{"results":[{"id":"ss_dddbf9524c154797d7a28532569e9bd0bb7dedb4","title":"Ammonia emissions from agriculture and their contribution to fine particulate matter: A review of implications for human health.","authors":[{"name":"Katie E. Wyer"},{"name":"David B. Kelleghan"},{"name":"V. Blanes-Vidal"},{"name":"Günther Schauberger"},{"name":"T. Curran"}],"abstract":"Atmospheric ammonia (NH3) released from agriculture is contributing significantly to acidification and atmospheric NH3 may have on human health is much less readily available. The potential direct impact of NH3 on the health of the general public is under-represented in scientific literature, though there have been several studies which indicate that NH3 has a direct effect on the respiratory health of those who handle livestock. These health impacts can include a reduced lung function, irritation to the throat and eyes, and increased coughing and phlegm expulsion. More recent studies have indicated that agricultural NH3 may directly influence the early on-set of asthma in young children. In addition to the potential direct impact of ammonia, it is also a substantial contributor to the fine particulate matter (PM2.5) fraction (namely the US and Europe); where it accounts for the formation of 30% and 50% of all PM2.5 respectively. PM2.5 has the ability to penetrate deep into the lungs and cause long term illnesses such as Chronic Obstructive Pulmonary Disease (COPD) and lung cancer. Hence, PM2.5 causes economic losses which equate to billions of dollars (US) to the global economy annually. Both premature deaths associated with the health impacts from PM2.5 and economic losses could be mitigated with a reduction in NH3 emissions resulting from agriculture. As agriculture contributes to more than 81% of all global NH3 emissions, it is imperative that food production does not come at a cost to the world's ability to breathe; where reductions in NH3 emissions can be easier to achieve than other associated pollutants.","source":"Semantic Scholar","year":2022,"language":"en","subjects":["Medicine"],"doi":"10.1016/j.jenvman.2022.116285","url":"https://www.semanticscholar.org/paper/dddbf9524c154797d7a28532569e9bd0bb7dedb4","pdf_url":"https://doi.org/10.1016/j.jenvman.2022.116285","is_open_access":true,"citations":347,"published_at":"","score":76.41},{"id":"ss_6760f97cd0ec4b721ceec96205a846e6b56f05bc","title":"Advances in Agriculture Robotics: A State-of-the-Art Review and Challenges Ahead","authors":[{"name":"Luiz F. P. Oliveira"},{"name":"A. Moreira"},{"name":"Manuel Silva"}],"abstract":"The constant advances in agricultural robotics aim to overcome the challenges imposed by population growth, accelerated urbanization, high competitiveness of high-quality products, environmental preservation and a lack of qualified labor. In this sense, this review paper surveys the main existing applications of agricultural robotic systems for the execution of land preparation before planting, sowing, planting, plant treatment, harvesting, yield estimation and phenotyping. In general, all robots were evaluated according to the following criteria: its locomotion system, what is the final application, if it has sensors, robotic arm and/or computer vision algorithm, what is its development stage and which country and continent they belong. After evaluating all similar characteristics, to expose the research trends, common pitfalls and the characteristics that hinder commercial development, and discover which countries are investing into Research and Development (R\u0026D) in these technologies for the future, four major areas that need future research work for enhancing the state of the art in smart agriculture were highlighted: locomotion systems, sensors, computer vision algorithms and communication technologies. The results of this research suggest that the investment in agricultural robotic systems allows to achieve short—harvest monitoring—and long-term objectives—yield estimation.","source":"Semantic Scholar","year":2021,"language":"en","subjects":["Computer Science"],"doi":"10.3390/ROBOTICS10020052","url":"https://www.semanticscholar.org/paper/6760f97cd0ec4b721ceec96205a846e6b56f05bc","pdf_url":"https://www.mdpi.com/2218-6581/10/2/52/pdf?version=1618833746","is_open_access":true,"citations":346,"published_at":"","score":75.38},{"id":"ss_1132fdb12aa19615af1e6991be59cc01543e6bce","title":"Conservation Agriculture as a Sustainable System for Soil Health: A Review","authors":[{"name":"B. Cárceles Rodríguez"},{"name":"V. H. Durán-Zuazo"},{"name":"Miguel Soriano Rodríguez"},{"name":"I. García-Tejero"},{"name":"Baltasar Gálvez Ruiz"},{"name":"Simón Cuadros Tavira"}],"abstract":"Soil health is a term used to describe the general state or quality of soil, and in an agroecosystem, soil health can be defined as the ability of the soil to respond to agricultural practices in a way that sustainably supports both agricultural production and the provision of other ecosystem services. Conventional agricultural practices cause deterioration in soil quality, increasing its compaction, water erosion, and salinization and decreasing soil organic matter, nutrient content, and soil biodiversity, which negatively influences the productivity and long-term sustainability of the soil. Currently, there are many evidences throughout the world that demonstrate the capability of conservation agriculture (CA) as a sustainable system to overcome these adverse effects on soil health, to avoid soil degradation and to ensure food security. CA has multiple beneficial effects on the physical, chemical, and biological properties of soil. In addition, CA can reduce the negative impacts of conventional agricultural practices on soil health while conserving the production and provision of soil ecosystem services. Today, agricultural development is facing unprecedented challenges, and CA plays a significant role in the sustainability of intensive agriculture. This review will discuss the impact of conservation agricultural practices on soil health and their role in agricultural sustainability.","source":"Semantic Scholar","year":2022,"language":"en","subjects":null,"doi":"10.3390/soilsystems6040087","url":"https://www.semanticscholar.org/paper/1132fdb12aa19615af1e6991be59cc01543e6bce","pdf_url":"https://www.mdpi.com/2571-8789/6/4/87/pdf?version=1669195097","is_open_access":true,"citations":208,"published_at":"","score":72.24000000000001},{"id":"ss_679884bf55e81e802988aceac01488b7c55a1a7b","title":"Toward the Next Generation of Digitalization in Agriculture Based on Digital Twin Paradigm","authors":[{"name":"A. Nasirahmadi"},{"name":"O. Hensel"}],"abstract":"Digitalization has impacted agricultural and food production systems, and makes application of technologies and advanced data processing techniques in agricultural field possible. Digital farming aims to use available information from agricultural assets to solve several existing challenges for addressing food security, climate protection, and resource management. However, the agricultural sector is complex, dynamic, and requires sophisticated management systems. The digital approaches are expected to provide more optimization and further decision-making supports. Digital twin in agriculture is a virtual representation of a farm with great potential for enhancing productivity and efficiency while declining energy usage and losses. This review describes the state-of-the-art of digital twin concepts along with different digital technologies and techniques in agricultural contexts. It presents a general framework of digital twins in soil, irrigation, robotics, farm machineries, and food post-harvest processing in agricultural field. Data recording, modeling including artificial intelligence, big data, simulation, analysis, prediction, and communication aspects (e.g., Internet of Things, wireless technologies) of digital twin in agriculture are discussed. Digital twin systems can support farmers as a next generation of digitalization paradigm by continuous and real-time monitoring of physical world (farm) and updating the state of virtual world.","source":"Semantic Scholar","year":2022,"language":"en","subjects":["Computer Science","Medicine"],"doi":"10.3390/s22020498","url":"https://www.semanticscholar.org/paper/679884bf55e81e802988aceac01488b7c55a1a7b","pdf_url":"https://www.mdpi.com/1424-8220/22/2/498/pdf?version=1641810907","is_open_access":true,"citations":195,"published_at":"","score":71.85},{"id":"ss_4ec7ead56820ea5fec0b90c85681ec8d6d51b731","title":"Sustainable SMART fertilizers in agriculture systems: A review on fundamentals to in-field applications.","authors":[{"name":"S. Divya"},{"name":"Iryna Rusyn"},{"name":"Omar Solorza-Feria"},{"name":"K. Sathish-Kumar"}],"abstract":"Agriculture will face the issue of ensuring food security for a growing global population without compromising environmental security as demand for the world's food systems increases in the next decades. To provide enough food and reduce the harmful effects of chemical fertilization and improper disposal or reusing of agricultural wastes on the environment, will be required to apply current technologies in agroecosystems. Combining biotechnology and nanotechnology has the potential to transform agricultural practices and offer answers to both immediate and long-term issues. This review study seeks to identify, categorize, and characterize the so-called smart fertilizers as the future frontier of sustainable agriculture. The conventional fertilizer and smart fertilizers in general are covered in the first section of this review. Another key barrier preventing the widespread use of smart fertilizers in agriculture is the high cost of materials. Nevertheless, smart fertilizers are widely represented on the world market and are actively used in farms that have already switched to sustainable technologies. The advantages and disadvantages of various raw materials used to create smart fertilizers, with a focus on inorganic and organic materials, synthetic and natural polymers, along with their physical and chemical preparation processes, are contrasted in the following sections. The rate and the mechanism of release are covered. The purpose of this study is to provide a deep understanding of the advancements in smart fertilizers during the last ten years. Trends are also recognized and studied to provide insight for upcoming agricultural research projects.","source":"Semantic Scholar","year":2023,"language":"en","subjects":["Medicine"],"doi":"10.1016/j.scitotenv.2023.166729","url":"https://www.semanticscholar.org/paper/4ec7ead56820ea5fec0b90c85681ec8d6d51b731","is_open_access":true,"citations":110,"published_at":"","score":70.3},{"id":"ss_8ebfb2e76f5c0c925d8bf65ee6c3d8b219e8a66b","title":"Smart Irrigation Systems in Agriculture: A Systematic Review","authors":[{"name":"David Vallejo-Gómez"},{"name":"Marisol Osorio"},{"name":"Carlos A. Hincapié"}],"abstract":"This research aims to carry out a systematic review of the available literature about smart irrigation systems. It will be focused on systems using artificial intelligence techniques in urban and rural agriculture for soil crops to identify those that are currently being used or can be adapted to urban agriculture. To this end, a modified PRISMA 2020 method is applied, and three search equations are formulated. From those filters, and after a screening process, 170 articles are obtained. These articles are analyzed through VantagePoint, a text processing software. After this, they are taken through a detailed analysis phase in which 50 sources are selected as the most relevant to be read and analyzed by topic. Finally, the different phases of the analysis are used to draw conclusions that might be interesting for researchers working in this specific field or for the general public interested in rural and urban agriculture and its automation.","source":"Semantic Scholar","year":2023,"language":"en","subjects":null,"doi":"10.3390/agronomy13020342","url":"https://www.semanticscholar.org/paper/8ebfb2e76f5c0c925d8bf65ee6c3d8b219e8a66b","pdf_url":"https://www.mdpi.com/2073-4395/13/2/342/pdf?version=1675999934","is_open_access":true,"citations":71,"published_at":"","score":69.13},{"id":"arxiv_2506.10106","title":"One For All: LLM-based Heterogeneous Mission Planning in Precision Agriculture","authors":[{"name":"Marcos Abel Zuzuárregui"},{"name":"Mustafa Melih Toslak"},{"name":"Stefano Carpin"}],"abstract":"Artificial intelligence is transforming precision agriculture, offering farmers new tools to streamline their daily operations. While these technological advances promise increased efficiency, they often introduce additional complexity and steep learning curves that are particularly challenging for non-technical users who must balance tech adoption with existing workloads. In this paper, we present a natural language (NL) robotic mission planner that enables non-specialists to control heterogeneous robots through a common interface. By leveraging large language models (LLMs) and predefined primitives, our architecture seamlessly translates human language into intermediate descriptions that can be executed by different robotic platforms. With this system, users can formulate complex agricultural missions without writing any code. In the work presented in this paper, we extend our previous system tailored for wheeled robot mission planning through a new class of experiments involving robotic manipulation and computer vision tasks. Our results demonstrate that the architecture is both general enough to support a diverse set of robots and powerful enough to execute complex mission requests. This work represents a significant step toward making robotic automation in precision agriculture more accessible to non-technical users.","source":"arXiv","year":2025,"language":"en","subjects":["cs.RO","cs.AI"],"url":"https://arxiv.org/abs/2506.10106","pdf_url":"https://arxiv.org/pdf/2506.10106","is_open_access":true,"published_at":"2025-06-11T18:45:44Z","score":69},{"id":"arxiv_2509.25091","title":"Crop Spirals: Re-thinking the field layout for future robotic agriculture","authors":[{"name":"Lakshan Lavan"},{"name":"Lanojithan Thiyagarasa"},{"name":"Udara Muthugala"},{"name":"Rajitha de Silva"}],"abstract":"Conventional linear crop layouts, optimised for tractors, hinder robotic navigation with tight turns, long travel distances, and perceptual aliasing. We propose a robot-centric square spiral layout with a central tramline, enabling simpler motion and more efficient coverage. To exploit this geometry, we develop a navigation stack combining DH-ResNet18 waypoint regression, pixel-to-odometry mapping, A* planning, and model predictive control (MPC). In simulations, the spiral layout yields up to 28% shorter paths and about 25% faster execution for waypoint-based tasks across 500 waypoints than linear layouts, while full-field coverage performance is comparable to an optimised linear U-turn strategy. Multi-robot studies demonstrate efficient coordination on the spirals rule-constrained graph, with a greedy allocator achieving 33-37% lower batch completion times than a Hungarian assignment under our setup. These results highlight the potential of redesigning field geometry to better suit autonomous agriculture.","source":"arXiv","year":2025,"language":"en","subjects":["cs.RO"],"url":"https://arxiv.org/abs/2509.25091","pdf_url":"https://arxiv.org/pdf/2509.25091","is_open_access":true,"published_at":"2025-09-29T17:26:54Z","score":69},{"id":"arxiv_2511.15990","title":"Digital Agriculture Sandbox for Collaborative Research","authors":[{"name":"Osama Zafar"},{"name":"Rosemarie Santa González"},{"name":"Alfonso Morales"},{"name":"Erman Ayday"}],"abstract":"Digital agriculture is transforming the way we grow food by utilizing technology to make farming more efficient, sustainable, and productive. This modern approach to agriculture generates a wealth of valuable data that could help address global food challenges, but farmers are hesitant to share it due to privacy concerns. This limits the extent to which researchers can learn from this data to inform improvements in farming. This paper presents the Digital Agriculture Sandbox, a secure online platform that solves this problem. The platform enables farmers (with limited technical resources) and researchers to collaborate on analyzing farm data without exposing private information. We employ specialized techniques such as federated learning, differential privacy, and data analysis methods to safeguard the data while maintaining its utility for research purposes. The system enables farmers to identify similar farmers in a simplified manner without needing extensive technical knowledge or access to computational resources. Similarly, it enables researchers to learn from the data and build helpful tools without the sensitive information ever leaving the farmer's system. This creates a safe space where farmers feel comfortable sharing data, allowing researchers to make important discoveries. Our platform helps bridge the gap between maintaining farm data privacy and utilizing that data to address critical food and farming challenges worldwide.","source":"arXiv","year":2025,"language":"en","subjects":["cs.CR","cs.CY","cs.DC","cs.LG"],"url":"https://arxiv.org/abs/2511.15990","pdf_url":"https://arxiv.org/pdf/2511.15990","is_open_access":true,"published_at":"2025-11-20T02:41:35Z","score":69},{"id":"arxiv_2505.09278","title":"A drone that learns to efficiently find objects in agricultural fields: from simulation to the real world","authors":[{"name":"Rick van Essen"},{"name":"Gert Kootstra"}],"abstract":"Drones are promising for data collection in precision agriculture, however, they are limited by their battery capacity. Efficient path planners are therefore required. This paper presents a drone path planner trained using Reinforcement Learning (RL) on an abstract simulation that uses object detections and uncertain prior knowledge. The RL agent controls the flight direction and can terminate the flight. By using the agent in combination with the drone's flight controller and a detection network to process camera images, it is possible to evaluate the performance of the agent on real-world data. In simulation, the agent yielded on average a 78% shorter flight path compared to a full coverage planner, at the cost of a 14% lower recall. On real-world data, the agent showed a 72% shorter flight path compared to a full coverage planner, however, at the cost of a 25% lower recall. The lower performance on real-world data was attributed to the real-world object distribution and the lower accuracy of prior knowledge, and shows potential for improvement. Overall, we concluded that for applications where it is not crucial to find all objects, such as weed detection, the learned-based path planner is suitable and efficient.","source":"arXiv","year":2025,"language":"en","subjects":["cs.RO"],"url":"https://arxiv.org/abs/2505.09278","pdf_url":"https://arxiv.org/pdf/2505.09278","is_open_access":true,"published_at":"2025-05-14T10:59:09Z","score":69},{"id":"doaj_10.3390/agriculture15030242","title":"Unlocking the Power of Eggs: Nutritional Insights, Bioactive Compounds, and the Advantages of Omega-3 and Omega-6 Enriched Varieties","authors":[{"name":"Marius Giorgi Usturoi"},{"name":"Roxana Nicoleta Rațu"},{"name":"Ioana Cristina Crivei"},{"name":"Ionuț Dumitru Veleșcu"},{"name":"Alexandru Usturoi"},{"name":"Florina Stoica"},{"name":"Răzvan-Mihail Radu Rusu"}],"abstract":"This study explores the nutritional benefits and health implications of omega-3- and omega-6-enriched eggs, positioning them within the context of functional foods aimed at improving public health outcomes. With rising consumer interest in nutritionally fortified foods, omega-enriched eggs have emerged as a viable source of essential fatty acids, offering potential benefits for cardiovascular health, inflammation reduction, and cognitive function. This research examines enrichment techniques, particularly dietary modifications for laying hens, such as the inclusion of flaxseed and algae, to enhance omega-3 content and balance the omega-6-to-omega-3 ratio in eggs. The findings indicate that enriched eggs provide significantly higher levels of essential fatty acids and bioactive compounds than conventional eggs, aligning with dietary needs in populations with limited access to traditional omega-3 sources like fish. This study further addresses consumer perception challenges, regulatory constraints, and environmental considerations related to sustainable production practices. The conclusions underscore the value of omega-enriched eggs as a functional food that aligns with health-conscious dietary trends and recommend ongoing research to refine enrichment methods and expand market accessibility.","source":"DOAJ","year":2025,"language":"","subjects":["Agriculture (General)"],"doi":"10.3390/agriculture15030242","url":"https://www.mdpi.com/2077-0472/15/3/242","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.21608/jesaun.2024.326709.1374","title":"Evaluation of the Applications of using Global free Digital Elevation Models and GNSS-RTK data for Agricultural purposes in Egypt using Machine Learning","authors":[{"name":"Ashraf abdallah"},{"name":"Bara\u0026#039; Al-MISTAREHI"},{"name":"Amir SHTAYAT"}],"abstract":"Agriculture is a vital component of Egypt's economy; therefore, using Digital Elevation Models (DEMs) in agricultural planning in Egypt has significant benefits regarding water management, site appropriateness assessment, flood risk mitigation, and infrastructure construction. It is also essential for planners to make more informed decisions, optimize resource allocation, and support sustainable farming practices. This research paper investigates the accuracy of obtaining DEM data from four free global models (STRM30, ALOS30, COP30, and TanDEM-X90). The global DEM data has been compared to an actual GNSS-RTK DEM data surveyed onsite for two agricultural block areas in Aswan, the southern Government of Egypt. The two blocks are a part of a national project. For Block I and II, the RMSE of the Model STRM30 was 2.92 m and 3.59 m, respectively, indicating a poorer solution. Regarding accuracy, the ALOS30 model ranks third, reporting an RMSE of 2.58 m for block II and 3.30 m for block I. COP30 has an RMSE value of 1.06 m for blocks I and II and.91 m overall. TanDEM-X90 is the most accurate model in this investigation; block I provided an RMSE of 0.90 m with an SD of 0.58 m (SD95% = 0.38 m). After removing the anomalies, the model's stated RMSE for block II was 0.34 m, with an SD value of 0.62 m and 1.03 m. According to the classification using machine learning algorithms, with an accuracy of 84.7% for block I and 85% for block II, TanDEM-X90 is the best solution.","source":"DOAJ","year":2025,"language":"","subjects":["Engineering (General). Civil engineering (General)"],"doi":"10.21608/jesaun.2024.326709.1374","url":"https://jesaun.journals.ekb.eg/article_393447_9546e355affffebb2c5da930aaeba9a0.pdf","pdf_url":"https://jesaun.journals.ekb.eg/article_393447_9546e355affffebb2c5da930aaeba9a0.pdf","is_open_access":true,"published_at":"","score":69},{"id":"doaj_10.5281/zenodo.17879057","title":"Effect of Short-term Treated Wastewater Application on Physiological Traits and Stress-related Genes (CaCAT2, CaDREB32, CaLOX1) in Pepper","authors":[{"name":"Merve Dilek  KARATAŞ "}],"abstract":"\nThis study investigated the effects of treated wastewater irrigation on physiological traits and gene expression in pepper (Capsicum annuum) plants during early development. Control (tap water) and treated wastewater treatments were compared, and physiological parameters, including SPAD values and leaf color measurements, were assessed. In addition, the expression levels of stress-related genes CaCAT2, CaDREB32, and CaLOX1 were analyzed using RT-qPCR. The results showed that treated wastewater irrigation significantly enhanced leaf chlorophyll content and caused a shift in leaf coloration toward darker, greenish tones. At the molecular level, a moderate upregulation of CaCAT2 was detected, whereas CaDREB32 exhibited only a slight and statistically insignificant change. The most pronounced response was observed for CaLOX1, which showed an approximately 50% increase in expression under treated wastewater treatment compared with the control. These findings suggest that treated wastewater irrigation can temporarily enhance photosynthetic capacity in pepper plants, while concurrently activating stress-related signaling pathways. This dual effect highlights that the agricultural use of treated wastewater entails potential benefits but also carries long-term risks that must be carefully considered.\n","source":"DOAJ","year":2025,"language":"","subjects":["Agriculture (General)"],"doi":"10.5281/zenodo.17879057","url":"https://ispecjournal.com/index.php/ispecjas/article/view/865","is_open_access":true,"published_at":"","score":69},{"id":"ss_2c48622ded1753aadb835b307f23dd809a83065d","title":"Endophytes in Agriculture: Potential to Improve Yields and Tolerances of Agricultural Crops","authors":[{"name":"Declan Watts"},{"name":"E. Palombo"},{"name":"Alex Jaimes Castillo"},{"name":"Bita Zaferanloo"}],"abstract":"Endophytic fungi and bacteria live asymptomatically within plant tissues. In recent decades, research on endophytes has revealed that their significant role in promoting plants as endophytes has been shown to enhance nutrient uptake, stress tolerance, and disease resistance in the host plants, resulting in improved crop yields. Evidence shows that endophytes can provide improved tolerances to salinity, moisture, and drought conditions, highlighting the capacity to farm them in marginal land with the use of endophyte-based strategies. Furthermore, endophytes offer a sustainable alternative to traditional agricultural practices, reducing the need for synthetic fertilizers and pesticides, and in turn reducing the risks associated with chemical treatments. In this review, we summarise the current knowledge on endophytes in agriculture, highlighting their potential as a sustainable solution for improving crop productivity and general plant health. This review outlines key nutrient, environmental, and biotic stressors, providing examples of endophytes mitigating the effects of stress. We also discuss the challenges associated with the use of endophytes in agriculture and the need for further research to fully realise their potential.","source":"Semantic Scholar","year":2023,"language":"en","subjects":["Medicine"],"doi":"10.3390/microorganisms11051276","url":"https://www.semanticscholar.org/paper/2c48622ded1753aadb835b307f23dd809a83065d","pdf_url":"https://www.mdpi.com/2076-2607/11/5/1276/pdf?version=1683904422","is_open_access":true,"citations":66,"published_at":"","score":68.97999999999999},{"id":"ss_f37250dc4d761494bb664b800d519f6d4e930ab5","title":"A pending task for the digitalisation of agriculture: A general framework for technologies classification in agriculture","authors":[{"name":"J. C. Moreno"},{"name":"M. Berenguel"},{"name":"J. G. Donaire"},{"name":"F. Rodríguez"},{"name":"J. Sánchez-Molina"},{"name":"J. Guzmán"},{"name":"Cynthia L. Giagnocavo"}],"abstract":"","source":"Semantic Scholar","year":2024,"language":"en","subjects":null,"doi":"10.1016/j.agsy.2023.103794","url":"https://www.semanticscholar.org/paper/f37250dc4d761494bb664b800d519f6d4e930ab5","is_open_access":true,"citations":27,"published_at":"","score":68.81},{"id":"ss_36c42462466f7bb02c72db5f4fa6c40688ef70de","title":"Plant Detection and Counting: Enhancing Precision Agriculture in UAV and General Scenes","authors":[{"name":"Dunlu Lu"},{"name":"Jianxiong Ye"},{"name":"Yangxu Wang"},{"name":"Zhenghong Yu"}],"abstract":"Plant detection and counting play a crucial role in modern agriculture, providing vital references for precision management and resource allocation. This study follows the footsteps of machine learning experts by introducing the state-of-the-art Yolov8 technology into the field of plant science. Moreover, we made some simple yet effective improvements. The integration of shallow-level information into the Path Aggregation Network (PANet) served to counterbalance the resolution loss stemming from the expanded receptive field. The enhancement of upsampled features was accomplished through combining the lightweight up-sampling operator Content-Aware ReAssembly of Features (CARAFE) with the Multi-Efficient Channel Attention (Mlt-ECA) technique to optimize the precision of upsampled features. This collective approach markedly amplified the discernment of small objects in Unmanned Aerial Vehicle (UAV) images, naming it Yolov8-UAV. Our evaluation is based on datasets containing four different plant species. Experimental results demonstrate the strong competitiveness of our proposed method even when compared to the most advanced counting techniques, and it possesses sufficient robustness. In order to advance the cross-disciplinary research of computer vision and plant science, we also release a new cotton boll dataset with detailed annotated bounding box information. What’s more, we address previous oversights in existing wheat ear datasets by providing updated labels consistent with global research advancements. Overall, this research offers practitioners a powerful solution for addressing real-world application challenges. For UAV scenarios, recommend using the specialized Yolov8-UAV, while Yolov8-N is a wise choice for general scenes due to its sufficient accuracy and speed in the majority of cases. Furthermore, we contribute two meaningful datasets that have research significance, effectively promoting the application of data resources in the field of plant science. In short, our contribution is to improve the use of Yolov8 in UAV scenarios and open two datasets with bounding boxes. The curated data and code can be accessed at the following link: https://github.com/Ye-Sk/Plant-dataset.","source":"Semantic Scholar","year":2023,"language":"en","subjects":["Computer Science"],"doi":"10.1109/ACCESS.2023.3325747","url":"https://www.semanticscholar.org/paper/36c42462466f7bb02c72db5f4fa6c40688ef70de","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10287390.pdf","is_open_access":true,"citations":45,"published_at":"","score":68.35},{"id":"arxiv_2404.08931","title":"Label-free Anomaly Detection in Aerial Agricultural Images with Masked Image Modeling","authors":[{"name":"Sambal Shikhar"},{"name":"Anupam Sobti"}],"abstract":"Detecting various types of stresses (nutritional, water, nitrogen, etc.) in agricultural fields is critical for farmers to ensure maximum productivity. However, stresses show up in different shapes and sizes across different crop types and varieties. Hence, this is posed as an anomaly detection task in agricultural images. Accurate anomaly detection in agricultural UAV images is vital for early identification of field irregularities. Traditional supervised learning faces challenges in adapting to diverse anomalies, necessitating extensive annotated data. In this work, we overcome this limitation with self-supervised learning using a masked image modeling approach. Masked Autoencoders (MAE) extract meaningful normal features from unlabeled image samples which produces high reconstruction error for the abnormal pixels during reconstruction. To remove the need of using only ``normal\" data while training, we use an anomaly suppression loss mechanism that effectively minimizes the reconstruction of anomalous pixels and allows the model to learn anomalous areas without explicitly separating ``normal\" images for training. Evaluation on the Agriculture-Vision data challenge shows a mIOU score improvement in comparison to prior state of the art in unsupervised and self-supervised methods. A single model generalizes across all the anomaly categories in the Agri-Vision Challenge Dataset","source":"arXiv","year":2024,"language":"en","subjects":["cs.CV","cs.AI","cs.LG"],"url":"https://arxiv.org/abs/2404.08931","pdf_url":"https://arxiv.org/pdf/2404.08931","is_open_access":true,"published_at":"2024-04-13T08:49:17Z","score":68},{"id":"doaj_10.3390/app14010442","title":"Potential of Radioactive Isotopes Production in DEMO for Commercial Use","authors":[{"name":"Pavel Pereslavtsev"},{"name":"Christian Bachmann"},{"name":"Joelle Elbez-Uzan"},{"name":"Jin Hun Park"}],"abstract":"There is widespread use of nuclear radiation for medical imagery and treatments. Worldwide, almost 40 million treatments are performed per year. There are also applications of radiation sources in other commercial fields, e.g., for weld inspection or steelmaking processes, in consumer products, in the food industry, and in agriculture. The large number of neutrons generated in a fusion reactor such as DEMO could potentially contribute to the production of the required radioactive isotopes. The associated commercial value of these isotopes could mitigate the capital investments and operating costs of a large fusion plant. The potential of producing various radioactive isotopes was studied from material pieces arranged inside a DEMO equatorial port plug. In this location, they are exposed to an intensive neutron spectrum suitable for a high isotope production rate. For this purpose, the full 3D geometry of one DEMO toroidal sector with an irradiation chamber in the equatorial port plug was modeled with an MCNP code to perform neutron transport simulations. Subsequent activation calculations provide detailed information on the quality and composition of the produced radioactive isotopes. The technical feasibility and the commercial potential of the production of various isotopes in the DEMO port are reported.","source":"DOAJ","year":2024,"language":"","subjects":["Technology","Engineering (General). Civil engineering (General)","Biology (General)","Physics","Chemistry"],"doi":"10.3390/app14010442","url":"https://www.mdpi.com/2076-3417/14/1/442","is_open_access":true,"published_at":"","score":68},{"id":"ss_b911175792141a73053a5b9bc822a4b5165daca3","title":"The Impact of Climate Change on Agriculture Production in Ethiopia: Application of a Dynamic Computable General Equilibrium Model","authors":[{"name":"R. Solomon"},{"name":"B. Simane"},{"name":"B. Zaitchik"}],"abstract":"The challenge of meeting the ever-increasing food demand for the growing population will be further exacerbated by climate change in Ethiopia. This paper presents the simulated economy-wide impacts of climate change on the agriculture sector of Ethiopia using a dynamic computable general equilibrium (CGE) model. The study simulated the scenarios of agricultural productivity change induced by climate change up to the year 2050. At national level, the simulation results suggest that crop production will be adversely affected during the coming four decades and the severity will increase over the time period. Production of teff, maize and sorghum will decline by 25.4, 21.8 and 25.2 percent, respectively by 2050 compared to the base period. Climate change will also cause losses of 31.1 percent agricultural GDP at factor cost by 2050. Climate change affects more the income and consumption of poor rural households than urban rural non-farming households. The reduction in agricultural production will not be evenly distributed across agro ecological zones, and will not all be negative. Among rural residents, climate change impacts tend to hurt the income of the poor more in drought prone regions. Income from labor, land and livestock in moisture sufficient highland cereal-based will decline by 5.1, 8.8 and 15.2 percent in 2050. This study indicated that since climate change is an inevitable phenomenon, the country should start mainstreaming adaptation measures to sustain the overall performance of the economy.","source":"Semantic Scholar","year":2021,"language":"en","subjects":["Economics"],"doi":"10.4236/AJCC.2021.101003","url":"https://www.semanticscholar.org/paper/b911175792141a73053a5b9bc822a4b5165daca3","pdf_url":"http://www.scirp.org/journal/PaperDownload.aspx?paperID=107791","is_open_access":true,"citations":83,"published_at":"","score":67.49000000000001},{"id":"arxiv_2303.02460","title":"Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis","authors":[{"name":"Jing Wu"},{"name":"David Pichler"},{"name":"Daniel Marley"},{"name":"David Wilson"},{"name":"Naira Hovakimyan"},{"name":"Jennifer Hobbs"}],"abstract":"A key challenge for much of the machine learning work on remote sensing and earth observation data is the difficulty in acquiring large amounts of accurately labeled data. This is particularly true for semantic segmentation tasks, which are much less common in the remote sensing domain because of the incredible difficulty in collecting precise, accurate, pixel-level annotations at scale. Recent efforts have addressed these challenges both through the creation of supervised datasets as well as the application of self-supervised methods. We continue these efforts on both fronts. First, we generate and release an improved version of the Agriculture-Vision dataset (Chiu et al., 2020b) to include raw, full-field imagery for greater experimental flexibility. Second, we extend this dataset with the release of 3600 large, high-resolution (10cm/pixel), full-field, red-green-blue and near-infrared images for pre-training. Third, we incorporate the Pixel-to-Propagation Module Xie et al. (2021b) originally built on the SimCLR framework into the framework of MoCo-V2 Chen et al.(2020b). Finally, we demonstrate the usefulness of this data by benchmarking different contrastive learning approaches on both downstream classification and semantic segmentation tasks. We explore both CNN and Swin Transformer Liu et al. (2021a) architectures within different frameworks based on MoCo-V2. Together, these approaches enable us to better detect key agricultural patterns of interest across a field from aerial imagery so that farmers may be alerted to problematic areas in a timely fashion to inform their management decisions. Furthermore, the release of these datasets will support numerous avenues of research for computer vision in remote sensing for agriculture.","source":"arXiv","year":2023,"language":"en","subjects":["cs.CV"],"url":"https://arxiv.org/abs/2303.02460","pdf_url":"https://arxiv.org/pdf/2303.02460","is_open_access":true,"published_at":"2023-03-04T17:35:24Z","score":67}],"total":10492128,"page":1,"page_size":20,"sources":["CrossRef","arXiv","DOAJ","Semantic Scholar"],"query":"Agriculture (General)"}