Agricultural Fields Research Articles

Distribution, sources, and risk assessment of polycyclic aromatic hydrocarbons in surface soils and plants from industrial and agricultural areas, Junggar Basin, Xinjiang

The contamination characteristics of Polycyclic Aromatic Hydrocarbons (PAHs) in different environmental functional areas are different. In this study, the contamination of PAHs in soils and common plants in typical mining and farmland areas in Xinjiang, China, was analyzed. The results showed that the contamination levels of PAHs in mining soils were significantly higher than those in farmland soils, and the mining soils were dominated by 4-5-ring PAHs and farmland soils by 3-4-ring PAHs. Analysis of their sources using a positive definite factor matrix model showed that PAHs in mining soils mainly originated from coal and natural gas combustion, and transportation processes; while farmland soils mainly came from biomass and coal combustion, and fossil fuel volatile spills. The cancer risk of PAHs in soils was evaluated using a combination of the Monte Carlo and the lifetime carcinogenic risk models, and the results showed that the overall level of cancer risk for mining soils was higher than that for farmland soils, and can put some people in high risk of cancer. For plant samples, except for individual crop samples, the contamination levels of mining plants and crops were similar, with 4-5-ring PAHs dominating in desert plants in mining areas and the highest proportion of 3-ring PAHs in crops in agricultural fields, and PAHs in both plants were mainly from biomass and coal combustion. The results of correlation analysis showed that 2-ring PAHs in crop roots were significantly positively correlated with it in corresponding soils, and some high-ring PAHs in crop leaves were significantly negatively correlated with it in corresponding soils. Therefore, there were significant differences in the pollution characteristics of PAHs in soils and common plants in mining and agricultural areas. Human health risks and ecological risks are mainly concentrated in mining areas, and appropriate intervention measures should be taken for pollution remediation.

水射流技术在农业领域的应用研究现状

Water jet technology, as a non-contact cutting and crushing technique, is commonly used in industries such as cleaning, rust removal, cutting, drilling, and mining. It enables a green and environmentally friendly production process by avoiding environmental pollution, heat generation, blade replacement, and sharpening issues. Cutting, cleaning, and crushing techniques are widely applied in various stages of agricultural production, but mostly through contact-based methods. The application of non-contact water jet technology in the agricultural field is still in its infancy. This paper summarized the current application status and research progress of water jet technology in different operational stages of agriculture, including sowing, management, field harvesting, and post-harvest processing. The specific requirements of key parameters for different operational objects and stages were analysed. Furthermore, the challenges encountered in the application of water jet technology in agriculture and provides prospects for future research directions were discussed. The review is intended to provide references for the promotion and green sustainable development of water jet technology in the agricultural field.

Local Wisdom and Economic-Conservation Strategy in Communities Affected by Natural Disasters

Community interaction with the natural environment is reflected through the local wisdom implemented by the community. This is not free from the parties' interests in natural resources. Exploitative behavior or otherwise maintaining ecological and socio-economic balance in the natural environment arises from the various interests of the parties. Such conditions raise the question: How can society's socio-economic strategies be strengthened so that harmonious relations between humans and nature are maintained through a balance of ecological-conservation and socio-economic interests? This research uses a qualitative approach by collecting data through interviews and observation. The research results show that people living in areas prone to natural disasters in the form of earthquakes and landslides have a pattern of relationship with their natural environment in socio-economic activities. Efforts to escape the impact of natural disasters are experiencing obstacles because the community's socio-economic strategies and activities rely on agriculture and plantations which are directly related to the condition of natural resources. In this situation, it is necessary to improve and strengthen community economic activities in the fields of agriculture, plantations and collaboration at the community level.

Jasmine Flower Classification with CNN Architectures: A Comparative Study of NasNetMobile, VGG16, and Xception in Agricultural Technology

Jasmine flowers have many benefits and uses such as for traditional medicine, tea, perfume, cosmetics, decoration, and others. in the selection of fresh jasmine flowers for making tea is very important, currently the classification of jasmine flowers for making tea is mostly still using manual methods. Often influenced by individual preferences, opinions, or biases. this causes a lack of objectivity and uncertainty in the classification of jasmine flowers. The manual method is very weak due to human visual limitations and fatigue levels which can result in less than the optimal jasmine flower classification. Therefore, in the research that has been done, a transfer learning system was applied that can classify fresh jasmine flowers with rotten jasmine flowers. This study aims to compare three different Convolutional Neural Network architectures: NasNetMobile, VGG16, and Xception. The results on the three architectures can show maximum results, namely 99.21% for NasNetMobile, 98.69% for VGG16 and 97.91% for Xception. This study provides insight into the classification of good and bad jasmine flowers to encourage further exploration in the field of agriculture.

Factors influencing shorebird use of post‐harvest flooded rice fields in California's Sacramento Valley

AbstractBecause of the importance of the Central Valley of California, USA, to shorebirds along the Pacific Flyway, conservation investments have been made in the area's agricultural fields and managed wetlands. Increasingly, landowner incentive programs are being used to deliver shorebird habitat, presenting opportunities to answer remaining questions about which implementation strategies and field management practices are most effective at attracting birds. To provide management guidance for these investments, we collected and analyzed 5 years of data (2014–2018) on shorebird abundance in flooded rice fields enrolled in a dynamic habitat incentive program. The program incentivized flooding in fallow and post‐harvest rice fields in seasons when habitat is relatively sparse, specifically the early fall and late spring. Across nearly 9,000 field observations over 5 years, we explored the relationship between abundance and density (birds/ha) of shorebirds and vegetation cover, soil clay, landscape‐level flooding, and local flood timing, duration, and depth. We observed more shorebirds in fields that were approximately 50% flooded, had water depths of 5–10 cm, and had minimal rice straw or stubble cover, with strong or very strong evidence for each of these relationships. We found that the timing of habitat provisioning was important, with moderate evidence that earlier fall flooding and strong evidence that the duration of fall flooding was associated with higher shorebird density. We observed lower shorebird densities in locations with ample flooded rice habitat in surrounding areas, potentially because shorebirds spread out across the landscape. We found very strong evidence that flooding consistency, either at a site that was continually flooded over many months or a site that had been flooded in previous years, was associated with higher shorebird density. Soil clay content was associated with decreased observed shorebird density, potentially through its influence on the ability of shorebirds to forage for invertebrate prey. These results suggest best practices for shorebird habitat creation in agricultural landscapes, providing important information for conservation and population recovery.

Synergistic Strategies for the Management of Thrips, Scirtothrips dorsalis in Pomegranate Ecosystem

Thrips management in pomegranate cultivation is critical due to the significant damage these pests can inflict on fruit yield and quality. The study was carried out in farmers field at Managuli, Vijayapura, College of Agriculture, Vijayapura, Karnataka during 2020-2021. The integrated management of thrips, Scirtothrips dorsalis in pomegranate revealed that the in-situ vermiculture technique along with chemicals (86.93 per cent) and botanicals (59.40 %) found to be more effective in controlling thrips than in-situ vermiculture technique alone (39.22 %). It also revealed that in-situ vermiculture technique influencing only 2-5 % pest reduction along with chemical insecticides. Whereas, in-situ vermiculture technique along with botanical influencing 15-20 % pest reduction and it mainly due to compatibility and positive effect of botanicals on earthworms than chemicals. However, all the treatments recorded higher yield and found significantly superior over untreated control (5.34 t/ha). By integrating these methods, pomegranate growers can achieve effective thrips control, leading to healthier crops, improved yields while promoting sustainable agricultural practices and minimizing environmental impact.

Modern methods for determining heavy metals in soil

This article discusses the problem of heavy metal detection in soil and its impact on vegetation. Based on the experience of foreign and domestic research, this article discusses global fundamental problems and challenges, modern methods of heavy metal detection, as well as prospects for further research and new challenges facing the scientific community. The aim of the study is to identify modern and established methods for the detection of heavy metals in soil, such as spectral analysis methods and reflectance spectra of plant parts. The review summarizes the results of experimental studies confirming the effectiveness of the combined sampling and spectrometry method for estimating the concentration of heavy metals in soil, as well as the feasibility of using plant reflectance spectra to measure pollution. World experience confirms the expediency of using spectral approaches to determine heavy metals in soil and analyze their impact on vegetation. The results of the research have practical application in the field of ecology, agriculture and nature protection, allow effectively controlling the level of heavy metal pollution and taking measures for its elimination.

Agricultural path detection systems using Canny-edge detection and Hough transform

Navigation is one of the crucial aspects of automation technology within the field of agriculture, such as robotics systems or autonomous agricultural vehicles. Despite many navigation systems having been developed for agricultural land, due to their high development and component costs, these systems are difficult to access for farmers or organizations with limited capital. In this study, the Canny-edge detection and Hough transform methods are implemented in a path detection system on agricultural land to find an alternative, cost-effective navigation system for autonomous farming robots or vehicles. The system is tested on ground-level view images, which are captured from a low perspective and under three different lighting conditions. The testing and experimentation process involves adjusting the parameters of the Canny-edge detection and Hough transform methods for different lighting conditions. Subsequently, an evaluation is conducted using Intersection over Union to obtain the best accuracy results, followed by fine-tuning of the canny-edge detection and Hough transform method parameters. The identified parameters, specifically a 15×15 Gaussian kernel, low threshold of 50, high threshold of 150, Hough threshold, minimum line length of 150, and maximum line gap, have been discerned as optimal for the canny-edge and Hough transform algorithms under medium lighting conditions (G=1.0). The observed efficacy of these parameter configurations suggests the method’s viability for implementation in path detection systems for agricultural vehicles or robots. This underscores its potential to deliver reliable performance and navigate seamlessly across diverse lighting scenarios within the agricultural context.

PARTICIPATION OF WOMEN AND GIRLS IN THE DEVELOPMENT OF NATURAL SCIENCES IN TASHKENT CITY STATE HIGHER EDUCATION INSTITUTIONS

This article sheds light on the invaluable contribution to science of some of the most mature women scientists working in the state higher education institutions in the field of agriculture and biology in Uzbekistan. Through a comprehensive analysis of their research, innovation, and experience, it sheds light on their remarkable achievements in the face of social challenges. Today, important initiatives are being put forward in the academic circles of the country to increase the place and position of women, to educate the new generation of young women scientists, and to encourage their promising projects. Ensuring gender equality in various aspects of public life, including in the field of education and science, is one of the priorities of the internal political concept of Uzbekistan. It aims to recognize the important role of women in the development of these areas by studying the historical context, current situation, problems and future prospects.

Crop Pest Detection using Convolutional Neural Network

Pests in plants can cause significant losses in agricultural production. As a result, various technologies are used nowadays to improve agriculture's efficiency and make it more sustainable. This research highlights the contribution of machine learning algorithms and image recognition technologies for pest identification. Farmers can use the system to recognize pests and take the necessary actions to reduce them. Convolutional Neural Networks (CNN) is used in this study for image recognition tasks, including pest identification in agricultural fields. The algorithm is trained using the Agricultural Pests Dataset acquired from Kaggle. The experiment results showed that the CNN performed better than the other state-of-the-art machine learning models, with a much lower false rejection rate of 0.12% and an accuracy of 99%.

Application of Image Recognition Technology Using Deep Learning to the Medical and Agricultural Fields

Application of Image Recognition Technology Using Deep Learning to the Medical and Agricultural Fields

Microplastic fate in a chronosequence of biosolid‐amended agricultural soil in Southern Ontario, Canada

AbstractMunicipally sourced biosolids are commonly used as cost‐effective fertilizers, diverting material from landfills and contributing to the circular economy. However, biosolids contain high concentrations of microplastics (MPs), which are emerging contaminants of concern due to their ubiquity in the environment. Despite this, there is a lack of environmentally relevant field studies. In 2022, composite topsoil samples (0–15 cm depth) were collected from seven agricultural fields in Southern Ontario, Canada, representing a chronosequence of biosolid applications ranging from 1 to 9 years since amendment and a control (untreated) field. MP particles down to 20 μm in size were extracted by density separation, enumerated, characterized by stereomicroscope and polymers identified using attenuated total reflectance Fourier transform infrared (ATR‐FTIR) spectroscopy. Here, we report on the characteristics, abundance and polymer type of MP particles in the study area to assess their fate in biosolid‐amended soils. The average MP concentration among fields was 6.87 ± 1.47 MP g−1 (3.43 ± 0.74 mg MP kg−1). Additionally, the MP soil pool increased with repeated applications of biosolids. The dewatered biosolid plastic content of 8816 ± 1809 MP g−1 dry weight (11.6 ± 17.5 g MP kg−1 dry weight) was used to estimate a mean MP loading of 94.5 ± 10.9 kg ha−1 to each field per application, suggesting that 7% of the MP soil pool persisted over time. Quantifying the MP pool in biosolid‐amended agricultural soil will inform evidence‐based plastic policy changes in our global effort to understand and reduce plastic pollution.

A Survey on Crop Management in Smart Agriculture

The agricultural sector's importance is highlighted by the upcoming global population growth predicted by the food and agriculture organization. This growth will lead to increased food demand. However, traditional farming methods require a lot of time and effort and result in diminishing returns. Recent technological advancements, especially in the Internet of Things (IoT), are changing this landscape by introducing smart agriculture. Smart agriculture aims to improve decision-making and crop management using IoT's connectivity and data analysis capabilities. Artificial intelligence (AI) is becoming crucial in this context, offering speed, accuracy, and cost-effectiveness. Machine learning, a type of AI, is beneficial in detecting yields and forecasting weather. This survey highlights the evolving field of intelligent agriculture, covering topics like crop monitoring, and anomaly detection, and explains the main challenges in farming practices.

Smart agriculture model in detecting oil palm plantation diseases using a convolution neural network

Planning models for sustainable crop care in the context of smart agriculture are complex issues as they involve many factors such as productivity, quality, growth sustainability, workforce use, and information technology use. In this study, we will create an optimized model using a convolution neural network (CNN) that can classify and monitor plant diseases. Part of the plant care system is to be aware of plant diseases and to be able to deal with them immediately. This study aims to acquire a new smart farming model for integrated crop care. The results of this research are findings in the form of a CNN model for classifying plant diseases detected from the leaves of the plants studied in oil palm. Testing using Google Colab obtains 100% accuracy and 99% accuracy using a teachable machine. The contributions of this paper create a new model in the field of informatics, especially in the field of intelligent agriculture based on information technology.

Applications of Silver Nanoparticles in Patent Research.

Silver nanoparticles (AgNPs) have been widely applied in research and industrial fields, finding applications in nanomedicine, drug delivery, biomedical devices, electronics, the energy sector, and environmental protection. Patents provide information on the industrial viability of product technologies, and the number of patent documents provides an estimate of the evolution of a specific technological field. The present work aims to describe the current trends in AgNPs patent applications. In addition, a retrospective study of published patents in Brazil is presented. Analyses of AgNPs-related patents were conducted using the free platform for patent search Lens® in 2010-2019 and articles published in same period using the Scholar® base. The patent applications and their evolution over time, major depositors and holders, and the main technological areas associated with AgNP applications have been described. China and United States are the major patent applicants for nanotechnologies. The worldwide distribution of publications of journal articles shows that China, India, and the United States are the leading countries in the total number of articles published, in that order. Our study of patent applications and published articles confirmed the growing global increase in new technologies involving NPs and AgNPs, particularly in the biotechnology area, in the fields of medicine and agriculture.

The Wear Behaviour of a New Eccentric Meshing Reducer with Small Teeth Difference

Eccentric meshing reducers are widely used in agriculture, industrial robots, and other fields due to their ability to achieve a high reduction ratio within a compact volume. However, the contact wear problem seriously affects the service performance of the eccentric meshing reducer, thereby limiting their range of applications. To effectively address this issue, this study involved a stress analysis of the contact pairs and a surface wear analysis of a new eccentric meshing reducer. The wear equation for the contact pairs was derived using Archard’s wear theory, incorporating geometric and material parameters from both the reducer gear contact pair and the spline contact pair. In parallel, a wear simulation was conducted by integrating the UMESHMOTION subprogram with ALE adaptive grids. Additionally, the effects of load amplitudes on contact pair stress and surface wear were systematically investigated. It is revealed that the contact pair stress of the reducer gear was higher than that of the spline contact pair. Furthermore, the internal spline exhibited the highest wear rate, followed by the output shaft gear, external spline, and input shaft gear, in that order. This work provides a comprehensive and in-depth understanding of the wear behaviors of general reducers with small teeth differences and offers valuable scientific references for design optimization, fault diagnosis, and maintenance strategy formulation.

Identification of Indigenous Bacterial Strains from Thai Agricultural Fields for Potential Bioremediation of Carbofuran

This study explored the bioremediation potential of indigenous soil bacteria for carbofuran-contaminated agricultural areas. Twenty soil samples from regions with a history of pesticide use in Pathum Thani and Nakhon Pathom provinces, Thailand, were collected. Five bacterial strains capable of degrading carbofuran were isolated and subjected to cell morphology and biochemical analyses. Phylogenetic analysis of the 16S rRNA gene sequences identified these strains as related to Pseudomonas and Stenotrophomonas species. High-Performance Liquid Chromatography (HPLC) analysis confirmed the complete degradation of carbofuran within 3 days, reducing its concentration from 0.05 mg/mL to below detectable limits. These bacteria could use carbofuran as their sole carbon source, demonstrating their potential for bioremediation of contaminated soils. Given their resilience and ability to thrive in natural environmental conditions, these indigenous strains are well-suited for in-situ degradation of pollutants. The findings indicate that these isolated soil bacteria present a promising method for reducing the environmental risks associated with carbofuran contamination.

Seasonal Variability of Pesticide Residues in Vellode Bird Sanctuary: Implications for Ecosystem Health

This research provides a comprehensive examination of the seasonal fluctuations in pesticide residues within the Vellode Bird Sanctuary, with a particular emphasis on the detection and quantification of organochlorines, organophosphates, synthetic pyrethroids, and herbicides in the sanctuary's aquatic environment. The pesticide analysis was conducted from July 2019 to May 2020. This study period spanned three seasons: monsoon, winter, and summer. Gas Chromatography-Mass Spectrometry (GC-MS) from Perkin Elmer was used to analyze the pesticide residues in lake water. The pesticide analysis showed that aldrin, α-cypermethrin, α-HCH, β-HCH, bifenthrin, butachlor, chlorpyrifos, dimethoate, endosulfan-α, γ-HCH, pendimethalin, phorate, profenophos, p,p'-DDT, and quinalphos were recorded at higher levels during the monsoon season. The values of pesticide residues in the winter and summer seasons were below the level of detectable quantification (less than 0.5 μg/l). The study shows that increased pesticide concentrations during the monsoon are due to runoff from nearby agricultural fields caused by heavy rainfall. In contrast, lower pesticide levels in winter and summer are likely due to reduced pesticide use and rainfall. These findings highlight the significant impact of seasonal variations on pesticide contamination, with greater ecological risks during the monsoon. The sanctuary's aquatic ecosystem is particularly vulnerable during periods of high rainfall, emphasizing the need for targeted conservation and management strategies.

IoT-based Smart Agriculture Field Monitoring using Arduino Nano

The rapid developments in the Internet of Things (IoT) have enabled even individuals with limited technical knowledge to access real-time reception of information conveniently at their doorstep. Agriculture, one of the necessities for human livelihood, has been significantly enhanced with technological advancements. The study proposes an IoT-based smart agriculture field monitoring system using Arduino Nano. The device provides real-time data on temperature and soil moisture content for cultivating crops like paddy, tapioca, turmeric, etc. The system also allows farmers to get remote access to the irrigation systems, hence improving the management of the fields along with their resources.

Determinants Of Adoption Of Soil Conservation Technologies Among Smallholder Farmers In Izzi Local Government Area Of Ebonyi State, Nigeria

The study examined the determinants of adoption of soil conservation technologies among smallholder farmers in Izzi Local Government Area, Ebonyi State, Nigeria. Multistage sampling technique was used to select 160 respondents for the study. Data collected with the aid of structured questionnaire were analyzed using descriptive and inferential statistics. Results showed that farmers in the area had average age of 47 years and operated on 1.5 hectares of land with an average farm income of ₦152,125. Further analysis revealed that the smallholder farmers had an adoption index of 2.21 which implied that they used at least two (2) soil conservation technologies with an average source of information of 2.72 showing that they got information on soil conservation technologies from at least three different sources. Furthermore, results revealed erosion, deforestation, reduced soil fertility and indiscriminate bush burning as the forms of soil degradation existing in the area while adopted coping strategies were crop rotation, use of organic manure, intercropping practices, appropriate use of fertilizer, use of mixed cropping system and planting of trees. The results of OLS multiple regression analysis showed that the coefficient of multiple determination (R2 ) of the regression model was 0.760. This implies that seventy-six percent (76%) of the variations in level of soil conservation technology adoption were accounted for by the independent variables socio-economic characteristics of the smallholder farmers included in the model. The result of factor analysis identified social, institutional and economic factors as constraints militating against adoption of soil conservation technologies among the smallholder farmers in the study area. The rejected hypothesis concluded that there is a significant relationship between the socioeconomic characteristics of smallholder farmers and their adoption of soil conservation technologies in the area. Based on the findings, the study recommends; that local government authorities and NGOs working in areas that are prone to soil fertility losses should introduce programmes that will train and promote soil conservation technologies to be adopted by farmers to improve productivity; provision of Infrastructural facilities such as good road network to ensure easy movement of agricultural inputs to the farmers field; and Agricultural extension services which are saddled with the responsibility of disseminating timely, complete actionable information should be re-positioned and strengthened to ensure smallholder farmers access to information, through appropriate channels.