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10 pages, 909 KB

Open AccessProceeding Paper

Evaluation of the Antifungal Effect of Carvacrol-Rich Essential Oils: In Vitro Study on the Phytopathogenic Fungi Alternaria and Fusarium

by Vasileios Papantzikos, Georgios Patakioutas and Paraskevi Yfanti

Biol. Life Sci. Forum 2025, 54(1), 1; https://doi.org/10.3390/blsf2025054001 - 21 Nov 2025

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Abstract

Certain essential oils (EOs) from aromatic plants have shown potent antifungal effects. In this work, an in vitro study was conducted to examine the antifungal effect of EOs obtained from Greek flora aromatic plants that belong to the Lamiaceae family on two phytopathogenic [...] Read more.

Certain essential oils (EOs) from aromatic plants have shown potent antifungal effects. In this work, an in vitro study was conducted to examine the antifungal effect of EOs obtained from Greek flora aromatic plants that belong to the Lamiaceae family on two phytopathogenic fungi. Specifically, Satureja horvatii ssp. macrophylla, Coridothymus capitatus, and Origanum vulgare ssp. hirtum were tested against Alternaria sp., which causes tomato black spot, and Fusarium sp., which causes potato tuber dry rot during storage. The antifungal activity of the EOs was assessed using fumigant assays, and their chemical composition was analyzed using gas chromatography–mass spectrometry (GC–MS). After 8 days of incubation at 26 ± 1 °C, the EOs of O. vulgare ssp. hirtum and C. capitatus completely inhibited mycelial growth at 2 µL plate⁻¹ in the case of Fusarium sp. and at 3 µL plate⁻¹ in the case of Alternaria sp. S. horvatii ssp. macrophylla completely inhibited the mycelial growth of Fusarium sp. at 3 µL plate⁻¹ and that of Alternaria sp. at 4 µL plate⁻¹. All the essential oils used in the experiments were rich in carvacrol (41.4–70.0%), while thymol levels ranged from 0% to 18.9%. This fumigant effect could be further evaluated for the fruits’ postharvest protection from phytopathogenic fungi during storage. Full article

(This article belongs to the Proceedings of The 3rd International Online Conference on Agriculture)

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6 pages, 583 KB

Open AccessProceeding Paper

Fruit Storage of Bitter Gourd (Momordica charantia) to Enhance Its Seedling Vigor

by Mariz Dahilig, Raymund Julius Rosales, Christian Butch Andrew Balbas, Glisten Faith Pascua and Micah Benize Gregorio-Balbas

Biol. Life Sci. Forum 2025, 54(1), 2; https://doi.org/10.3390/blsf2025054002 - 8 Dec 2025

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Abstract

A study was conducted on the fruit storage (0, 2, 4, and 6 days) of bitter gourd (half-ripe stage) under ambient conditions (temperature: 28 ± 2 °C and relative humidity: 65 ± 5%). It was conducted to evaluate the impact of fruit storage [...] Read more.

A study was conducted on the fruit storage (0, 2, 4, and 6 days) of bitter gourd (half-ripe stage) under ambient conditions (temperature: 28 ± 2 °C and relative humidity: 65 ± 5%). It was conducted to evaluate the impact of fruit storage on the enhancement of bitter gourd’s seedling vigor. Four days of fruit storage significantly improved the seedling vigor index (SVI), and beyond this period, did not contribute to a higher SVI. It produced better growth for shoot, root, and seedling lengths as compared to lower and higher periods, as well as unstored fruits. Moreover, a higher root count per seedling and seedling fresh weight were observed in 4-day fruit storage. Conversely, 6-day fruit storage exhibited a higher chlorophyll content (SPAD index) as compared to shorter storage periods and unstored fruits. A 4-day fruit-storage period may be recommended to the farmers to obtain vigorous bitter gourd seedlings. Full article

(This article belongs to the Proceedings of The 3rd International Online Conference on Agriculture)

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9 pages, 7875 KB

Open AccessProceeding Paper

Mapping Soil Salinity by Integrating Field EC Measurements and Landsat-Derived Spectral Indices by Cloud-Based Geospatial Analysis

by Saffi Ur Rehman, Tingting Chang, Zahid Maqbool and Muhammad Adnan Shahid

Biol. Life Sci. Forum 2025, 54(1), 3; https://doi.org/10.3390/blsf2025054003 - 9 Dec 2025

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Abstract

Soil salinity is an essential constraint on sustainable crop production, particularly in arid and semi-arid regions, due to its effects on soil fertility. This study presents a data-driven approach for mapping soil salinity by integrating field-based electrical conductivity (EC) measurements with remote sensing [...] Read more.

Soil salinity is an essential constraint on sustainable crop production, particularly in arid and semi-arid regions, due to its effects on soil fertility. This study presents a data-driven approach for mapping soil salinity by integrating field-based electrical conductivity (EC) measurements with remote sensing and geospatial analysis in the district of Mandi Baha Uddin, Pakistan. Eleven georeferenced soil samples were collected and analyzed for EC (range: 0.59–1.06 dS/m), serving as training data for model calibration. Using Landsat 8 Surface Reflectance imagery within Google Earth Engine, spectral indices Normalized Difference Salinity Index (NDSI), Salinity Index (SI), and Brightness Index (BI) were extracted. Among various modeling approaches, a linear regression model was applied to these indices, revealing NDSI as the most significant predictor (coefficient = 12.938), while SI and BI show negligible contribution. The model achieved moderate accuracy (R² = 0.566, RMSE = 0.085 dS/m). A Random Forest approach yielded higher training accuracy (R² = 0.841) but suffered from overfitting during cross-validation, indicating limited sample size constraints. The regression equation (EC = 12.938 × NDSI + 5.864) was applied in GEE to generate the EC prediction map. The resulting 30 m resolution EC map was classified into salinity categories and validated through independent field observations. This framework highlights the effectiveness of using freely available satellite data and cloud-based platforms like GEE for cost-effective soil salinity monitoring. The study provides a transferable methodology for precision agriculture, enabling informed land management and crop planning in salinity-affected regions. Full article

(This article belongs to the Proceedings of The 3rd International Online Conference on Agriculture)

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7 pages, 1521 KB

Open AccessProceeding Paper

Comparative Assessment of UAV Nozzle Type and Flight Height for Efficient Rice Canopy Spraying in Northern India

by Shefali Vinod Ramteke, Pritish Kumar Varadwaj and Vineet Tiwari

Biol. Life Sci. Forum 2025, 54(1), 4; https://doi.org/10.3390/blsf2025054004 - 16 Dec 2025

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Abstract

Unmanned aerial vehicle (UAV)-based spraying is transforming precision agriculture by enabling targeted, uniform agrochemical application. This study evaluates four nozzle types across three flight heights for rice crop canopy, analyzing spray metrics including canopy coverage (CA%), droplet density (DD), volume median diameter (VMD), [...] Read more.

Unmanned aerial vehicle (UAV)-based spraying is transforming precision agriculture by enabling targeted, uniform agrochemical application. This study evaluates four nozzle types across three flight heights for rice crop canopy, analyzing spray metrics including canopy coverage (CA%), droplet density (DD), volume median diameter (VMD), and swath width (SW). Statistical analysis identified nozzle N-1 at 3 m and N-3 at 2.5 m as optimal configurations, maximizing coverage and droplet uniformity. Results support evidence-based nozzle–height selection to enhance spraying efficiency and reduce environmental impact. The findings promote sustainable UAV spraying strategies, especially for smallholder rice farmers in Northern India. Full article

(This article belongs to the Proceedings of The 3rd International Online Conference on Agriculture)

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8 pages, 225 KB

Open AccessProceeding Paper

Comparative Evaluation of UAV Nozzle Geometries for Sustainable Water and Pesticide Management in Rice Cultivation

by Shefali Vinod Ramteke, Pritish Kumar Varadwaj and Vineet Tiwari

Biol. Life Sci. Forum 2025, 54(1), 5; https://doi.org/10.3390/blsf2025054005 - 22 Dec 2025

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Abstract

This study evaluates the influence of four unmanned aerial vehicle (UAV) spray nozzle geometries—flat-fan, hollow-cone, air-induction, and ultra-fine electrostatic—on water and pesticide use, canopy coverage, and greenhouse gas emissions in PB-112 rice under field conditions in Saharanpur, India. Across six farms (n [...] Read more.

This study evaluates the influence of four unmanned aerial vehicle (UAV) spray nozzle geometries—flat-fan, hollow-cone, air-induction, and ultra-fine electrostatic—on water and pesticide use, canopy coverage, and greenhouse gas emissions in PB-112 rice under field conditions in Saharanpur, India. Across six farms (n = 6), ultra-fine nozzles achieved the greatest reductions in water (41%) and pesticide (43%) volumes, yielding direct pump energy savings of 737 kWh ha⁻¹ and 369 kg CO₂e ha⁻¹, plus further indirect savings from manufacturing. Paired t-tests confirmed highly significant differences (p < 0.001) with large effect sizes. Finer droplets also reduced run-off and evaporation losses by over 60%. These findings demonstrate that nozzle optimization markedly enhances resource efficiency and environmental protection in precision rice spraying. Full article

(This article belongs to the Proceedings of The 3rd International Online Conference on Agriculture)

9 pages, 1166 KB

Open AccessProceeding Paper

Yield Stability of Selected Potato Cultivars Under Mulch and Fungicide Applications Across Different Environments

by Nosipho Precious Minenhle Phungula, Sandile Thamsanqa Hadebe, Lucky Sithole, Morgan Nadioo and Nomali Ziphorah Ngobese

Biol. Life Sci. Forum 2025, 54(1), 6; https://doi.org/10.3390/blsf2025054006 - 31 Dec 2025

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Abstract

Smallholder farmers’ yields fluctuate yearly due to the variability of climate, resources, and diseases. The study aimed to assess genotypes-by-environment interactions under different management practices using additive main effects and multiplicative interaction models. Potato cultivars (Mondial, Electra, Sababa, and Panamera) were grown in [...] Read more.

Smallholder farmers’ yields fluctuate yearly due to the variability of climate, resources, and diseases. The study aimed to assess genotypes-by-environment interactions under different management practices using additive main effects and multiplicative interaction models. Potato cultivars (Mondial, Electra, Sababa, and Panamera) were grown in five environments (Mbalenhle, Hlathikhulu, Mbhava, Stezi, and Gobizembe) for three seasons (2021–2023). Potatoes were planted under mulch (non-mulched and mulched) and fungicide (sprayed and unsprayed) management practices. The results revealed that the genotype–environment effect had a minimal contribution to tuber yield, ranging from 8.42% to 11.01% across management practices. For instance, in the absence of fungicide application with mulch and non-mulched practices, resulted in genotype effects of 69.92% and 60.62% and environments effects of 20.52% and 30.95%, respectively. Full article

(This article belongs to the Proceedings of The 3rd International Online Conference on Agriculture)

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10 pages, 1829 KB

Open AccessProceeding Paper

Machine Learning Based Agricultural Price Forecasting for Major Food Crops in India Using Environmental and Economic Factors

by P. Ankit Krishna, Gurugubelli V. S. Narayana, Siva Krishna Kotha and Debabrata Pattnayak

Biol. Life Sci. Forum 2025, 54(1), 7; https://doi.org/10.3390/blsf2025054007 - 12 Jan 2026

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Abstract

The contemporary agricultural market is profoundly volatile, where agricultural prices are based on a complex supply chain, climatic irregularity or unscheduled market demand. Prices of crops need to be predicted in a reliable and timely manner for farmers, policy-makers and other stakeholders to [...] Read more.

The contemporary agricultural market is profoundly volatile, where agricultural prices are based on a complex supply chain, climatic irregularity or unscheduled market demand. Prices of crops need to be predicted in a reliable and timely manner for farmers, policy-makers and other stakeholders to take evidence-based decisions ultimately for the benefit towards sustainable agriculture and economic sustainability. Objective: The objective of this study is to develop and evaluate a comprehensive machine learning model for predicting agricultural prices incorporating logistic, economic and environmental considerations. It is the desire to make agriculture more profitable by building simple and accurate forecasting models. Methods: An assorted dataset was collected, which covers major factors to constitute the dataset of temperature, rainfall, fertiliser use, pest and disease attack level, cost of transportation, market demand-supply ratio and regional competitiveness. The data was subjected to pre-processing and feature extraction for quality control/quality assurance. Several machine learning models (Linear Regression, Support Vector Machines, AdaBoost, Random Forest, and XGBoost) were trained and evaluated using performance metrics such as R2 score, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Results: Out of the model approaches that were analysed, predictive performance was superior for XGBoost (with an R2 Score of 0.94, RMSE of 12.8 and MAE of 8.6). To generate accurate predictions, the ability to account for complex non-linear relationships between market and environmental information was necessary. Conclusions: The forecast model of the XGBoost-based prediction system is reliable, of low complexity and widely applicable to large-scale real-time forecasting of agricultural monitoring. The model substantially reduces the uncertainty of price forecasting, and does so by including multivariate environmental and economic aspects that permit more profitable management practices in a schedule for future sustainable agriculture. Full article

(This article belongs to the Proceedings of The 3rd International Online Conference on Agriculture)

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8 pages, 579 KB

Open AccessProceeding Paper

Baseline Susceptibility of Eldana saccharina to Coragen^® SC: Implications for Resistance Monitoring and Management in Sugarcane

by Kwanele Phiwinhlanhla Msele, Caswell Munyai, Ewald Hendrik Albertse and Lawrence Nkosikhona Malinga

Biol. Life Sci. Forum 2025, 54(1), 8; https://doi.org/10.3390/blsf2025054008 - 16 Jan 2026

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Abstract

Eldana saccharina Walker is a major sugarcane pest in South Africa, primarily controlled with chemical insecticides, though resistance threatens their effectiveness. Laboratory bioassays at the South African Sugarcane Research Institute evaluated the baseline susceptibility of E. saccharina to six concentrations of Coragen^® [...] Read more.

Eldana saccharina Walker is a major sugarcane pest in South Africa, primarily controlled with chemical insecticides, though resistance threatens their effectiveness. Laboratory bioassays at the South African Sugarcane Research Institute evaluated the baseline susceptibility of E. saccharina to six concentrations of Coragen^® (chlorantraniliprole). Mortality and larval weight data were analysed using probit analysis to determine LC₅₀ and LC₉₅ values and assess growth inhibition. Mortality and weight reduction increased with concentration, with the highest concentration causing 79% mortality. Full article

(This article belongs to the Proceedings of The 3rd International Online Conference on Agriculture)

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548 KB

Open AccessProceeding Paper

AI-Driven Wheat Crop Optimization and Yield Prediction Tool

by Wareesha Ayub, Muhammad Sameer, Muhammad Ali and Sharaf Hussain

Biol. Life Sci. Forum 2025, 54(1), 9; https://doi.org/10.3390/blsf2025054009 - 16 Jan 2026

Abstract

Precise prediction of wheat yield plays a crucial role in food security and resource management in Pakistan. The current research suggests an artificial intelligence-driven framework based on 23 years of agro-meteorological and yield data that predicts wheat production. Several machine learning models were [...] Read more.

Precise prediction of wheat yield plays a crucial role in food security and resource management in Pakistan. The current research suggests an artificial intelligence-driven framework based on 23 years of agro-meteorological and yield data that predicts wheat production. Several machine learning models were compared, and a two-layer LSTM model performed better because it was able to capture temporal dependencies. The model managed to achieve high accuracy (R² = 0.979) and low prediction errors, confirming the applicability of deep learning in agricultural forecasting in climate-sensitive regions and its applicability to other staple crops. Full article

(This article belongs to the Proceedings of The 3rd International Online Conference on Agriculture)

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