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Volume 2
Issue 2 JULY– DECEMBER 2025
Volume 2(Issue 2) JULY– DECEMBER 2025 Research Articles
Use of AI-Based Crop monitoring system to detect nutrient deficiency in Field Maize
Vol.2(2); Pages:1-8. Published on July 2025
Abstract
In precision agriculture nutrient deficiencies should be detected on time to ensure crop health and optimized yields. This paper assessed the performance of a crop monitoring system with the help of an AI algorithm and multispectral cameras mounted to drones to help early identification of nitrogen (N), phosphorus (P), and potassium (K) deficiencies in maize (Zea mays L.). Based on 60 hectares in Serbia, the trials have focused on determining the possibility to recover dead areas and reduce critical deficiency and put such knowledge into practice using the system in real-time. The algorithm was trained on 8,000 images annotated in field conditions and when tested against several thousand of field conditions images displayed 91.3 percent accuracy in determining the various deficiencies of the nutrients. Through this technology, early intervention measures were put in place and the increase in grain yield was a whopping 12.4 % better than the untreated controls. The research paper identifies the possibility of AI-based image recognition systems to address efficiently and effectively the problem of improving nutrient management, crop health, and yield optimization of data-dense agricultural systems. Such integration of real-time diagnostics can be a game-changer in the field of nutrient management strategies as it provides farmers with the most relevant and precise timely decisions to maximize both productivity and sustainability in agricultural practice.
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Real-time Scheduling of Irrigation Using IoT-based Soil Sensors and Mobile Apps in Small holder Farms
Vol.2(2); Pages:9-16. Published on August 2025
Abstract
The importance of water management in smallholder farming, especially in regions where there is water shortage, is growing by the day. The paper analyzed the performance of a IoT-based soil sensor network connected to a mobile decision-support program of real-time irrigation scheduling at three districts in Malaysia vegetable farms. Data gain was accurate as the system recorded the soil moisture every 30 minutes, and the recorded data were sent to a mobile application, which gave the instructions of irrigation following the specific thresholds of crop requirements in terms of soil moisture. The system was compared with the traditional irrigation where the farmers would manually gauge the moisture content of soil and they would, by their subjective impression use water. Findings indicated a positive outcome of smart irrigation system wherein there was a decrease of water consumption by 27 percent and yield increase of 11.6 percent of the okra and chili crops. The fact that the app was easy-to-use and that the alerts were timely were noted as major strengths based on the user feedback. This paper shows that using IoT-powered irrigation solutions can be an appropriate and successful way to enhance water productivity and agribusiness decisions in smallholder farming. The results hint at the potential of digital solutions to empower smallholders and allow them to optimize irrigation, decrease resource consumption, and increase water-deficient farm productivity.
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Variable Rate Drone Spraying with Biostimulant in Wheat- A Precision Spraying Pilot Study
Vol.2(2); Pages:17-24. Published on August 2025
Abstract
Optimal use of inputs is an important aspect in agriculture particularly in making it very sustainable and at the same time increasing crop yield. This paper checked the potential of variable rate spraying of a biostimulant seaweed with drones on wheat ( Triticum aestivum L. ) fields in the Netherlands. NDVI (Normalized Difference Vegetation Index) zone maps were used to deposit specific amounts of biostimulant on diverse crop vigor with a quadcopter UAV outfitted with a smart spraying module to employ both variable doses of biostimulant as well as area coverage across a 40-hectare field. The targeted treatment that could be applied with the drone also achieved greater uniformity in the canopy development, causing the increase in grain yield (9.8%). The application also saved the amount of input used by 17 percent as compared to spraying a uniform area on the ground, this was in the use of resources and environmental implication. Economic analysis demonstrated that net returns with the biostimulant application were 14% better than when no biostimulant was applied and demonstrating the cost-effectiveness of precision agriculture with the use of drones. This paper shows that the use of drone-based optimization of inputs is capable of making a modern field crop management more economically efficient, cost-effective, and crop-performance-driven, and thus, the skilful innovation thus can be used at scale.
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Solarism-powered Cold Storage Unit e Performance in Alleviating Post-Harvest Supply chain Losses in perishable horticultural supply chains
Vol.2(2); Pages:25-32. Published on September 2025
Abstract
Losses after harvest as a result of poor cold storage facilities in horticultural value chains continue to pose a great challenge to many especially in the tropics. This research was expected to assess the technical and financial efficiency of the solar-powered Cold stores that were intended to be used in three rural aggregation centers in Ghana. The units were; constructed to address cold chain weaknesses and minimized after-harvest losses in perishable crops like tomatoes and leafy vegetables. The critical performance indicators were the stabilization of temperatures inside, the energy saving criterion, enhanced shelf-life and cost reimbursement. The systems averagely kept an internal temperature of 5.4 +/- 0.8 oC thus achieving a shelf life of tomatoes and leafy greens of 4 to 6 days compared to the conventional means of storage. In addition, the cold storage units using solar power made the post-harvest losses to reduce by 39.6 percent (p < 0.01). An economic analysis revealed that the economic efficiency of the solar-powered storage system is demonstrated by the fact that the break-even of the investment recovery is realized in 18 months with a cooperatively-managed model. These results indicate that decentralized solar cold storage can be an effective way of addressing food security, food waste and maximizing the economic returns on the perishable horticultural supply chain.
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Blockchain-Centered Traceability System on how to increase Transparency and Trust in Supply Chains of Organic Produce
Vol.2(2); Pages:33-39. Published on September 2025
Abstract
Credibility in organic produce labeling by the consumers is often impeded by poor transparency and accountability in supply chains. This research explored how to apply a blockchain-enabled traceability system to increase transparency, traceability and trust within organic fruit and vegetable supply chains. The blockchain was used in 27 smallholder farms, 3 cooperative and 2 retail distributors in Spain. The platform had QR-coded smart contracts that served to capture all the broad data such as production, certification, logistic, and transaction records. The surveys and audits of the stakeholders showed that there was an increase of 93 percent in the traceability completeness, a decrease of 37 percent in the tampering of records and increase in consumer reliability at the point sale. The smallholder farmers gave significant results on time savings and associated better integration with export compliance. These results confirm the affordability capacity of the blockchain-based mechanism to promote higher transparency, responsibility, and confidence in organic agrisupply chain activity particularly in places with complicated certification systems.
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Issue 1 JANUARY- JUNE 2025
Volume 2(Issue 1) JANUARY- JUNE 2025 Research Articles
Big Data for Precision Agriculture and Global Food Security
Vol.2(1); Pages:1-9. Published on May 2025
Abstract
In agroecosystems, crop diversity plays a pivotal role in enhancing pest management and food production. Big Data is revolutionizing precision agriculture by enabling data-driven decision-making for improved crop yields, resource efficiency, and sustainability. The integration of remote sensing, IoT devices, machine learning, and real-time analytics allows farmers to monitor soil health, weather patterns, pest infestations, and crop performance with unprecedented accuracy. This technological advancement contributes significantly to global food security by optimizing agricultural productivity while minimizing environmental impact. However, challenges such as data privacy, infrastructure requirements, and the digital divide must be addressed to ensure widespread adoption. This paper explores the role of Big Data in transforming modern agriculture and its implications for global food security.
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Quantum Sensors for Precision Farming and Soil Health Monitoring
Vol.2(1); Pages:10-16. Published on May 2025
Abstract
Quantum sensors are emerging as transformative tools in precision farming and soil health monitoring, offering unprecedented sensitivity and accuracy in detecting environmental variables. These sensors leverage quantum phenomena such as superposition and entanglement to measure soil moisture, nutrient levels, pH, and contamination with greater precision than classical sensors. By integrating quantum magnetometers, gravimeters, and optical sensors into agricultural systems, farmers can achieve real-time, high-resolution data for informed decision-making, improving yield, resource efficiency, and sustainability. This paper explores the principles of quantum sensing, its applications in agriculture, and potential challenges, including cost, scalability, and integration with existing technologies. The adoption of quantum sensors in farming could revolutionize agricultural practices, ensuring enhanced soil health management and sustainable food production.
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Costs, Advantages, and Sustainability of Horticultural Therapy’s Economic Impact in Europe
Vol.2(1); Pages:17-23. Published on May 2025
Abstract
Horticultural therapy (HT) has become widely accepted in Europe because it shows great success in supporting mental health and physical health alongside social inclusion goals. The economic assessment in this work studies how HT delivers value through financial expense analysis and future sustainability assessments. Economic viability of HT programs together with their investment returns and public and private funding mechanisms receive assessment in European case examinations and economic studies. The research demonstrates that health through theater creates efficient use of health resources while it develops communities and reduces healthcare costs which supports social prosperity. The paper includes recommendations together with strategies for implementing HT within nationwide healthcare frameworks.
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Deep learning techniques for Alzheimer’s disease early detection based on neuroimaging and multi-omics data
Vol.2(1); Pages:24-29. Published on May 2025
Abstract
People who develop Alzheimer’s disease suffer from a progressive brain-wasting disorder and the onset of this condition depends on genetic and environmental factors. The combination of multi-dimensional imaging and genomics data has proven effective for detecting AD and predicting its development early on. Researchers examine deep learning methods for analyzing neuroimaging data from MRI and PET combined with genomic as well as transcriptomic and epigenomic information. Organizations employ machine learning methodologies to detect important biomarkers which enables more precise diagnostic testing and risk evaluation. The study demonstrates that integrative analysis offers needed potential to support early intervention together with customized AD treatments.
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Convolutions of Spectral Graphs for Population-based Disease Prediction
Vol.2(1); Pages:30-37. Published on May 2025
Abstract
Modern disease prediction in medical imaging and healthcare analytics increasingly relies on extensive multimodal data, including imaging and non-imaging information. This complexity necessitates sophisticated models that can simultaneously analyze individual patient features and the relationships between patients within large datasets. Traditional machine learning and deep learning techniques often struggle to fully leverage both subject-specific features and population-wide associations. Graph-based models offer a promising alternative due to their ability to represent structured relationships between subjects. However, prior approaches mainly focus on pairwise similarities without incorporating individual characteristics, limiting their predictive power. On the other hand, models that rely solely on individual imaging feature vectors fail to capture meaningful interactions and similarities between subjects, which can significantly hinder classification performance. To overcome these limitations, we introduce a novel application of Graph Convolutional Networks (GCNs) for brain analysis in large populations. Our method constructs a sparse graph representation where each node represents a subject and is associated with image-based feature vectors, while edges encode phenotypic similarities. This graph structure allows us to leverage both individual imaging characteristics and broader population-wide contextual information to improve classification accuracy. By training a GCN model on partially labeled graphs, we infer class labels for previously unlabeled nodes by integrating both node-specific attributes and structural relationships between subjects.
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Volume 1
Issue 1 JULY– DECEMBER 2024
Research Articles Volume 1 (Issue 1) JULY– DECEMBER 2024
Crop Diversity in Agroecosystems for Pest Management and Food Production
Vol.1(1); Pages:1-10. Published on September 2024
Abstract
In agroecosystems, crop diversity plays a pivotal role in enhancing pest management and food production. Increasing the variety of crops within agricultural landscapes can lead to more resilient and sustainable farming practices. Diverse cropping systems contribute to pest suppression by disrupting pest life cycles, enhancing natural enemy habitats, and reducing pest colonization and outbreak potential. This ecological approach minimizes reliance on chemical pesticides, thereby reducing environmental contamination and promoting biodiversity. Furthermore, crop diversity fosters soil health through improved nutrient cycling and soil structure, which in turn enhances crop yields and food security. Diverse agroecosystems are better equipped to adapt to climate variability, resist pest and disease pressures, and maintain long-term productivity. Integrating a range of crops, including cover crops, intercropping, and crop rotations, can optimize resource use and create synergies that bolster both pest management and food production. Policymakers and farmers should prioritize strategies that increase crop diversity to achieve more sustainable agricultural systems, ensuring ecological balance and food system resilience.
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Enhancing Soil Quality and Promoting Plant Health Through Beneficial Microorganisms
Vol.1(1); Pages:11-18. Published on October 2024
Abstract
Enhancing soil quality and promoting plant health through beneficial microorganisms is increasingly recognized as a sustainable approach in modern agriculture. Beneficial microorganisms encompass a diverse array of bacteria, fungi, archaea, and protozoa that interact synergistically with plants and soil biota. These microorganisms play crucial roles in nutrient cycling, disease suppression, and enhancing soil structure, thereby contributing to overall soil health and plant productivity.Key mechanisms through which beneficial microorganisms improve soil and plant health include nitrogen fixation by nitrogen-fixing bacteria (e.g., Rhizobium, Azotobacter), phosphorus solubilization by phosphate-solubilizing bacteria (e.g., Pseudomonas, Bacillus), and the creation of compounds like phytohormones and siderophores that encourage plant growth. Furthermore, symbiotic relationships between mycorrhizal fungi and plant roots improve nutrient uptake and increase plant resistance to environmental challenges.To fully utilize microbial communities in agriculture, it is imperative to comprehend their dynamics and the ways in which they interact with plants and soil biota. Advances in molecular biology, genomics, and metagenomics have provided insights into microbial diversity and functionality, enabling targeted strategies to manipulate microbial communities for sustainable crop production.Furthermore, integrating beneficial microorganisms into agricultural practices offers a promising alternative to chemical fertilizers and pesticides, reducing environmental impacts and enhancing soil biodiversity. However, challenges such as optimizing microbial inoculants for specific soil and crop types, ensuring long-term stability of microbial populations, and mitigating environmental stressors need to be addressed.
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Examining the Relationship Between Data-Driven Agriculture and Sustainable Farming: Allies or Adversaries?
Vol.1(1); Pages:19-28. Published on November-2024
Abstract
Data-driven agriculture is increasingly viewed as a transformative approach to improving productivity and efficiency within modern farming systems. This approach utilizes advanced technologies such as artificial intelligence (AI), Internet of Things (IoT), precision agriculture, and big data analytics to collect, analyze, and apply real-time data on farming operations. Sustainable farming, on the other hand, emphasizes environmental stewardship, resource conservation, and long-term viability in agriculture. While both data-driven and sustainable farming practices aim to enhance agricultural productivity, their compatibility raises critical questions. This paper explores whether these two paradigms act as allies, promoting a harmonious integration for eco-efficient agriculture, or if they are adversaries, where data-centric techniques potentially undermine core sustainability principles.Key areas of focus include the benefits of precision farming in reducing resource waste, optimizing water and energy use, and improving crop yield, all of which align with sustainability goals. However, concerns are also raised about the environmental and social costs of implementing high-tech solutions in agriculture, such as energy consumption, dependency on large corporations for proprietary technology, and potential exclusion of small-scale farmers from these advances. Furthermore, the ethical implications of large-scale data collection and its ownership are discussed, questioning the true beneficiaries of such innovations.Ultimately, the paper argues that data-driven agriculture and sustainable farming need not be inherently in conflict. With careful design, regulation, and inclusive access, data-driven technologies can support sustainable practices by promoting resource-efficient farming, reducing environmental impact, and enhancing climate resilience. The integration of these two approaches holds the potential for creating a more sustainable agricultural future, provided challenges related to equity, ethics, and environmental trade-offs are addressed.
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Overview of Greenhouse Gas Emissions in Polish Agriculture
Vol.1(1); Pages:29-36. Published on December-2024
Abstract
Poland’s agricultural sector plays a significant role in the country’s economy and food production; however, it is also a notable contributor to greenhouse gas (GHG) emissions. This overview examines the primary sources of GHG emissions within Polish agriculture, focusing on methane (CH₄), nitrous oxide (N₂O), and carbon dioxide (CO₂) emissions. Livestock production, particularly cattle and dairy farming, is identified as a major source of methane emissions, primarily through enteric fermentation and manure management. Nitrous oxide emissions largely stem from the application of nitrogen fertilizers and soil management practices. Additionally, carbon dioxide emissions arise from energy consumption in agricultural operations, land-use changes, and deforestation. The analysis further highlights the impact of agricultural practices on emissions intensity and explores current mitigation strategies, including the adoption of sustainable farming practices, agroecology, and technological innovations. Emphasizing the need for integrated policies that support both agricultural productivity and environmental sustainability, this overview concludes with recommendations for future research and policy development aimed at reducing GHG emissions while ensuring food security in Poland.
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The Role of Biopesticides in Enhancing Organic Agriculture Practices
Vol.1(1); Pages:37-46. Published on December-2024
Abstract
Biopesticides play a pivotal role in the enhancement of organic agriculture practices by offering an environmentally sustainable alternative to synthetic chemical pesticides. Derived from natural materials such as plants, bacteria, fungi, and certain minerals, biopesticides contribute to the reduction of chemical residues in crops and the environment, aligning with the core principles of organic farming. This paper explores the effectiveness of biopesticides in pest management, their modes of action, and the potential they hold in mitigating the impact of pests while maintaining ecological balance. Key factors driving the adoption of biopesticides in organic agriculture include their target specificity, reduced environmental persistence, and minimal impact on non-target organisms, including beneficial insects. The integration of biopesticides into organic farming practices supports biodiversity, enhances soil health, and contributes to the resilience of farming systems against pests and diseases. Furthermore, this study delves into challenges faced by farmers in adopting biopesticides, such as regulatory barriers, cost implications, and limited availability, while highlighting successful case studies of their implementation globally. The transition from conventional pest control to biopesticides in organic farming systems is seen as a significant step towards more sustainable and resilient agricultural practices, ensuring food safety and promoting environmental health.
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