Hasil untuk "Agriculture (General)"

L. Jeppson, H. Keifer, E. Baker

In this timely volume the authors provide an authoritative digest of all the available information on the mites (Acarina) known to produce injury to plants of economic importance. They review our present knowledge of the general systematics, biology, and distribution of the world's phytophagous mites, and of their biological enemies, their role in the transmission of plant diseases, and methods for their control. Modern theories concerning the factors influencing the population development of mites and their resistance to pesticides are also reviewed. The authors all world authorities have also presented hitherto unpublished information in each of their specialized fields of activity. Taxonomic keys to the economic species and a complete key to all genera of the Eriophyoidae are included. Lee R. Jeppson is a member of the Department of Entomology at the University of California, Riverside. Hartford H. Keifer recently retired as Program Supervisor of the Insect Identification Laboratory, California Department of Agriculture, Sacramento. Edward W. Baker is a research entomologist at the U.S. Department of Agriculture, Beltsville, Maryland. 528 pages £13.75

D. Slaughter, D. Giles, D. Downey

J. F. Velasco-Muñoz, J. F. Mendoza, J. A. Aznar-Sánchez et al.

Abstract In the current context of resource scarcity, global climate change, environmental degradation, and increasing food demand, the circular economy (CE) represents a promising strategy for supporting sustainable, restorative, and regenerative agriculture. A review of the literature on CE confirms the initial hypothesis that the theoretical CE framework has not yet been adapted to the field of agriculture. Therefore, this paper overcomes this gap in two ways: i) by adjusting the general CE framework to the agricultural sector's specificities, and ii) by analyzing the scope of the indicators available for measuring agricultural production systems’ circularity performance in supporting decision-making processes. Accordingly, the different elements in the theoretical CE framework are adapted to agricultural production systems. One major contribution of this paper is the definition of CE applied to agriculture. In addition, the principles of CE are adapted to the field, and CE strategies for agricultural activity are defined. Forty-one circularity indicators for application in agricultural systems were also comprehensively assessed to determine their strengths and weaknesses. Building on the key findings, future research paths and changes at the institutional and normative levels are proposed to facilitate CE implementation in agricultural production systems. For example, internationally recognized standards and adequate units of measurement must be defined, to develop meaningful studies and determine agricultural activities’ circularity performance.

D. Cordell, S. White

This paper reviews the latest information and perspectives on global phosphorus scarcity. Phosphorus is essential for food production and modern agriculture currently sources phosphorus fertilizers from finite phosphate rock. The 2008 food and phosphate fertilizer price spikes triggered increased concerns regarding the depletion timeline of phosphate rock reserves. While estimates range from 30 to 300 years and are shrouded by lack of publicly available data and substantial uncertainty, there is a general consensus that the quality and accessibility of remaining reserves are decreasing and costs will increase. This paper clarifies common sources of misunderstandings about phosphorus scarcity and identifies areas of consensus. It then asks, despite some persistent uncertainty, what would it take to achieve global phosphorus security? What would a ‘hard-landing’ response look like and how could preferred ‘soft-landing’ responses be achieved?

Yanbo Huang, Zhongxin Chen, Tao Yu et al.

Abstract Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and analysis results daily from the platforms of satellites, manned/unmanned aircrafts, and ground-based structures. Agricultural remote sensing is one of the backbone technologies for precision agriculture, which considers within-field variability for site-specific management instead of uniform management as in traditional agriculture. The key of agricultural remote sensing is, with global positioning data and geographic information, to produce spatially-varied data for subsequent precision agricultural operations. Agricultural remote sensing data, as general remote sensing data, have all characteristics of big data. The acquisition, processing, storage, analysis and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. This paper overviews available remote sensing data resources, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture. A five-layer-fifteen-level (FLFL) satellite remote sensing data management structure is described and adapted to create a more appropriate four-layer-twelve-level (FLTL) remote sensing data management structure for management and applications of agricultural remote sensing big data for precision agriculture where the sensors are typically on high-resolution satellites, manned aircrafts, unmanned aerial vehicles and ground-based structures. The FLTL structure is the management and application framework of agricultural remote sensing big data for precision agriculture and local farm studies, which outlooks the future coordination of remote sensing big data management and applications at local regional and farm scale.

Sue Han Lee, H. Goëau, P. Bonnet et al.

The control of plant diseases is a major challenge to ensure global food security and sustainable agriculture. Several recent studies have proposed to improve existing procedures for early detection of plant diseases through modern automatic image recognition systems based on deep learning. In this article, we study these methods in detail, especially those based on convolutional neural networks. We first examine whether it is more relevant to fine-tune a pre-trained model on a plant identification task rather than a general object recognition task. In particular, we show, through visualization techniques, that the characteristics learned differ according to the approach adopted and that they do not necessarily focus on the part affected by the disease. Therefore, we introduce a more intuitive method that considers diseases independently of crops, and we show that it is more effective than the classic crop-disease pair approach, especially when dealing with disease involving crops that are not illustrated in the training database. This finding therefore encourages future research to rethink the current de facto paradigm of crop disease categorization.

D. Satterthwaite

A. Alam, M. S. Bhat, M. Maheen

Land use and land cover (LULC) change has been one of the most immense and perceptible transformations of the earth’s surface. Evaluating LULC change at varied spatial scales is imperative in wide range of perspectives such as environmental conservation, resource management, land use planning, and sustainable development. This work aims to examine the land use and land cover changes in the Kashmir valley between the time periods from 1992–2001–2015 using a set of compatible moderate resolution Landsat satellite imageries. Supervised approach with maximum likelihood classifier was adopted for the classification and generation of LULC maps for the selected time periods. Results reveal that there have been substantial changes in the land use and cover during the chosen time periods. In general, three land use and land cover change patterns were observed in the study area: (1) consistent increase of the area under marshy, built-up, barren, plantation, and shrubs; (2) continuous decrease in agriculture and water; (3) decrease (1992–2001) and increase (2001–2015) in forest and pasture classes. In terms of the area under each LULC category, most significant changes have been observed in agriculture (−), plantation (+), built-up (+), and water (−); however, with reference to percent change within each class, the maximum variability was recorded in built-up (198.45%), plantation (87.98%), pasture (− 71%), water (− 48%) and agriculture (− 28.85%). The massive land transformation is largely driven by anthropogenic actions and has been mostly adverse in nature, giving rise to multiple environmental issues in the ecologically sensitive Kashmir valley.

Lei Wang, Chuan-chuan Ning, Taowen Pan et al.

The demand for agricultural crops continues to escalate with the rapid growth of the population. However, extreme climates, pests and diseases, and environmental pollution pose a huge threat to agricultural food production. Silica nanoparticles (SNPs) are beneficial for plant growth and production and can be used as nanopesticides, nanoherbicides, and nanofertilizers in agriculture. This article provides a review of the absorption and transportation of SNPs in plants, as well as their role and mechanisms in promoting plant growth and enhancing plant resistance against biotic and abiotic stresses. In general, SNPs induce plant resistance against stress factors by strengthening the physical barrier, improving plant photosynthesis, activating defensive enzyme activity, increasing anti-stress compounds, and activating the expression of defense-related genes. The effect of SNPs on plants stress is related to the physical and chemical properties (e.g., particle size and surface charge) of SNPs, soil, and stress type. Future research needs to focus on the “SNPs–plant–soil–microorganism” system by using omics and the in-depth study of the molecular mechanisms of SNPs-mediated plant resistance.

C. Larsen

Clifton Makate, Rongchang Wang, Marshall Makate et al.

This paper demonstrates how crop diversification impacts on two outcomes of climate smart agriculture; increased productivity (legume and cereal crop productivity) and enhanced resilience (household income, food security, and nutrition) in rural Zimbabwe. Using data from over 500 smallholder farmers, we jointly estimate crop diversification and each of the outcome variables within a conditional (recursive) mixed process framework that corrects for selectivity bias arising due to the voluntary nature of crop diversification. We find that crop diversification depends on the land size, farming experience, asset wealth, location, access to agricultural extension services, information on output prices, low transportation costs and general information access. Our results also indicate that an increase in the rate of adoption improves crop productivity, income, food security and nutrition at household level. Overall, our results are indicative of the importance of crop diversification as a viable climate smart agriculture practice that significantly enhances crop productivity and consequently resilience in rural smallholder farming systems. We, therefore, recommend wider adoption of diversified cropping systems notably those currently less diversified for greater adaptation to the ever-changing climate.

K. V. Van Meter, N. Basu, J. Veenstra et al.

Watershed and global-scale nitrogen (N) budgets indicate that the majority of the N surplus in anthropogenic landscapes does not reach the coastal oceans. While there is general consensus that this ‘missing’ N either exits the landscape via denitrification or is retained within watersheds as nitrate or organic N, the relative magnitudes of these pools and fluxes are subject to considerable uncertainty. Our study, for the first time, provides direct, large-scale evidence of N accumulation in the root zones of agricultural soils that may account for much of the ‘missing N’ identified in mass balance studies. We analyzed long-term soil data (1957–2010) from 2069 sites throughout the Mississippi River Basin (MRB) to reveal N accumulation in cropland of 25–70 kg ha−1 yr−1, a total of 3.8 ± 1.8 Mt yr−1 at the watershed scale. We then developed a simple modeling framework to capture N depletion and accumulation dynamics under intensive agriculture. Using the model, we show that the observed accumulation of soil organic N (SON) in the MRB over a 30 year period (142 Tg N) would lead to a biogeochemical lag time of 35 years for 99% of legacy SON, even with complete cessation of fertilizer application. By demonstrating that agricultural soils can act as a net N sink, the present work makes a critical contribution towards the closing of watershed N budgets.

Mohammadreza Haghighat, Alzayat Saleh, Mostafa Rahimi Azghadi

Understanding field imagery such as detecting plants and distinguishing individual crop and weed instances is a central challenge in precision agriculture. Despite progress in vision-language tasks like captioning and visual question answering, Visual Grounding (VG), localising language-referred objects, remains unexplored in agriculture. A key reason is the lack of suitable benchmark datasets for evaluating grounding models in field conditions, where many plants look highly similar, appear at multiple scales, and the referred target may be absent from the image. To address these limitations, we introduce gRef-CW, the first dataset designed for generalised visual grounding in agriculture, including negative expressions. Benchmarking current state-of-the-art grounding models on gRef-CW reveals a substantial domain gap, highlighting their inability to ground instances of crops and weeds. Motivated by these findings, we introduce Weed-VG, a modular framework that incorporates multi-label hierarchical relevance scoring and interpolation-driven regression. Weed-VG advances instance-level visual grounding and provides a clear baseline for developing VG methods in precision agriculture. Code will be released upon acceptance.

Övgü Gencer

The blue crab (Callinectes sapidus, Rathbun 1896) has become a significant source of raw materials in biotechnology and nanotechnology due to the biomaterials present in its shell. Natural polymers such as chitin and chitosan, derived from the crab's shell, are particularly noteworthy for their environmentally friendly and biologically compatible properties. These biopolymers provide an innovative alternative in the synthesis of quantum dots (QDs). Quantum dots are favored in various applications, including biomedical imaging, environmental sensors, and energy storage, due to their superior optoelectronic properties. Chitosan obtained from blue crab shells acts as both a stabilizer and a coating agent in the green synthesis of quantum dots. This process minimizes the use of toxic chemicals, thus promoting environmental sustainability. Moreover, the antimicrobial and biodegradable properties of chitosan enhance its usability in biomedical applications. For instance, biocompatible carbon-based quantum dots have shown promising results in cancer diagnostics and drug delivery systems. The synthesis of quantum dots using biomaterials is more cost-effective and environmentally friendly compared to traditional methods. Furthermore, utilizing blue crab shells as a waste material contributes to both marine ecosystem preservation and the circular economy. These synthesis methods are reported to create a significant paradigm shift in the field of sustainable technology development. In conclusion, the synthesis of quantum dots using biomaterials derived from blue crabs has the potential to reduce environmental impacts while serving advanced technological applications. This approach significantly contributes to the development of biotechnological innovations and sustainable development goals.

Santu Mondal, Sneha Ray, Aritra Acharyya et al.

This work investigates the application of artificial neural network (ANN)-based regression models to predict the static and dynamic characteristics of GaN impact avalanche transit time (IMPATT) sources in the terahertz (THz) frequency regime. A comprehensive dataset, derived from self-consistent quantum drift-diffusion (SCQDD) simulations of GaN IMPATT structures designed for a wide frequency range from the microwave frequency bands, up to 5 THz, is used to train the ANN models. The models effectively capture the impact of variations in structural, doping, and biasing parameters on device performance. The proposed ANN approach significantly reduces computational time for predicting breakdown characteristics, power output, and conversion efficiency properties of IMPATT sources, achieving similar accuracy to traditional SCQDD simulations while requiring only 7.8&#x2013;20.1% of the computational time. Mean square errors are observed to be on the order of <inline-formula> <tex-math notation="LaTeX">$10^{-4}$ </tex-math></inline-formula>&#x2013;<inline-formula> <tex-math notation="LaTeX">$10^{-6}$ </tex-math></inline-formula>, demonstrating the models&#x2019; high accuracy. Experimental validation shows strong agreement in terms of breakdown voltage, power output, and efficiency, supporting the potential of machine learning to streamline the design and optimization of high-frequency semiconductor devices.

Amr S. Mohamed, Emily Nguyen, Deepa Kundur

Amidst the growing demand for implementing advanced control and decision-making algorithms|to enhance the reliability, resilience, and stability of power systems|arises a crucial concern regarding the safety of employing machine learning techniques. While these methods can be applied to derive more optimal control decisions, they often lack safety assurances. This paper proposes a framework based on control barrier functions to facilitate safe learning and deployment of reinforcement learning agents for power system control applications, specifically in the context of automatic generation control. We develop the safety barriers and reinforcement learning framework necessary to establish trust in reinforcement learning as a safe option for automatic generation control - as foundation for future detailed verification and application studies.

Simar Ghumman, Fabio Di Troia, William Andreopoulos et al.

Unmanned Ground Vehicles (UGVs) are emerging as a crucial tool in the world of precision agriculture. The combination of UGVs with machine learning allows us to find solutions for a range of complex agricultural problems. This research focuses on developing a UGV capable of autonomously traversing agricultural fields and capturing data. The project, known as AGRO (Autonomous Ground Rover Observer) leverages machine learning, computer vision and other sensor technologies. AGRO uses its capabilities to determine pistachio yields, performing self-localization and real-time environmental mapping while avoiding obstacles. The main objective of this research work is to automate resource-consuming operations so that AGRO can support farmers in making data-driven decisions. Furthermore, AGRO provides a foundation for advanced machine learning techniques as it captures the world around it.

Mihir Gupta, Abhay Mangla, Ross Greer et al.

Precision agriculture relies heavily on accurate image analysis for crop disease identification and treatment recommendation, yet existing vision-language models (VLMs) often underperform in specialized agricultural domains. This work presents a domain-aware framework for agricultural image processing that combines prompt-based expert evaluation with self-consistency mechanisms to enhance VLM reliability in precision agriculture applications. We introduce two key innovations: (1) a prompt-based evaluation protocol that configures a language model as an expert plant pathologist for scalable assessment of image analysis outputs, and (2) a cosine-consistency self-voting mechanism that generates multiple candidate responses from agricultural images and selects the most semantically coherent diagnosis using domain-adapted embeddings. Applied to maize leaf disease identification from field images using a fine-tuned PaliGemma model, our approach improves diagnostic accuracy from 82.2\% to 87.8\%, symptom analysis from 38.9\% to 52.2\%, and treatment recommendation from 27.8\% to 43.3\% compared to standard greedy decoding. The system remains compact enough for deployment on mobile devices, supporting real-time agricultural decision-making in resource-constrained environments. These results demonstrate significant potential for AI-driven precision agriculture tools that can operate reliably in diverse field conditions.

Eluana Domingues Gonçalves, Eloisa Borchardt de Araújo, Lucas Felipe Alves de Araújo et al.

Associations between different groups of sweet corn traits enable both the direct and indirect selection of plants, thus increasing the chances of success in breeding programs. This study aimed to estimate the relationships between vegetative and productive traits, as well as genotypic values, using canonical correlations and mixed models. The experiment was carried out in a randomized block design, with ten genotypes and four replications. The following traits were assessed: plant height, main ear insertion height, yield of ears with and without straw, grain mass, ear length, ear diameter and percentage of commercial ears. The significant correlations obtained in the first canonical pair indicate that an increase in height and main ear insertion height result in a decrease in the percentage of commercial ears and yield of ears without straw, being necessary to select plants with plant height values of less than 2.0 m and first ear insertion height of less than 1.0 m to increase them. It was observed that the plant height and main ear insertion height have the highest heritability, indicating the possibility of genetic gain from the artificial selection.

Jing Lu, Alexandra Garzon-Garcia, Ann Chuang et al.

Point- and non-point source nutrients are likely to have different ecological impacts in receiving waters, due to differences in the concentration and proportions of nutrient fractions. However, the direct comparison of their ecological impacts in receiving waters has barely been quantified. We undertook algal bioassays with algal communities from river sites and showed that there was a photosynthetic yield (Fv/Fm) response to nutrient enrichment when river nutrient concentrations were relatively low, but not at higher nutrient concentrations. To combat this variability in the photosynthetic state of algae, we developed a standardized algal bioassay (3-day), using a cultured species of algae which was starved of nitrogen, to compare the photosynthetic response to three nitrogen sources: treated wastewater, aquaculture farm discharges, and soil erosion-derived nutrient sources. This study showed that the nutrient parameter that had the highest correlation with algal photosynthetic response was total dissolved nitrogen (TDN), i.e., the sum of dissolved inorganic and organic nitrogen, rather than dissolved inorganic nitrogen alone. This was true across all three nutrient sources (R2 = 0.58–0.78). Additionally, the same concentrations of TDN from soil erosion-derived and aquaculture samples resulted in a significantly higher algal photosynthetic response, compared to the treated wastewater. This indicates that TDN from soils and aquaculture farms was significantly more bioavailable to the cultured algae than treated wastewater. When a range of parameters were correlated with algal responses, organic and inorganic nutrients, and organic carbon were the parameters that had the highest explanatory power for soil erosion-derived and aquaculture samples (R2 = 0.75–0.87). The importance of organic compounds in these equations points to the potential of microbial transformation of organic nutrients into more bioavailable forms during the 3-day bioassay. This highlights the need to understand the relationship between algal and microbial communities in natural systems for nutrient source impact assessment. This study provides an improved understanding and metrics for comparing the algal growth response to different nutrient sources.