Hasil untuk "Agriculture (General)"

Renata Mariane de Souza, Daiana Seibert, H. Quesada et al.

Abstract A review of the main pesticides employed in agriculture found that the pesticide groups present in the highest amounts are herbicides, fungicides, and insecticides. For this reason, their occurrence in surface waters around the world, as well as their adverse effects on non-target organisms were reviewed for the period 2012–2019. Among the most common vegetal herbicides is atrazine, followed by metalochlor, both of which are widely-used on soybean and corn crops. Insecticides are used to control insects by agonizing them. Although they present low toxicity for mammals, they are toxic to ecosystems and impact the environment when present. Fungicides are employed to prevent fungal infections by damaging the cellular membrane, causing damage to non-target organisms, tebuconazole and carbendazim were the most frequent fungicides identified in surface waters throughout the world. Once pesticides reach water bodies, they can impact the whole ecological food chain, since other animals, including humans, feed on aquatic animals that may be contaminated. Another concern is the mixing of pesticides, in which case the mixture may be more toxic than any one single compound. Because mixtures of pesticides are commonly found in surface water, the need for suitable water treatment is crucial.

L. Schreefel, R. Schulte, I. D. de Boer et al.

Abstract Regenerative agriculture (RA) is proposed as a solution towards sustainable food systems. A variety of actors perceive RA differently, and a clear scientific definition is lacking. We reviewed 28 studies to find convergence and divergence between objectives and activities that define RA. Our results show convergence related to objectives that enhance the environment and stress the importance of socio-economic dimensions that contribute to food security. The objectives of RA in relation to socio-economic dimensions, however, are general and lack a framework for implementation. From our analysis, we propose a provisional definition of RA as an approach to farming that uses soil conservation as the entry point to regenerate and contribute to multiple ecosystem services.

K. Demestichas, N. Peppes, T. Alexakis et al.

Food holds a major role in human beings’ lives and in human societies in general across the planet. The food and agriculture sector is considered to be a major employer at a worldwide level. The large number and heterogeneity of the stakeholders involved from different sectors, such as farmers, distributers, retailers, consumers, etc., renders the agricultural supply chain management as one of the most complex and challenging tasks. It is the same vast complexity of the agriproducts supply chain that limits the development of global and efficient transparency and traceability solutions. The present paper provides an overview of the application of blockchain technologies for enabling traceability in the agri-food domain. Initially, the paper presents definitions, levels of adoption, tools and advantages of traceability, accompanied with a brief overview of the functionality and advantages of blockchain technology. It then conducts an extensive literature review on the integration of blockchain into traceability systems. It proceeds with discussing relevant existing commercial applications, highlighting the relevant challenges and future prospects of the application of blockchain technologies in the agri-food supply chain.

P. Sambo, C. Nicoletto, A. Giro et al.

Soilless cultivation represent a valid opportunity for the agricultural production sector, especially in areas characterized by severe soil degradation and limited water availability. Furthermore, this agronomic practice embodies a favorable response toward an environment-friendly agriculture and a promising tool in the vision of a general challenge in terms of food security. This review aims therefore at unraveling limitations and opportunities of hydroponic solutions used in soilless cropping systems focusing on the plant mineral nutrition process. In particular, this review provides information (1) on the processes and mechanisms occurring in the hydroponic solutions that ensure an adequate nutrient concentration and thus an optimal nutrient acquisition without leading to nutritional disorders influencing ultimately also crop quality (e.g., solubilization/precipitation of nutrients/elements in the hydroponic solution, substrate specificity in the nutrient uptake process, nutrient competition/antagonism and interactions among nutrients); (2) on new emerging technologies that might improve the management of soilless cropping systems such as the use of nanoparticles and beneficial microorganism like plant growth-promoting rhizobacteria (PGPRs); (3) on tools (multi-element sensors and interpretation algorithms based on machine learning logics to analyze such data) that might be exploited in a smart agriculture approach to monitor the availability of nutrients/elements in the hydroponic solution and to modify its composition in realtime. These aspects are discussed considering what has been recently demonstrated at the scientific level and applied in the industrial context.

Meike Janssen, C. Busch, Manika Rödiger et al.

E. Hunt, C. Daughtry

ABSTRACT Remote sensing from unmanned aircraft systems (UAS) was expected to be an important new technology to assist farmers with precision agriculture, especially crop nutrient management. There are three advantages using UAS platforms compared to manned aircraft platforms with the same sensor for precision agriculture: (1) smaller ground sample distances, (2) incident light sensors for image calibration, and (3) canopy height models created from structure-from-motion point clouds. These developments hold promise for future data products. In order to better match vendor capabilities with farmer requirements, we classify applications into three general niches: (1) scouting for problems, (2) monitoring to prevent yield losses, and (3) planning crop management operations. The three different niches have different requirements for sensor calibration and have different costs of operation. Planning crop management operations may have the most environmental and economic benefits. However, a USDA Economic Research Report showed that only about 20% of farmers in the USA have adopted variable rate applicators; so, most farmers in the USA may not have the technology to benefit from management plans. In the near-term, monitoring to prevent yield losses from weeds, insects, and diseases may provide the most economic and environmental benefits, but the costs for data acquisition need to be reduced.

Vanesa Martos, Ali Ahmad, P. Cartujo et al.

Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.

Malakhi Hopkins, Alice Kate Li, Shobhita Kramadhati et al.

Common remote sensing modalities (RGB, multispectral, hyperspectral imaging or LiDAR) are often used to indirectly measure crop health and do not directly capture plant stress indicators. Commercially available direct leaf sensors are bulky, powered electronics that are expensive and interfere with crop growth. In contrast, low-cost, passive and bio-degradable leaf sensors offer an opportunity to advance real-time monitoring as they directly interface with the crop surface while not interfering with crop growth. To this end, we co-design a sensor-detector system, where the sensor is a passive colorimetric leaf sensor that directly measures crop health in a precision agriculture setting, and the detector autonomously obtains optical signals from these leaf sensors. The detector comprises a low size weight and power (SWaP) mobile ground robot with an onboard monocular RGB camera and object detector to localize each leaf sensor, as well as a hyperspectral camera with a motorized mirror and halogen light to acquire hyperspectral images. The sensor's crop health-dependent optical signals can be extracted from the hyperspectral images. The proof-of-concept system is demonstrated in row-crop environments both indoors and outdoors where it is able to autonomously navigate, locate and obtain a hyperspectral image of all leaf sensors present, and acquire interpretable spectral resonance with 80 $\%$ accuracy within a required retrieval distance from the sensor.

M. Grosse, K. Honda, C. Spech et al.

Agricultural field operations are generally tracked as work records (WR), incorporating data points such as; work type, machine type, timestamped trajectories and field information. WR data which is automatically recorded by modern machinery equipped with Information and Communication Technologies (ICT) can enable efficient farm management decision making. Globally, farmers often rely on aged or legacy farming machinery and manual data recording, which introduces significant labor costs and increases the risk of inaccurate data input. To address this challenge, a field study in Central Japan was conducted to showcase automated data collection by retrofitting legacy farming machinery with low-cost Internet of Things (IoT) devices. For single-purpose vehicles (SPV), which only carry out single work types such as planting, LTE (Long Term Evolution) and Global Navigation Satellite System (GNSS) units were installed to record trajectory data. For multi-purpose vehicles (MPV), such as tractors which perform multiple work types, the configuration settings of these vehicles had to include implements and attachments data. To obtain this data, industry standard LTE-GNSS Bluetooth gateways were fitted onto MPV and low-cost BLE (Bluetooth Low Energy) beacons were attached to implements. After installation, over a seven-month field preparation and planting period 1,623 WR, including 421 WR for SPV and 1,120 WR for MVP, were automatically obtained. For MPV, the WR included detailed configuration settings enabling detection of the specific work types. These findings demonstrate the potential of low cost IoT GNSS devices for precision agriculture strategies to support management decisions in farming operations.

Vishal Nedungadi, Xingguo Xiong, Aike Potze et al.

Food security remains a global concern as population grows and climate change intensifies, demanding innovative solutions for sustainable agricultural productivity. Recent advances in foundation models have demonstrated remarkable performance in remote sensing and climate sciences, and therefore offer new opportunities for agricultural monitoring. However, their application in challenges related to agriculture-such as crop type mapping, crop phenology estimation, and crop yield estimation-remains under-explored. In this work, we quantitatively evaluate existing foundational models to assess their effectivity for a representative set of agricultural tasks. From an agricultural domain perspective, we describe a requirements framework for an ideal agricultural foundation model (CropFM). We then survey and compare existing general-purpose foundational models in this framework and empirically evaluate two exemplary of them in three representative agriculture specific tasks. Finally, we highlight the need for a dedicated foundational model tailored specifically to agriculture.

Pallavi Gupta

While agriculture is recognised as vital for improving nutrition, the evidence linking women's participation to sustained nutritional gains remains inconclusive. This review synthesizes studies published between 2000 and 2024 to reflect current agricultural practices and nutritional challenges. We examine how agricultural practices and time use affect nutritional outcomes among rural women through pathways such as income generation food preparation and intra-household labour allocation. A structured methodology with clear inclusion and exclusion criteria was used to assess gender-sensitive and nutrition-sensitive interventions. Using narrative synthesis the review categorizes findings around key themes and contextual factors including socio-economic status seasonality and labour intensity. The results show that while increased involvement in agriculture can boost household dietary diversity and income it also raises time burdens that affect food preparation childcare and self-care. Positive outcomes occur when interventions enhance women's decision-making power income access and use of time-saving technologies whereas negative outcomes emerge when excessive workloads compromise energy balance and limit rest. A conceptual framework is presented to map the dual pathways linking agriculture time use and nutrition capturing the roles of labour distribution social norms and resource access. The framework underscores the need to integrate gender equity time efficiency and nutritional objectives into agricultural policies. In conclusion agricultural interventions have potential for nutritional improvement if they are carefully designed to avoid unintended negative impacts on women.

WANG Jiawei(王佳薇), GONG Shuying(龚淑英), FAN Fangyuan(范方媛) et al.

Granular green tea is a significant category of famous high-quality green tea in Zhejiang Province. In this study, we conducted comparative experiments on key technical parameters, including moisture resurgence, second fixation and stir-frying techniques, during the primary processing of granular green tea and systematically analyzed their effects on sensory quality and chemical composition. The results indicated that appropriately controlling the rehumidification time (1.5 h), reducing the moisture content of the second fixed leaves (40%), decreasing the amount of tea leaves per frying pan (4 kg/pan), and selecting a frying pan with better air permeability (60-type) effectively enhanced the dry tea appearance and the emerald color of the tea liquor and improved the freshness of the aroma and taste. Moderate rehumidification and a lower moisture content in the second fixed leaves increased the content of catechins and some key umami amino acids, whereas a lower loading amount of tea leaves per frying pan increased the total amino acid content; a higher moisture content in the second fixed leaves increased the water extract content. Higher-quality samples with superior aroma and freshness had higher levels of floral compounds such as linalool, geraniol, and α-terpineol. Conversely, a longer rehumidification time, greater moisture content in the second fixed leaves, more tea leaves per frying pan, and lower air permeability of the stir-frying machine led to higher temperatures and moisture and increased the relative content of compounds such as n-hexadecanoic acid, heptanoic acid, and 6, 10, 14-trimethyl-2-pentadecanone in the aroma profile. In conclusion, this study identifies an optimal processing combination for improving the overall quality of granular green tea and provides a theoretical basis for refining its processing technology.(颗粒形绿茶是浙江省名优绿茶的重要品类。本研究针对颗粒形绿茶初制中回潮、二青、炒制等工序的关键技术参数开展对比试验，系统分析不同技术参数对颗粒形绿茶感官品质及化学组分的影响。结果表明，适当控制回潮时间（1.5 h）、降低二青叶含水率（40%）、适当减少炒制投叶量（4 kg/小锅）及选用透气性能较好的炒锅（60型），能够提升颗粒形绿茶外形及茶汤的翠绿色泽，并提高香气滋味的鲜爽性。适度回潮和较低的二青叶含水率有助于提升儿茶素含量及部分对鲜味具有重要贡献的氨基酸含量；较低的炒制投叶量可提升氨基酸总量；而较高的二青叶含水率则有利于提升水浸出物含量。在香气鲜爽性高、品质较优的样品中，芳樟醇、香叶醇、α-松油醇等具有花香特征的化合物的相对含量较高；而长时间回潮、较高的二青叶含水率、较高的炒制投叶量及较低透气性的锅型所引起的较高温度及含水率，则会促进正十六烷酸、庚酸、6，10，14-三甲基-2-十五烷酮等香气组分的积累。本研究明确了提升颗粒形绿茶综合品质的适宜工艺组合，为优化其加工技术提供了理论依据。)

Jaeyoung Ro, Hor-Gil Hur, Sujin Lee

Abstract Agricultural practices are the largest anthropogenic source of nitrous oxide (N2O), a potent greenhouse gas contributing to global climate change. Applying symbiotic microbial inoculants capable of complete denitrification offers a promising strategy to mitigate N2O emissions from agricultural fields. This study reports the strain-level diversity and geographical distribution of soybean symbiont bacteria Bradyrhizobium species carrying the nosZ gene, which encodes nitrous oxide reductase. Of 227 indigenous Bradyrhizobium isolates from soybean root nodules across South Korea, 162 were found to possess the nosZ gene, indicating their potential for N2O reduction. The majority of the most prevalent species, Bradyrhizobium diazoefficiens, harbor the nosZ gene, contributing to the overall high frequency of nosZ-positive genotypes nationwide. In contrast, no evidence of the nosZ gene was detected in the second most abundant species, Bradyrhizobium elkanii, which was predominantly isolated from the southwestern regions, raising the possibility of elevated N₂O emissions in these areas. The presence of the nosZ gene varies substantially even within the same species, highlighting the importance of understanding strain-level genetic and functional diversity to develop Bradyrhizobium inoculants optimized for both nitrogen fixation and denitrification.

P. Rai, Vanish Kumar, Sang-Soo Lee et al.

In the recent techno-scientific revolution, nanotechnology has gained popularity at a rapid pace in different sectors and disciplines, specifically environmental, sensing, bioenergy, and agricultural systems. Controlled, easy, economical, and safe synthesis of nanomaterials is desired for the development of new-age nanotechnology. In general, nanomaterial synthesis techniques, such as chemical synthesis, are not completely safe or environmentally friendly due to harmful chemicals used or to toxic by-products produced. Moreover, a few nanomaterials are present as by-product during washing process, which may accumulate in water, air, and soil system to pose serious threats to plants, animals, and microbes. In contrast, using plants for nanomaterial (especially nanoparticle) synthesis has proven to be environmentally safe and economical. The role of plants as a source of nanoparticles is also likely to expand the number of options for sustainable green renewable energy, especially in biorefineries. Despite several advantages of nanotechnology, the nano-revolution has aroused concerns in terms of the fate of nanoparticles in the environment because of the potential health impacts caused by nanotoxicity upon their release. In the present panoramic review, we discuss the possibility that a multitudinous array of nanoparticles may find applications convergent with human welfare based on the synthesis of diverse nanoparticles from plants and their extracts. The significance of plant-nanoparticle interactions has been elucidated further for nanoparticle synthesis, applications of nanoparticles, and the disadvantages of using plants for synthesizing nanoparticles. Finally, we discuss future prospects of plant-nanoparticle interactions in relation to the environment, energy, and agriculture with implications in nanotechnology.

Ting Sun, Chiwei Su, Nawazish Mirza et al.

The present paper explores the impact of trade policy uncertainty (TPU) on agricultural commodity prices (ACP) by employing bootstrap full- and subsample rolling-window Granger causality tests We find that TPU has both positive and negative effects on ACP, suggesting that TPU may change the supply of and demand for agricultural commodities, leading to fluctuations in ACP These results support the hypotheses derived from the general equilibrium model, which highlights that TPU can significantly affect ACP In turn, we find a positive impact of ACP on TPU, indicating that the agricultural commodity market reflects trade conditions in advance In the context of Sino-U S trade frictions and the COVID-19 pandemic, the interaction between TPU and ACP can provide insights for governments to prevent large fluctuations in agricultural commodity markets and stabilize the national economy © 2021 Elsevier B V

Iason Tsardanidis, Alkiviadis Koukos, Vasileios Sitokonstantinou et al.

Uninterrupted optical image time series are crucial for the timely monitoring of agricultural land changes, particularly in grasslands. However, the continuity of such time series is often disrupted by clouds. In response to this challenge, we propose an innovative deep learning method that integrates cloud-free optical (Sentinel-2) observations and weather-independent (Sentinel-1) Synthetic Aperture Radar (SAR) data. Our approach employs a hybrid architecture combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to generate continuous Normalized Difference Vegetation Index (NDVI) time series, highlighting the role of NDVI in the synergy between SAR and optical data. We demonstrate the significance of observation continuity by assessing the impact of the generated NDVI time series on the downstream task of grassland mowing event detection. We conducted our study in Lithuania, a country characterized by extensive cloud coverage, and compared our approach with alternative interpolation techniques (i.e., linear, Akima, quadratic). Our method outperformed these techniques, achieving an average Mean Absolute Error (MAE) of 0.024 and a coefficient of determination R^2 of 0.92. Additionally, our analysis revealed improvement in the performance of the mowing event detection, with F1-score up to 84% using two widely applied mowing detection methodologies. Our method also effectively mitigated sudden shifts and noise originating from cloudy observations, which are often missed by conventional cloud masks and adversely affect mowing detection precision.

Uddhav Bhattarai, Santosh Bhusal, Qin Zhang et al.

One of the major challenges for the agricultural industry today is the uncertainty in manual labor availability and the associated cost. Automated flower and fruit density estimation, localization, and counting could help streamline harvesting, yield estimation, and crop-load management strategies such as flower and fruitlet thinning. This article proposes a deep regression-based network, AgRegNet, to estimate density, count, and location of flower and fruit in tree fruit canopies without explicit object detection or polygon annotation. Inspired by popular U-Net architecture, AgRegNet is a U-shaped network with an encoder-to-decoder skip connection and modified ConvNeXt-T as an encoder feature extractor. AgRegNet can be trained based on information from point annotation and leverages segmentation information and attention modules (spatial and channel) to highlight relevant flower and fruit features while suppressing non-relevant background features. Experimental evaluation in apple flower and fruit canopy images under an unstructured orchard environment showed that AgRegNet achieved promising accuracy as measured by Structural Similarity Index (SSIM), percentage Mean Absolute Error (pMAE) and mean Average Precision (mAP) to estimate flower and fruit density, count, and centroid location, respectively. Specifically, the SSIM, pMAE, and mAP values for flower images were 0.938, 13.7%, and 0.81, respectively. For fruit images, the corresponding values were 0.910, 5.6%, and 0.93. Since the proposed approach relies on information from point annotation, it is suitable for sparsely and densely located objects. This simplified technique will be highly applicable for growers to accurately estimate yields and decide on optimal chemical and mechanical flower thinning practices.

Andrew Heschl, Mauricio Murillo, Keyhan Najafian et al.

This paper introduces a methodology for generating synthetic annotated data to address data scarcity in semantic segmentation tasks within the precision agriculture domain. Utilizing Denoising Diffusion Probabilistic Models (DDPMs) and Generative Adversarial Networks (GANs), we propose a dual diffusion model architecture for synthesizing realistic annotated agricultural data, without any human intervention. We employ super-resolution to enhance the phenotypic characteristics of the synthesized images and their coherence with the corresponding generated masks. We showcase the utility of the proposed method for wheat head segmentation. The high quality of synthesized data underscores the effectiveness of the proposed methodology in generating image-mask pairs. Furthermore, models trained on our generated data exhibit promising performance when tested on an external, diverse dataset of real wheat fields. The results show the efficacy of the proposed methodology for addressing data scarcity for semantic segmentation tasks. Moreover, the proposed approach can be readily adapted for various segmentation tasks in precision agriculture and beyond.

Mikolaj Cieslak, Umabharathi Govindarajan, Alejandro Garcia et al.

We present a specialized procedural model for generating synthetic agricultural scenes, focusing on soybean crops, along with various weeds. This model is capable of simulating distinct growth stages of these plants, diverse soil conditions, and randomized field arrangements under varying lighting conditions. The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data. Our dataset includes 12,000 images with semantic labels, offering a comprehensive resource for computer vision tasks in precision agriculture, such as semantic segmentation for autonomous weed control. We validate our model's effectiveness by comparing the synthetic data against real agricultural images, demonstrating its potential to significantly augment training data for machine learning models in agriculture. This approach not only provides a cost-effective solution for generating high-quality, diverse data but also addresses specific needs in agricultural vision tasks that are not fully covered by general-purpose models.

Limin Chuan, Jingjuan Zhao, Shijie Qi et al.

From the perspective of project and paper datasets, research frontier recognition in the field of agricultural resources and the environment using the Latent Dirichlet Allocation (LDA) topic extraction model was studied. By combining the wisdom of domain experts to judge the similarities and differences of clustering topics between the two data sources, multidimensional indicators, such as the emerging degree, attention degree, innovation degree, and intersection degree, were comprehensively constructed for frontier identification. The methods for hot research frontiers, emerging research frontiers, extinction research frontiers, and potential research frontiers were proposed. The empirical research in the field of agricultural resources and the environment showed that the “interaction mechanism of plant–rhizosphere–microbial diversity” was a hot research frontier in the years 2016–2021. The themes of “wastewater treatment technology and efficient utilization of water resources”, the “value-added utilization of agricultural wastes and sustainable development”, the “soil ecological response mechanism under agronomic management measures”, and the “mechanism of soil landslide, erosion, degradation and prediction evaluation” were judged as potential research frontiers. The theme of “ecosystems management and pollution control of agricultural and animal husbandry” was recognized as an emerging research frontier. The results confirm that the fusion method of extracting topics from project and paper data, combined with expert intelligence and frontier indicators for fine classification of frontiers, is an optional approach. This study provides strong support for accurately identifying the forefront of scientific research, grasping the latest research progress, efficiently allocating scientific and technological resources, and promoting technological innovation.