Modern Agriculture Research Articles

Stiffness evaluation of semi-rigid connection using steel clamps in plastic greenhouse structure

Greenhouse structures, essential for modern agriculture, often experience significant uncertainties due to varying environmental conditions, leading to frequent damage and economic losses. Accurately analysing the structural responses of these greenhouses is particularly challenging due to the difficulty in understanding the actual behaviour of connections using steel clamps. This study focuses on evaluating the stiffness and mechanical behaviour of semi-rigid connections using steel clamps in plastic greenhouses. A specialised load-deformation testing apparatus was developed to assess the relationships between force and displacement or moment and rotation for these connections with various deformation modes. The experimental results were used to model stiffness coefficients and ultimate limit loads, providing a detailed understanding of the mechanical properties of these connections. Findings reveal that steel clamps introduce complex structural behaviours that differ significantly from traditional connections, highlighting the need for advanced modelling techniques. This comprehensive analysis offers new insights into the behaviour of semi-rigid connections in greenhouse structures and underscores the importance of detailed empirical studies. The research contributes to improving the structural design and safety assessments of agricultural facilities, ensuring better resilience against environmental stresses. The outcomes are crucial for developing more effective and reliable greenhouse designs that can withstand adverse conditions, ultimately supporting sustainable agricultural practices.

Context-aware self-powered intelligent soil monitoring system for precise agriculture

The agricultural sector is transforming with advanced technologies such as internet of things (IoT), cloud computing, and machine learning, for increased productivity and sustainability. However, fixed sensor deployments struggle to capture the dynamic and heterogeneous soil properties with irregularities in farming operations, and negatively impacting crop performance and resource utilization. This paper presents a novel context-aware, self-powered intelligent soil monitoring system (ISMS) applied in precision agriculture. By integrating advanced sensors, energy harvesting, real-time data analytics, and context-aware decision support, ISMS provides real-time context insights into soil, energy, and weather conditions. The informed decisions are enabled and tailored to their specific agricultural environment. The system utilizes a multi-parameter soil sensor, photovoltaic (PV) panel, and intelligent context-aware analytics for a sustainable, cost-effective solution powered by solar energy and OpenWeather application program interface (API) for weather data. Field tests over two months demonstrated the system's effectiveness, together with continuous operation without grid power. This research highlights ISMS's potential in enhancing soil nutrient management and decision-making and offering significant economic and environmental benefits for modern agriculture, especially in remote areas.

Ensemble Techniques in Plant Nutrient Deficiency Detection: A Survey

Abstract: Plant nutrient deficiency detection represents a critical challenge in modern agriculture, significantly im- pacting crop health, yield, and sustainable farming practices. This survey paper presents a comprehensive analysis of ensemble learning techniques for automated detection of plant nutrient deficiencies through image analysis. Our research evaluates a hybrid approach combining MobileNet’s efficient feature extraction capabilities with ensemble methods including Random Forest and Gradient Boosting algorithms. The methodology encompasses three key compo- nents: feature extraction using MobileNet’s lightweight architecture for processing plant leaf images, implementation of individual classification models, and development of ensemble techniques that leverage the strengths of multiple clas- sifiers. Through extensive experimentation and comparative analysis, our findings demonstrate that ensemble methods consistently outperform individual models, achieving superior accuracy in detecting nutrient deficiencies across various plant species. The integration of MobileNet with ensemble techniques provides a robust framework that balances com- putational efficiency with prediction accuracy. This research contributes to the advancement of precision agriculture by proposing a scalable, real-time solution for nutrient deficiency detection that can be practically implemented in field conditions, ultimately supporting improved crop management decisions and agricultural productivity..

The convergence of big data and SRE in modern agriculture: Transforming farming through technology

This article examines the transformative integration of Big Data analytics and Site Reliability Engineering (SRE) principles in modern agriculture. The article explores how precision agriculture leverages advanced technologies including IoT sensors, drone surveillance, and satellite imagery, to revolutionize traditional farming practices. It analyzes the implementation of sophisticated data collection infrastructures, Big Data systems, and SRE practices in agricultural settings. The article investigates the operational benefits and sustainability implications of these technological integrations, highlighting improvements in resource optimization, yield enhancement, and environmental conservation. Furthermore, it discusses future perspectives in agricultural technology, including edge computing applications, AI-driven autonomous systems, blockchain integration, and climate change adaptation strategies.

The development of RBL-STEM learning tools to improve students' computational thinking skills in solving plant disease classification problems using convolutional neural network segmentation

This research aims to develop Research Based Learning (RBL) and Science, Technology, Engineering and Mathematics (STEM) based learning tools to improve students' computational thinking skills. The tools include a student task design (RTM), a student worksheet (LKM) and a learning outcome test (THB). Using Thiagarajan's 4D development model (define, design, develop, disseminate), the device was tested on students using a Convolutional Neural Network (CNN) approach for citrus plant disease classification using data from quadcopter drones. The validation results showed that the device was valid with an average score of 3.85 (96.42%). The practicality of the device is very high with an implementation score of 3.85 (96.36%) and a positive student response of 90.08%. The effectiveness of the device was demonstrated by 90% of students achieving classical completeness on the post-test and an increase in computational thinking skills from 0% (high) on the pre-test to 92% on the post-test. The paired sample t-test results also confirmed the statistically significant increase. This learning tool proved to be valid, practical and effective, contributing to technology-based learning innovation in modern agriculture.

AI in agriculture: Smart greenhouses and indoor farming systems

This comprehensive article examines the transformative impact of artificial intelligence on modern indoor agriculture, focusing on key technological advancements in smart greenhouse management and controlled environment agriculture. The article explores critical areas, including precision environmental control, nutrient management in hydroponic systems, plant health monitoring and disease management, harvest optimization, quality control, and energy management with sustainability practices. AI-driven solutions have revolutionized traditional farming approaches by integrating deep learning algorithms, computer vision systems, and IoT sensor networks, enabling unprecedented levels of automation, resource efficiency, and crop yield optimization. Implementing these technologies has significantly improved agricultural metrics, from disease detection and prevention to harvest timing and quality assessment, while substantially reducing operational costs and environmental impact.

Agricultural Intelligence: AI-driven performance frameworks for modern farming

Agricultural Intelligence: AI-driven performance frameworks for modern farming

Development of a handheld GPU-assisted DSC-TransNet model for the real-time classification of plant leaf disease using deep learning approach

In agriculture, promptly and accurately identifying leaf diseases is crucial for sustainable crop production. To address this requirement, this research introduces a hybrid deep learning model that combines the visual geometric group version 19 (VGG19) architecture features with the transformer encoder blocks. This fusion enables the accurate and précised real-time classification of leaf diseases affecting grape, bell pepper, and tomato plants. Incorporating transformer encoder blocks offers enhanced capability in capturing intricate spatial dependencies within leaf images, promising agricultural sustainability and food security. By providing farmers and farming stakeholders with a reliable tool for rapid disease detection, our model facilitates timely intervention and management practices, ultimately leading to improved crop yields and mitigated economic losses. Through extensive comparative analyses on various datasets and filed tests, the proposed depth wise separable convolutional-TransNet (DSC-TransNet) architecture has demonstrated higher performance in terms of accuracy (99.97%), precision (99.94%), recall (99.94), sensitivity (99.94%), F1-score (99.94%), AUC (0.98) for Grpae leaves across different datasets including bell pepper and tomato. Furthermore, including DSC layers enhances the computational efficiency of the model while maintaining expressive power, making it well-suited for real-time agricultural applications. The developed DSC-TransNet model is deployed in NVIDIA Jetson Nano single board computer. This research contributes to advancing the field of automated plant disease classification, addressing critical challenges in modern agriculture and promoting more efficient and sustainable farming practices.

From Farms to Forests: An Exploration of Afforestation Efforts in Poland Under the Rural Development Programme (2007–2020)

Forests are important in mitigating climate change and addressing biodiversity loss. In Poland, where forest cover has steadily increased since World War II, afforestation of agricultural land has emerged as a key strategy supported by both EU and national policies. This study evaluates the implementation of Poland’s afforestation programs under the Rural Development Programme (RDP) for the periods of 2007–2013 and 2014–2020 using data provided by the Agency for Restructuring and Modernisation of Agriculture (ARMA) and focusing on the financial incentives offered to private landowners and regional variations in their uptake. Results show that afforestation under the RDP aligned with the EU climate neutrality goals, with a total of 37,721 hectares afforested and PLN 243.5 million handed as financial support, predominantly (81%) over the first studied period. The largest afforestation efforts were observed in the Mazowieckie and Warmińsko-Mazurskie voivodeships, with Warszawa, Poland’s capital, achieving the highest municipal afforestation area across both periods. Financial considerations influenced farmer participation, but rising land prices and historical land use patterns were contributing factors. Urban afforestation and increasing demand for woody biomass further highlight the benefits of afforestation. However, conifer-dominated stands, prevalent on low-quality land, can present challenges to ecosystem stability under future climate scenarios, necessitating diversification towards deciduous species. Additionally, low uptake during the 2014–2020 period showcases the need for improved incentives to bolster participation. Still, afforestation presents significant economic and environmental opportunities, advancing long-term policy objectives while addressing critical challenges in climate.

Non-microbial biostimulants for plant growth and abiotic stress mitigation: a review of recent scientific innovations

ABSTRACT Climate change, population growth and declining soil fertility are threatening global food security. Modern agriculture must therefore adopt ecological and sustainable practices to restore soil fertility, strengthen crop resilience and guarantee a stable supply of food product. Plant biostimulants, a sustainable solution, have a high potential to replace certain conventional methods and improve crop productivity. These formulations of bioactive molecules or microbial strains, applied at low concentrations, improve agronomic performance, nutritional efficiency and resistance to abiotic stresses. This review focuses on non-microbial biostimulants, such as protein hydrolysates, humic substances, seaweed extracts, chitosan, biopolymers, and specific inorganic compounds. It analyses their effectiveness and future potential in increasing yields, improving crop quality, and enhancing tolerance to abiotic stress.

Enhancing food and nutrition security in Himalayan foothills with neglected and underutilized millets

Global food production predominantly depends on a limited number of cereal crops; however, numerous other crops have the potential to support the nutrition and economy of many local communities in developing countries. The different crop species characterized as having relatively low perceived economic importance or agricultural significance are known as underutilized crops. Millet is one of the underutilized crops with significant potential to address nutrient and hunger-related challenges in many developing countries like Nepal due to its versatility and climate resilience. Little is known about the determinants of adoption, cultural importance, and nutritional benefits of millet. Therefore, this study uses data from 1988 to 2019 and examines trends in millet production and its climate resilience, employing the Dynamic Ordinary Least Square Method. Results indicate that fertilizer use, cultivated area, and rural population significantly impact millet production in Nepal, while mean temperature has a negative but insignificant effect. The findings suggest that climate change does not significantly impact millet production. Millet is well adapted to challenging environments and offers superior nutritional value, suggesting that integration of millet into modern agriculture could be a valuable tool for creating a more sustainable, equitable, resilient, and healthy agrifood system that benefits both people and the planet. This research provides valuable insights for policymakers to enhance underutilized crops such as millet and implement strategies to integrate them into central agrifood systems. It also has a more considerable socio-economic impact on local communities.

Vermicomposting and Its Role in Soil Health: A Comprehensive Review

Vermicomposting, the process of using earthworms to convert organic waste into nutrient-rich compost, has gained significant attention in recent years due to its numerous environmental benefits, particularly in improving soil health. This review paper explores the concept of vermicomposting, its mechanisms, advantages, and applications in enhancing soil fertility, structure, and biodiversity. We also examine the impact of vermicompost on crop growth and the potential role of vermiculture in sustainable agriculture. By synthesizing current research, this paper provides a thorough overview of the potential of vermicomposting in promoting soil health and addressing the challenges of modern farming practices.

A Review of Research Literature on the Impact of Industrial Digitization on the Integrated Development of Logistics and Modern Agriculture

With the continuous progress of science and technology, industrial digitization has become a key force to promote the development of all walks of life. Especially in the two fields of logistics industry and modern agriculture, the application of digital technology not only improves the efficiency, but also promotes the deep integration of the two. This paper will be from the perspective of research at home and abroad, and from the digital industry, logistics industry and the agricultural industry integration development, the influence of the development of logistics industry, digital industry influence on the development of modern agriculture, the influence of the logistics industry and modern agricultural integration development five aspects on the main view of the existing literature.

Indigenous Knowledge on Shifting Cultivation and Sustainable Agriculture

Indigenous knowledge on shifting mountainous farm management of Arfak farmers is the original culture of Arfak people, held firm passed down to generations. This can be seen in the agricultural activities of Arfak farmers who are still applying indigenous knowledge to this day. Modernization can affect the existence and can also be a threat to the indigenous knowledge of Arfak farmers. The low level of education of farmers, the oral transfer of indigenous knowledge and the absence of written documentation of indigenous knowledge are the causes of indigenous knowledge being swept away in the flow of modernization. Apart from that, the existence of indigenous knowledge is increasingly threatened by the development of plantations and urban areas. Especially the increasing demand for land for plantations, infrastructure, settlements and mining. Based on data from the West Papua Agriculture Service, in 2017 plantation development reached 53,806 ha and mining reached 89,256 ha in the Arfak Mountains. Agricultural modernization and industrialization will certainly reduce shifting cultivation and the value of indigenous knowledge. The high biodiversity makes Arfak Mountains a conservation area and makes this region a center of highland vegetable production in West Papua. In 2021, agricultural sector contributed 33.61% to growth Arfak Mountains Gross Regional Domestic Product. This study aims to describe how Arfak farmers apply their indigenous knowledge, namely the concept of igya ser hanjob in managing land to support sustainable agricultural activities. This research uses qualitative approach with inductive methods and case study techniques to document the knowledge and experience Arfak farmers selected with the snowball technique. Data was collected through interviews and observations. This study found that sustainable land management in agricultural activities is based on the ecological concept of the Arfak people, namely igya ser hanjob. This concept regulates appropriate management areas for agriculture and ensures environment sustainability and family food. This ecological concept was socialized for generations Arfak farming family. However, in reality there is a threat of landslides in agricultural areas due to socio-economic and ecological changes in mountainous farming are of Arfak community.

Efficient Tomato Disease Detection Using MaxMin-Diffusion Mechanism and Lightweight Techniques

This paper proposes a disease detection model based on the maxmin-diffusion mechanism, aimed at improving the accuracy and robustness of disease detection tasks in the agricultural field. With the development of smart agriculture, automated disease detection has become one of the key tasks driving agricultural modernization. Traditional disease detection models often suffer from significant accuracy loss and robustness issues when dealing with complex disease types and dynamically changing time-series data. To address these problems, this paper introduces the maxmin-diffusion mechanism, which dynamically adjusts attention weights to enhance the model’s focus on key disease regions while suppressing interference from irrelevant areas, significantly improving the segmentation accuracy of disease regions. Through a series of experiments, the proposed model demonstrates outstanding performance across various disease detection tasks. For bacterial spot disease detection, the model achieves a precision of 0.98, recall of 0.95, accuracy of 0.96, and mIoU of 0.96, indicating that it can efficiently and accurately identify disease regions even in complex backgrounds. Compared to traditional self-attention and CBAM mechanisms, the maxmin-diffusion mechanism shows significant advantages in fine-grained feature extraction and time-series data processing, particularly in the recognition of dynamically changing disease regions, where it exhibits higher detection accuracy and robustness. Furthermore, the model underwent lightweight optimization, enabling the proposed disease detection model to not only achieve high-precision detection but also run efficiently on resource-constrained mobile devices. This provides strong technical support for the application of smart agriculture.

Balancing Tradition and Innovation: The Role of Environmental Conservation Agriculture in the Sustainability of the Ifugao Rice Terraces

This study investigates the continuation of Environmental Conservation Agriculture (ECA) practices among farmers in the Ifugao Rice Terraces, a Globally Important Agricultural Heritage System (GIAHS) in the Philippines. Through a cross-sectional survey of ECA farmers in the municipality of Banaue, this research explores the socio-demographic, environmental, and economic factors influencing the adoption and persistence of ECA. The findings reveal that while access to resources such as high-yielding seeds, modern farming equipment, and financial support is important for the adoption of ECA, the shift toward high-yielding varieties has contributed to a decline in the cultivation of Tinawon rice, which is vital for maintaining the ecological balance and cultural heritage of the terraces. This study underscores the importance of balancing modern agricultural practices with the continued cultivation of Tinawon rice to preserve biodiversity, soil health, and cultural identity, while also enhancing agricultural productivity. Additionally, the roles of community-based support systems, market access, and financial incentives are highlighted as key factors in sustaining ECA practices. Climate change presents both challenges and opportunities for adaptation, making it essential to integrate traditional knowledge with modern techniques to build resilience. Understanding the factors that shape ECA continuation is crucial for refining initiatives that address both the economic and cultural contexts. By emphasizing the importance of tailored, community-driven interventions, this study provides critical insights for enhancing ECA adoption in the Ifugao Rice Terraces, contributing to climate resilience and the long-term sustainability of this significant agricultural heritage system.

Chitosan- and sodium alginate-coated dendritic mesoporous organosilica nanoparticles improve pesticide adhesion on leaves and enable dual-stimulus-responsive release.

During the application of most conventional pesticides, a significant proportion is lost through rain wash-off and leaf rolling, leading to reduced actual utilization efficiency. In this paper, aminated dendritic mesoporous organosilicon nanoparticles (DMONs-NH2) were synthesized via a one-pot method and used as carriers. Carbendazim (CBZ) was then encapsulated within DMONs-NH2 through hydrogen bonding and electrostatic interactions. The particles were coated with sodium alginate (SA) and chitosan (CS), resulting in CBZ@DMONs@SA/CS nanopesticides with pH-temperature dual responsiveness and sustained-release properties. The SA/CS polymer shell significantly improved the wetting and adhesion of the pesticide on cucumber leaves. After rinsing, the retention rate of CBZ@DMONs@SA/CS was 3.61 times higher than free CBZ. The CBZ@DMONs@SA/CS exhibited 8.59-fold improved photostability under UV irradiation compared to free CBZ. The results of antifungal experiments demonstrated its effectiveness in controlling Botrytis cinereal (B.c.) and extending pesticide action time. Additionally, the acute toxicity of CBZ@DMONs@SA/CS against zebrafish was reduced by 4.75-fold compared to free CBZ. In conclusion, this dual-stimulation-responsive nanopesticide slow-release system with high adhesion and environmentally friendly characteristics provides a novel idea for the sustainable development of modern agriculture.

Comparison Between Food Security in Indonesia and Thailand: Rice Export and Import

This paper examines the food security of Indonesia and Thailand, focusing on rice production, consumption, population, exports, and imports. Despite rice being a staple food in both countries, Thailand has achieved a surplus in rice production and serves as a major exporter, while Indonesia faces challenges in meeting its rice demand due to high population growth and inefficient production systems. Thailand’s success is attributed to its advanced agricultural infrastructure, strict quality control, and robust government support. Conversely, Indonesia, although having a larger arable land area, struggles with inefficient farming practices and high rice consumption, leading to a reliance on rice imports, primarily from Thailand. This study uses a comparative analytic design, relying on secondary data from government reports, academic journals, and trade databases. The findings suggest that Indonesia can improve its food security and move toward self-sufficiency by adopting Thailand’s practices, including modernization of farming techniques, policy support, and infrastructure development. The paper emphasizes the need for strategic investments in agricultural modernization and policy adaptation to bridge the production-consumption gap in Indonesia

Plant Detection in RGB Images from Unmanned Aerial Vehicles Using Segmentation by Deep Learning and an Impact of Model Accuracy on Downstream Analysis.

Crop field monitoring using unmanned aerial vehicles (UAVs) is one of the most important technologies for plant growth control in modern precision agriculture. One of the important and widely used tasks in field monitoring is plant stand counting. The accurate identification of plants in field images provides estimates of plant number per unit area, detects missing seedlings, and predicts crop yield. Current methods are based on the detection of plants in images obtained from UAVs by means of computer vision algorithms and deep learning neural networks. These approaches depend on image spatial resolution and the quality of plant markup. The performance of automatic plant detection may affect the efficiency of downstream analysis of a field cropping pattern. In the present work, a method is presented for detecting the plants of five species in images acquired via a UAV on the basis of image segmentation by deep learning algorithms (convolutional neural networks). Twelve orthomosaics were collected and marked at several sites in Russia to train and test the neural network algorithms. Additionally, 17 existing datasets of various spatial resolutions and markup quality levels from the Roboflow service were used to extend training image sets. Finally, we compared several texture features between manually evaluated and neural-network-estimated plant masks. It was demonstrated that adding images to the training sample (even those of lower resolution and markup quality) improves plant stand counting significantly. The work indicates how the accuracy of plant detection in field images may affect their cropping pattern evaluation by means of texture characteristics. For some of the characteristics (GLCM mean, GLRM long run, GLRM run ratio) the estimates between images marked manually and automatically are close. For others, the differences are large and may lead to erroneous conclusions about the properties of field cropping patterns. Nonetheless, overall, plant detection algorithms with a higher accuracy show better agreement with the estimates of texture parameters obtained from manually marked images.

MyIPM smartphone applications—tools to increase adoption of integrated pest management

Abstract Global agricultural productivity faces significant challenges due to pest-related crop losses, and while integrated pest management (IPM) is a sustainable approach to mitigate pest risks, it is also inherently complex, which limits its implementation and adoption by farmers and pest management professionals. This article provides an overview of the MyIPM series of smartphone applications (apps), designed to facilitate IPM adoption by providing comprehensive pest management recommendations, covering diseases, arthropods, and weeds for multiple US commodities. Key features of the apps include high-resolution images for accurate pest identification, non-chemical integrated management information, detailed life cycle and epidemiology information, a pesticide search bar, and interactive tables allowing active ingredients and trade names to be sorted by multiple characteristics, including modes of action for easier implementation of resistance management tactics. The MyIPM apps complement traditional Extension outreach methods, offering portable access to a wealth of pest management information. In addition, the article highlights 3 practical examples—managing blossom blight and brown rot of peach caused by Monilinia sp.; addressing sugarcane aphids, Melanaphis sorghi (Theobald), in grain sorghum; and managing urban pests in Hawaii and postharvest quarantine of Hawaiian crops for export—demonstrating how the MyIPM apps streamline IPM decision-making for a diverse group of farmers and pest management professionals. Overall, the MyIPM series of apps provides a valuable tool for modern agriculture, offering users a wealth of IPM knowledge at their fingertips, and supporting the adoption of more sustainable and effective practices.