow sustainability principles. This comprehensive review underscores the use of biostimulants as a sustainable strategy to increase crop yields in the face of climate change, reducing reliance on synthetic agrochemicals. Full article

Current ecological value assessment models predominantly emphasize the potential value of ecological resources, neglecting the crucial aspect of value realization processes. Analyzing the value of ecological resources from the perspective of ecological products (EPs) is more instructive in realizing ecological values. The key factors controlling the realization of ecological product value are potential value, ecological risk, development costs, and human demand. Previous research has rarely integrated these four factors within the ecological zoning framework. This study proposes a suitability evaluation and zoning framework for ecological product development based on the “value-risk-cost-demand” perspective. First, an evaluation index system for the potential value of ecological products was developed, dividing EPs into ecological agriculture (EA), ecological industry (EI), and ecological tourism (ET), and assessing them using 13 indicators. Ecological risks were modeled using multi-scale patch analysis (MSPA) and other models. Development costs were estimated using cost entropy. The impact of population dynamics on EP demand was quantified using population density, night-time light data, and average land GDP, along with stacked buffer analysis. Next, an improved TOPSIS method was applied to integrate these four dimensions, producing a comprehensive suitability assessment for EP development. Finally, EP zoning was determined by overlaying the comprehensive evaluation results. This framework was used to identify the dominant mode zones of EPs within the region of Jintan District, Jiangsu Province, China. The findings suggest that the integrated assessment model proposed in this study has produced more reasonable outcomes in terms of spatial layout, land use area, reduction of fragmentation and ecological risk. This conclusion is supported by spatial distribution comparisons, optimal area deviation analyses, landscape index calculations and multi-model driven future simulations. This model effectively resolves the spatial mismatch present in the traditional approach, which solely focuses on the potential value of EPs. This study can be applied to other regions with developed economies and rich ecological resources, providing an effective reference for the choice of paths to realize the value of EPs. Full article

Straw incorporation (SI) is a key strategy for promoting sustainable agriculture. It aims to mitigate environmental pollution caused by straw burning and enhances soil organic matter content, which increases crop yields. Consequently, the accurate and efficient monitoring of SI is crucial for promoting sustainable agricultural practices and effective management. In this study, we employed the Google Earth Engine (GEE) to analyze time-series Sentinel-2 data with the Mann–Kendall (MK) algorithm. This approach enabled the extraction and spatial distribution retrieval of SI regions in a representative household mode area in Northeast China. Among the eight tillage indices analyzed, the simple tillage index (STI) exhibited the highest inversion accuracy, with an overall accuracy (OA) of 0.85. Additionally, the bare soil index (BSI) achieved an overall accuracy of 0.84. In contrast, the OA of the remaining indices ranged from 0.28 to 0.47, which were significantly lower than those of the STI and BSI. This difference indicated the limited performance of the other indices in retrieving SI. The high accuracy of the STI is primarily attributed to its reliance on the bands B11 and B12, thereby avoiding potential interference from other spectral bands. The geostatistical analysis of the SI distribution revealed that the SI rate in the household mode area was 36.10% in 2022 in the household mode area. Regions A, B, C, and D exhibited SI rates of 34.76%, 33.05%, 57.88%, and 22.08%, respectively, with SI mainly concentrated in the eastern area of Gongzhuling City. Furthermore, the study investigated the potential impacts of household farming practices and national policies on the outcomes of SI implementation. Regarding state subsidies, the potential returns from SI per hectare of cropland in the study area varied from RMB −65 to 589. This variation indicates the importance of higher subsidies in motivating farmers to adopt SI practices. Sentinel-2 satellite imagery and the MK test were used to effectively monitor SI practices across a large area. Future studies will aim to integrate deep learning techniques to improve retrieval accuracy. Overall, this research presents a novel perspective and approach for monitoring SI practices and provides theoretical insights and data support to promote sustainable agriculture. Full article

Converting agricultural and forestry waste into high-value-added bio-oil via hydrothermal liquefaction (HTL) reduces incineration pollution and alleviates fuel oil shortages. Current research focuses on adjusting HTL parameters like temperature, time, catalyst, and pretreatment. Few studies explore raw material composition and its interactions with bio-oil properties, limiting guidance for future multi-material hydrothermal co-liquefaction. In view of the above problems, the lignocellulosic model in this paper used cellulose, hemicellulose, lignin, and protein as raw materials. At a low hydrothermal temperature (220 °C), the yield and properties of hydrothermal bio-oil were used as indicators to explore the influence of the proportional content of different model components on the interaction in the hydrothermal process through its simple binary blending and multivariate blending. Then, compared with the hydrothermal liquefaction process of cotton stalk, the interaction between components in the hydrothermal process of real lignocellulose was explored. The results demonstrated significant interactions among cellulose, lignin, and hemicellulose in cotton stalks. The relative strength of component interactions was ranked by yield (wt.%) and property modulation as follows: cellulose–lignin (C-L, 6.82%, synergistic enhancement) > cellulose–hemicellulose (C-X, 1.83%, inhibitory effect) > hemicellulose–lignin (X-L, 1.32%, non-significant interaction). Glycine supplementation enhanced bio-oil yields, with the most pronounced effect observed in cellulose–glycine (C-G) systems, where hydrothermal bio-oil yield increased from 2.29% to 4.59%. Aqueous-phase bio-oil exhibited superior high heating values (HHVs), particularly in hemicellulose–glycine (X-G) blends, which achieved the maximum HHV of 29.364 MJ/kg among all groups. Meanwhile, the characterization results of hydrothermal bio-oil under different mixing conditions showed that the proportion of model components largely determined the composition and properties of hydrothermal bio-oil, which can be used as a regulation method for the synthesis of directional chemicals. Cellulose–lignin (C-L) interactions demonstrated the strongest synergistic enhancement, reaching maximum efficacy at a 3:1 mass ratio. This study will deepen the understanding of the composition of lignocellulose raw materials in the hydrothermal process, promote the establishment of a hydrothermal product model of lignocellulose, and improve the yield of bio-oil. Full article

The Songliao River Basin (SLRB) is a key agricultural region in China, and understanding precipitation variations can provide crucial support for water resource management and sustainable development. This study used CN05.1 observational data and the Coupled Model Intercomparison Project Phase 6 (CMIP6) data to simulate and evaluate the precipitation characteristics within the SLRB. The optimal model ensemble was selected for future precipitation predictions. We analyzed the historical precipitation characteristics within the SLRB and projected future precipitation variations under SSP126, SSP245, and SSP585, while exploring the driving factors influencing precipitation. The results indicated that EC-Earth3-Veg (0.507) and BCC-CSM2-MR (0.493) from MME2 effectively capture precipitation variations, with MME2 corrected data more closely matching actual precipitation characteristics. From 1971 to 2014, precipitation showed an insignificant increasing trend, with most precipitation concentrated between May and September. Precipitation in the basin decreased from southeast to northwest. From 2026 to 2100, the increasing trend in precipitation became significant. The trend of precipitation growth over time was as follows: SSP126 < SSP245 < SSP585. Future precipitation distribution resembled the historical period, but the area of semiarid regions gradually decreased while the area of humid regions gradually increased, particularly under SSP585. The long-term increase in precipitation will become more pronounced, with a significant expansion of high-precipitation areas. In low-latitude, high-longitude areas, more precipitation events were expected to occur, while the impact of altitude was relatively weaker. From SSP126 to SSP585, the response of precipitation changes to temperature changes within the SLRB shifts from negative to positive. Under SSP585, this response becomes more pronounced, with average precipitation increasing by 4.87% for every 1 °C rise in temperature. Full article

Machine learning (ML) and deep learning (DL), subsets of artificial intelligence (AI), are the core technologies that lead significant transformation and innovation in various industries by integrating AI-driven solutions. Understanding ML and DL is essential to logically analyse the applicability of ML and DL and identify their effectiveness in different areas like healthcare, finance, agriculture, manufacturing, and transportation. ML consists of supervised, unsupervised, semi-supervised, and reinforcement learning techniques. On the other hand, DL, a subfield of ML, comprising neural networks (NNs), can deal with complicated datasets in health, autonomous systems, and finance industries. This study presents a holistic view of ML and DL technologies, analysing algorithms and their application’s capacity to address real-world problems. The study investigates the real-world application areas in which ML and DL techniques are implemented. Moreover, the study highlights the latest trends and possible future avenues for research and development (R&D), which consist of developing hybrid models, generative AI, and incorporating ML and DL with the latest technologies. The study aims to provide a comprehensive view on ML and DL technologies, which can serve as a reference guide for researchers, industry professionals, practitioners, and policy makers. Full article

Smart farming leverages Artificial Intelligence (AI) to address modern agricultural sustainability challenges. This study investigates the application of machine learning (ML), deep learning (DL), and time series analysis in agriculture through a systematic literature review following the PRISMA methodology. The review highlights the critical roles of ML and DL techniques in optimizing agricultural processes, such as crop selection, yield prediction, soil compatibility classification, and water management. ML algorithms facilitate tasks like crop selection and soil fertility classification, while DL techniques contribute to forecasting crop production and commodity prices. Additionally, time series analysis is employed for demand forecasting of crops, commodity price prediction, and forecasting crop yield production. The focus of this article is to provide a comprehensive overview of ML and DL techniques within the farming industry. Utilizing crop datasets, ML algorithms are instrumental in classifying soil fertility, crop selection, and various other aspects. DL algorithms, when applied to farming data, enable effective time series analysis and crop selection. By synthesizing the integration of these technologies, this review underscores their potential to enhance decision-making in agriculture and mitigate food scarcity challenges in the future. Full article