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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

Cited by 1 | Viewed by 1609

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

Cited by 1 | Viewed by 2592

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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8 pages, 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

Viewed by 1144

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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9 pages, 6982 KB

Open AccessProceeding Paper

Spatial Assessment and Mapping of Soil Micronutrient Status in Cultivated Lands of Karaikal District, Puducherry, India

by Muhilan Gangadaran, Bagavathi Ammal Uma, Sankar Ramasamy, Mummadi Thrivikram Reddy and Hemavathi Manivannan

Biol. Life Sci. Forum 2025, 54(1), 10; https://doi.org/10.3390/blsf2025054010 - 23 Jan 2026

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Abstract

Soil micronutrient assessment is crucial for ensuring sustainable crop production and environmental quality, particularly in intensively cultivated regions. This study aimed to evaluate and map the spatial distribution of Diethylenetriamine Pentaacetic Acid (DTPA)-extractable micronutrients (Fe, Mn, Zn and Cu) in agricultural lands of [...] Read more.

Soil micronutrient assessment is crucial for ensuring sustainable crop production and environmental quality, particularly in intensively cultivated regions. This study aimed to evaluate and map the spatial distribution of Diethylenetriamine Pentaacetic Acid (DTPA)-extractable micronutrients (Fe, Mn, Zn and Cu) in agricultural lands of Thirunallar commune, Karaikal, for augmenting site-specific nutrient management. A total of 233 geo-referenced surface soil samples (0–20 cm) were collected using a handheld GPS on a pre-defined grid and analyzed for available micronutrients. The spatial variability and distribution patterns were generated in ArcGIS 10.8.2 using semivariogram-based kriging interpolation. The results indicated that Fe, Mn and Cu were sufficient across the study area, with concentrations ranging from 4.74 to 99.80 ppm, 3.70–97.40 ppm, and 1.46–12.40 ppm, respectively, mainly due to the presence of iron-rich minerals, reduced manganese forms, and continuous application of copper-based inputs. Zinc showed greater variability (0.52–17.20 ppm), ranging from deficient to sufficient levels, likely influenced by fertilizer application and organic matter additions. The findings emphasize the importance of site-specific nutrient management to optimize fertilizer usage and crop productivity, particularly in fine-textured clay soils. This study demonstrates the effectiveness of geostatistical approaches for supporting precision agriculture in micronutrient-deficient areas. Full article

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

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11 pages, 250 KB

Open AccessProceeding Paper

Landraces of Barley Exhibit Superior Drought Resistance: Insights from Agro-Morphological and Physiological Analysis

by Abhisek Shrestha, Bharti Thapa, Santosh Marahatta, Krishna Hari Dhakal, Dhurva Prasad Gauchan and Tirth Narayan Yadav

Biol. Life Sci. Forum 2025, 54(1), 11; https://doi.org/10.3390/blsf2025054011 - 28 Jan 2026

Viewed by 599

Abstract

Barley is a marginalized crop subjected to several types of abiotic stress but need to intensify for future climate smart crop. This study investigated the drought resistance of barley landraces focusing on agro-morphological and physiological traits under controlled drought conditions. The experiment employed [...] Read more.

Barley is a marginalized crop subjected to several types of abiotic stress but need to intensify for future climate smart crop. This study investigated the drought resistance of barley landraces focusing on agro-morphological and physiological traits under controlled drought conditions. The experiment employed a two-factorial completely randomized design (CRD) with 14 barley landraces (of which 8 completed the maturity period examination) subjected to drought stress at three growth stages (CRI, tillering, and grain filling). Key parameters such as SPAD values (chlorophyll content), tiller number, and yield attributes were measured and analyzed using drought tolerance indices. Fourteen genotypes were initially tested, of which six failed to reach maturity; eight genotypes completed the full growth cycle and were used for yield and stress index analysis. Results revealed significant genotypic variation in drought response. Eight landraces exhibited higher SPAD values under drought, indicating better photosynthetic retention. Notably, AFU202501 demonstrated high yield stability (Stress Tolerance Index, STI = 1.782) under both stress and non-stress conditions, while Saptari Local showed exceptional drought avoidance (low Stress Susceptibility Index, SSI = −0.068) through early maturity and minimal yield reduction. In contrast, genotypes like Muktinath and NGRC 6010 were highly sensitive to drought, with significant yield losses (49–87%). Physiological traits such as chlorophyll retention and phenological plasticity (e.g., accelerated maturity under stress) were critical for drought adaptation. The findings highlight the potential of landraces like AFU202501 and Saptari Local as genetic resources for breeding climate-resilient barley varieties. The study underscores the importance of integrating traditional landraces into modern breeding programs to enhance food security in drought-prone regions. Full article

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

185 pages, 1188 KB

Open AccessConference Report

The Abstracts of the 3rd International Online Conference on Agriculture

by Bin Gao

Biol. Life Sci. Forum 2025, 54(1), 12; https://doi.org/10.3390/blsf2025054012 - 28 Jan 2026

Cited by 1 | Viewed by 8814

Abstract

This collection presents the accepted abstracts for the 3rd International Online Conference on Agriculture (IOCAG2025), organized by the MDPI journal Agriculture, and held online from 22 to 24 October 2025. The event highlighted integrative solutions and data-driven innovations for building resilient agricultural [...] Read more.

This collection presents the accepted abstracts for the 3rd International Online Conference on Agriculture (IOCAG2025), organized by the MDPI journal Agriculture, and held online from 22 to 24 October 2025. The event highlighted integrative solutions and data-driven innovations for building resilient agricultural systems, spanning topics from climate-smart practices, precision water management, and AI-enhanced smart farming to sustainable crop protection, soil health, and advanced crop genetics. Discussions emphasized translating research into practical applications that balance productivity with environmental stewardship. Collectively, IOCAG2025 showcased forward-looking approaches and collaborative insights aimed at advancing sustainable, efficient, and climate-adapted agriculture on a global scale. Full article

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

11 pages, 2265 KB

Open AccessProceeding Paper

Retrieving Canopy Chlorophyll Content from Sentinel-2 Imagery Using Google Earth Engine

by Tarun Teja Kondraju, Rabi N. Sahoo, Rajan G. Rejith, Amrita Bhandari, Rajeev Ranjan, Devanakonda V. S. C. Reddy and Selvaprakash Ramalingam

Biol. Life Sci. Forum 2025, 54(1), 13; https://doi.org/10.3390/blsf2025054013 - 2 Feb 2026

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Abstract

Google Earth Engine (GEE) has revolutionised remote sensing. The GEE cloud platform lets users quickly analyse large satellite imagery datasets with custom programmes, enhancing global-scale analysis. Crop condition monitoring using GEE would greatly help in decision-making and precision agriculture. Estimating canopy chlorophyll content [...] Read more.

Google Earth Engine (GEE) has revolutionised remote sensing. The GEE cloud platform lets users quickly analyse large satellite imagery datasets with custom programmes, enhancing global-scale analysis. Crop condition monitoring using GEE would greatly help in decision-making and precision agriculture. Estimating canopy chlorophyll content (CCC) is an effective way to monitor crops using remote sensing because leaf chlorophyll is a key indicator. A hybrid model that combines radiative transfer models (RTMs), such as PROSAIL, with Gaussian Process Regression (GPR) can effectively estimate crop biophysical parameters using remote sensing images. GPR has proven to be one of the best methods for this purpose. This study aimed to develop a hybrid model to estimate CCC from S2 imagery and transfer it to the GEE platform for efficient data processing. In this work, the CCC (g/cm²) data from the S2 biophysical processor toolbox for the S2 imagery of the ICAR-Indian Agricultural Research Institute (IARI) on 23 February 2023 were used as observation data to train the hybrid algorithm. The hybrid model was successfully validated against the 155 input data with an R2 of 0.94, RMSE of 10.02, and NRMSE of 5.04%. The model was integrated into GEE to successfully generate a CCC-estimated map of IARI using S2 imagery from 23 February 2023. An R2 value of 0.96 was observed when GEE-estimated CCC values were compared against CCC values estimated locally. This establishes that the GEE-based CCC estimation with the PROSAIL + GPR hybrid model is an effective and accurate method for monitoring vegetation and crop conditions over large areas and extended periods. Full article

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

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9 pages, 692 KB

Open AccessProceeding Paper

Optimizing Microclimate for Maize–Mushroom Intercropping Under Semi-Arid Conditions: A Climate-Smart Farming Approach

by Devanakonda Venkata Sai Chakradhar Reddy, Dheebakaran Ga, Thiribhuvanamala Gurudevan, Sathyamoorthy Nagaranai Karuppasamy, Divya Dharshini Saravanan, Selvaprakash Ramalingam, Hirekari Chandrakant Raj and Sake Manideep

Biol. Life Sci. Forum 2025, 54(1), 14; https://doi.org/10.3390/blsf2025054014 - 3 Feb 2026

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Abstract

Agriculture in semi-arid regions faces increasing challenges from temperature extremes and moisture stress, necessitating climate-smart and resource-efficient production systems. This study examined maize–mushroom intercropping as a climate-smart strategy for semi-arid regions. Field experiments conducted at Tamil Nadu Agricultural University evaluated four maize planting [...] Read more.

Agriculture in semi-arid regions faces increasing challenges from temperature extremes and moisture stress, necessitating climate-smart and resource-efficient production systems. This study examined maize–mushroom intercropping as a climate-smart strategy for semi-arid regions. Field experiments conducted at Tamil Nadu Agricultural University evaluated four maize planting geometries, with and without mulch, in 2022. Results showed that close-maize spacing (45 × 25 cm) with mulch moderated temperature, increased humidity, and improved mushroom yield and biological efficiency. The treatment achieved a land equivalent ratio above one, indicating superior land use efficiency. Optimal microclimatic conditions (26–33 °C; 80–98% RH) enhanced paddy straw mushroom growth, demonstrating that simple field-level modifications can stabilize microclimate and promote resilient farming in semi-arid ecosystems. Full article

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

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6 pages, 419 KB

Open AccessProceeding Paper

Bigger Edible Pods, Smaller Gene Pools? Exploring Trade-Offs Across Inga edulis Mart. Cultivation Systems

by David Draper, Fani Tinitana, Ángel Benítez and Isabel Marques

Biol. Life Sci. Forum 2025, 54(1), 15; https://doi.org/10.3390/blsf2025054015 - 4 Feb 2026

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Abstract

Inga edulis Mart. (Fabaceae) is a multipurpose fruit tree widely cultivated in Ecuador. We explored how contrasting cultivation systems are associated with variation in fruit traits and genetic diversity by comparing agroforestry plantations and traditional home gardens. Trees in agroforestry systems exhibited significantly [...] Read more.

Inga edulis Mart. (Fabaceae) is a multipurpose fruit tree widely cultivated in Ecuador. We explored how contrasting cultivation systems are associated with variation in fruit traits and genetic diversity by comparing agroforestry plantations and traditional home gardens. Trees in agroforestry systems exhibited significantly larger pods whereas home-garden populations showed higher levels of genetic diversity. These patterns suggest a potential trade-off between productivity-oriented management and the maintenance of genetic variation, possibly reflecting differences in management practices and seed sourcing. These results highlight the complementary roles of agroforestry and home gardens in the sustainable use and in situ conservation of I. edulis within traditional landscapes. Full article

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

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9 pages, 813 KB

Open AccessProceeding Paper

by Idrissa Diédhiou, Rosa Isabella Rossi Franco, Josafath A. Otero and Hugo M. Ramírez-Tobias

Biol. Life Sci. Forum 2025, 54(1), 16; https://doi.org/10.3390/blsf2025054016 - 5 Feb 2026

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Abstract

Climate change poses an increasing challenge to tropical agriculture, particularly for heat-sensitive crops such as local varieties of beans (Phaseolus vulgaris L.). This study evaluated the effects of induced passive heat on the root architecture and symbiotic interactions of two local genotypes, [...] Read more.

Climate change poses an increasing challenge to tropical agriculture, particularly for heat-sensitive crops such as local varieties of beans (Phaseolus vulgaris L.). This study evaluated the effects of induced passive heat on the root architecture and symbiotic interactions of two local genotypes, Tayní and Bruncas, using Open Top Chambers under field conditions. Both varieties were included in the analysis and exhibited consistent qualitative responses to warming, with no contrasting cultivar-specific trends detected. Both varieties developed more roots and greater root area compared to the control, while mycorrhizal colonization increased up to 80% under warming. Soil temperature emerged as the main environmental factor influencing root expansion. These findings highlight the adaptive plasticity of local bean varieties under induced passive heat. Full article

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

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7 pages, 1168 KB

Open AccessProceeding Paper

Molecular Characterization and Identification of Endophytic Bacteria from Sugarcane Stalk Against Ringspot Disease of Sugarcane (Epicoccum sorghinum) in Negros Island Region (NIR), Philippines

by Jevie Jaranilla, Sam Michael Decatoria, Romnic Cabelin, Hanzel Pedrosa, Jesimiel Curbita, Ma. May Opino and Mari Neila Quintos

Biol. Life Sci. Forum 2025, 54(1), 17; https://doi.org/10.3390/blsf2025054017 - 11 Feb 2026

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Abstract

Sugarcane productivity in the Philippines is threatened by ringspot disease caused by Epicoccum sorghinum. This study evaluated the antagonistic potential of sugarcane endophytic bacteria against E. sorghinum using Dual Culture (DCA) and Volatile Compound Assays (VCA). Molecular identification via 16S rRNA sequencing [...] Read more.

Sugarcane productivity in the Philippines is threatened by ringspot disease caused by Epicoccum sorghinum. This study evaluated the antagonistic potential of sugarcane endophytic bacteria against E. sorghinum using Dual Culture (DCA) and Volatile Compound Assays (VCA). Molecular identification via 16S rRNA sequencing confirmed the bacterial identities. Burkholderia gladioli exhibited the highest inhibition in DCA (57.79%), while Bacillus zhangzhouensis was most effective in VCA (49.56%). Stenotrophomonas rhizophila also demonstrated inhibitory activity (16.55%). These results indicate that these endophytic strains are promising, sustainable biocontrol alternatives to chemical pesticides for managing sugarcane ringspot disease. Future work should focus on validation in screenhouse and field testing. Full article

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

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9 pages, 1865 KB

Open AccessProceeding Paper

Detection of Respiratory Diseases Based on Poultry Vocalizations Using Deep Learning

by Farook Sattar

Biol. Life Sci. Forum 2025, 54(1), 18; https://doi.org/10.3390/blsf2025054018 - 9 Feb 2026

Viewed by 1054

Abstract

In this study, we design a deep learning-based intelligent recognition method capable of accurately distinguishing abnormal chicken vocalizations among complex sound signals. Our proposed framework is based on the wavelet scattering transform (WST) and a Long Short-Term Memory (LSTM) network, and uses preprocessed [...] Read more.

In this study, we design a deep learning-based intelligent recognition method capable of accurately distinguishing abnormal chicken vocalizations among complex sound signals. Our proposed framework is based on the wavelet scattering transform (WST) and a Long Short-Term Memory (LSTM) network, and uses preprocessed chicken vocalizations processed through a denoising scheme, adopting an audio image generation model (AIGM) based on rectified STFT (Short-Term Fourier Transform). We have used a public chicken language dataset that consists of a total of segments for each of the two categories (Healthy or Sick), totaling 4000 five-second audio clips from actual farming environments, which are labeled by veterinary experts. The proposed method achieves promising performance, outperforming state-of-the-art methods for detecting poultry respiratory diseases and enabling poultry personnel to accurately assess the health and well-being of the chickens. Full article

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

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7 pages, 4435 KB

Open AccessProceeding Paper

AI Audio-Based Poultry Behavior Monitoring Using Vocal Sound Analysis

by Farook Sattar

Biol. Life Sci. Forum 2025, 54(1), 19; https://doi.org/10.3390/blsf2025054019 - 9 Feb 2026

Viewed by 923

Abstract

The aim is to develop a simple and efficient AI audio-based approach to recognize chickens’ key behaviors, such as eating, greeting, foraging, hunting, and tidbitting, to improve poultry farming. First, the proposed study performs cepstral and entropy analyses on the chickens’ vocalizations to [...] Read more.

The aim is to develop a simple and efficient AI audio-based approach to recognize chickens’ key behaviors, such as eating, greeting, foraging, hunting, and tidbitting, to improve poultry farming. First, the proposed study performs cepstral and entropy analyses on the chickens’ vocalizations to extract new vocal features. Second, a simple deep unsupervised clustering method is proposed to recognize the behaviors of the chickens. Alternations in recognized behaviors can be indicators of lameness in chickens. Here, we used an open access chicken language dataset consisting of a total of 74 distinct chicken calls with their probable meanings as based on careful observations. Promising results are obtained by the proposed scheme for chicken behavior monitoring, enabling poultry personnel to accurately determine the health and well-being of chickens. Full article

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

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6 pages, 585 KB

Open AccessProceeding Paper

Groundwater Vulnerability to Pesticide Pollution in a Semi-Arid Agricultural Basin and Electrocoagulation-Based Mitigation

by Benan Yazıcı Karabulut

Biol. Life Sci. Forum 2025, 54(1), 20; https://doi.org/10.3390/blsf2025054020 - 12 Feb 2026

Viewed by 451

Abstract

This study investigates the occurrence and electrochemical removal of four commonly used pesticides—lufenuron, ethoprophos, dichlobenil, and picloram—from groundwater in a semi-arid agricultural basin in Southeastern Türkiye. Groundwater samples were collected from two locations within the study area. At the first sampling site, pesticide [...] Read more.

This study investigates the occurrence and electrochemical removal of four commonly used pesticides—lufenuron, ethoprophos, dichlobenil, and picloram—from groundwater in a semi-arid agricultural basin in Southeastern Türkiye. Groundwater samples were collected from two locations within the study area. At the first sampling site, pesticide concentrations were 0.54 µg/L (lufenuron), 0.14 µg/L (ethoprophos), 0.38 µg/L (dichlobenil), and 0.61 µg/L (picloram), while corresponding values at the second site were 0.48 µg/L, 0.42 µg/L, 0.26 µg/L, and 0.17 µg/L, respectively. An electrocoagulation (EC) process employs aluminum electrodes. Following electrocoagulation treatment, the concentrations of all target pesticides were reduced to levels below the European Union drinking water limit for individual pesticides (0.1 µg/L), as defined by Directive (EU) 2020/2184. Full article

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

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10 pages, 6553 KB

Open AccessProceeding Paper

Comparative Analysis of Raw and Preprocessed Vis–NIR and MIR Spectra for Soil Property Estimation

by Yasas Gamagedara and Nuwan K. Wijewardane

Biol. Life Sci. Forum 2025, 54(1), 21; https://doi.org/10.3390/blsf2025054021 - 13 Feb 2026

Cited by 1 | Viewed by 446

Abstract

Demand for rapid and cost-effective soil analysis has increased the use of spectroscopy, particularly in the visible–near-infrared (Vis–NIR) and mid-infrared (MIR) regions. Using 8304 soil samples from the United States Department of Agriculture spectral library, this study evaluated the effects of raw and [...] Read more.

Demand for rapid and cost-effective soil analysis has increased the use of spectroscopy, particularly in the visible–near-infrared (Vis–NIR) and mid-infrared (MIR) regions. Using 8304 soil samples from the United States Department of Agriculture spectral library, this study evaluated the effects of raw and preprocessed spectra on the prediction accuracy of eleven key soil properties across Vis–NIR and MIR regions using multiple machine learning algorithms. Spectral preprocessing, combining baseline correction and standard normal variate transformation, consistently improved prediction accuracy compared to the raw spectra. Overall, MIR-based models consistently outperformed Vis–NIR across all soil properties, with the largest performance gains observed for potassium, bulk density, and nitrate nitrogen. Among the machine learning approaches evaluated, artificial neural networks and categorical boosting algorithms provided the strongest and most consistent predictive performance across both spectral regions. These findings demonstrate that combining appropriate spectral preprocessing, spectral region selection, and advanced machine learning algorithms can substantially improve soil property prediction using spectroscopy. Full article

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

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9 pages, 663 KB

Open AccessProceeding Paper

From Policy Failure to Collective Self-Consumption: The Penthéréaz Agrivoltaic Energy Community in Switzerland

by Sabrina BenGhida, Sonia BenGhida, Djamil BenGhida and Riad BenGhida

Biol. Life Sci. Forum 2025, 54(1), 22; https://doi.org/10.3390/blsf2025054022 - 13 Feb 2026

Viewed by 390

Abstract

Policy instability and regulatory barriers remain key obstacles to the long-term viability of agriphotovoltaics (APV) deployment. The Penthéréaz case in Switzerland provides empirical evidence of how cooperative governance and collective self-consumption can restore project feasibility after subsidy withdrawal. Using a single-case study and [...] Read more.

Policy instability and regulatory barriers remain key obstacles to the long-term viability of agriphotovoltaics (APV) deployment. The Penthéréaz case in Switzerland provides empirical evidence of how cooperative governance and collective self-consumption can restore project feasibility after subsidy withdrawal. Using a single-case study and process-tracing approach based on cooperative documentation and regulatory records, the analysis explains how Penthéréaz Énergie Photovoltaïque S.A. cooperative (PEP)., initially structured as a subsidy-dependent venture, transitioned into a resilient collective self-consumption network supported by a private micro-grid. Following the withdrawal of federal feed-in tariffs, the project faced major economic risk and responded through decentralized financial restructuring, including community-funded debt at a 2% interest rate. The installation comprises 1180 photovoltaic panels with an installed capacity of 283 kWp, producing approximately 290,000 kWh per year while providing water-tightness and light permeability for agricultural infrastructure. The findings further indicate that operational success contributed to Swiss regulatory adjustments, enabling private distribution networks to cross public roads and secure geographic continuity for local energy sharing. With a reported self-consumption rate of 40% across a diversified user base including agri-food and residential consumers, the case demonstrates the operational value of local load-matching. The findings propose six context-dependent lessons derived from a single case, emphasizing governance capacity, tariff risk management, regulatory adaptability, and demand-oriented system design. Full article

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

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9 pages, 1156 KB

Open AccessProceeding Paper

Integrated Hydroponic Bioelectrochemical Wastewater Treatment Process for Sustainable Agriculture

by Nachiket Aparajithan Magesh, Khin Thandar Tun and Veera Gnaneswar Gude

Biol. Life Sci. Forum 2025, 54(1), 23; https://doi.org/10.3390/blsf2025054023 - 13 Feb 2026

Viewed by 650

Abstract

As the global population grows rapidly, effective wastewater management and resource recovery is increasingly critical. Conventional wastewater treatment is energy-intensive, and it increases reliance on fossil fuel supplies. Microbial Electrochemical Systems (MES) offer a sustainable alternative by treating wastewater and producing renewable electricity. [...] Read more.

As the global population grows rapidly, effective wastewater management and resource recovery is increasingly critical. Conventional wastewater treatment is energy-intensive, and it increases reliance on fossil fuel supplies. Microbial Electrochemical Systems (MES) offer a sustainable alternative by treating wastewater and producing renewable electricity. This study evaluates a combined MES–hydroponic system facilitating Lactuca sativa growth in cathodes via nutrient transport across a cation exchange membrane (CEM) from municipal wastewater being treated in the anode chamber. The system achieved 56 ± 11% COD removal, 48 ± 21% N removal, 4.32 mW/m² peak power density, and a 25% increase in plant wet weight. In contrast, a standard air-cathode cell had 60 ± 15% COD removal, 55 ± 20% N removal, and 0.29 mW/m² peak power density. Findings demonstrate the potential of combined MES–hydroponic systems for wastewater treatment and agriculture in a circular economy framework. Full article

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

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14 pages, 1687 KB

Open AccessProceeding Paper

Influence of LED-Spectra on Yield and Phytochemical Content of Chinese Kale (Brassica oleracea var. alboglabra) in a Hydroponic Vertical Farming System

by Ajit Singh, Loke Kha Chun and Xiaoyu Jiang

Biol. Life Sci. Forum 2025, 54(1), 24; https://doi.org/10.3390/blsf2025054024 - 14 Feb 2026

Viewed by 936

Abstract

Rapid urbanization and population growth demand sustainable food systems. This study investigated hydroponic vertical farming with LED lighting for Chinese kale (Brassica oleracea var. alboglabra), comparing white LEDs (WL), 20% red + 80% blue (20% RL: 80% BL), and 80% red [...] Read more.

Rapid urbanization and population growth demand sustainable food systems. This study investigated hydroponic vertical farming with LED lighting for Chinese kale (Brassica oleracea var. alboglabra), comparing white LEDs (WL), 20% red + 80% blue (20% RL: 80% BL), and 80% red + 20% blue (80% RL:20% BL). Plants grown under control conditions were assessed at weeks 2, 4, and 6. The 80% RL:20% BL treatment enhanced fresh weight, leaf area, root length, and biomass, while 20% RL:80% BL maximized chlorophyll, anthocyanin, and phenolics. Leaf number and quantum yield remained similar, though stress was evident. The findings of this research highlight red-dominant light for growth and blue-dominant light for phytochemical enrichment. Full article

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

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10 pages, 451 KB

Open AccessProceeding Paper

Environmental Assessment of Meat and Milk Production of Sedentary Dual-Purpose Cattle Farms in Two Vegetation Zones of Benin Using the GLEAM-i Model

by Pénéloppe G. T. Gnavo, Rodrigue V. Cao. Diogo and Luc H. Dossa

Biol. Life Sci. Forum 2025, 54(1), 25; https://doi.org/10.3390/blsf2025054025 - 14 Feb 2026

Viewed by 294

Abstract

To comply with new pastoral regulations in Benin, herders are increasingly adopting sedentary cattle systems, which may pose environmental risks if poorly managed. This study assessed greenhouse gas (GHG) emissions from three sedentary cattle farm types: zebu (SZF), taurine (STF), and crossbreed (SCF), [...] Read more.

To comply with new pastoral regulations in Benin, herders are increasingly adopting sedentary cattle systems, which may pose environmental risks if poorly managed. This study assessed greenhouse gas (GHG) emissions from three sedentary cattle farm types: zebu (SZF), taurine (STF), and crossbreed (SCF), across two vegetation zones: Sudanian (SZ) and Guineo-Congolian (GCZ) using the GLEAM-i model, online version. Irrespective of the farm type, the animals were exclusively fed on natural pasture. A total of 12 cattle herds were surveyed to collect input data (herd structure, demographic parameters, milk production and composition, and weight data) for the GLEAM-i. The fat and protein content of the milk (determined using a milkotester device), the live weight, and weight at slaughter of animals were entered into the GLEAM-i, which automatically determines the emission intensity values per kg of protein produced. The results revealed that CH₄ was the main GHG emitted (88%), followed by CO₂ (6–7%) and N₂O (6%). The highest and lowest total GHG emissions (kgCO₂-eq/year) were recorded in SZF (188,497) and STF (52,003) farms, respectively. With regard to emission intensity (kgCO₂-eq/kg protein), this varied from 506.59 to 3043.73 for meat and from 588.86 to 3043.73 for milk. Overall, preliminary trends suggest lower emission intensities for taurine in the GCZ and for zebu in the SZ. However, these results would be more meaningful and more accurate if emission values were directly measured from individual animals using the GreenFeed Technology under current production conditions, using various pasture resources and controlled allocation. These would allow us to make firm recommendations for breeding strategies to reduce GHG emissions in Benin’s extensive livestock production system. Full article

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

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12 pages, 2324 KB

Open AccessProceeding Paper

Engineered Biochar–Nanocomposites Enhanced Vetiver Growth and Nickel Uptake in Ni-Elevated Ultramafic Soils

by Marilou M. Sarong, Paul Jhon G. Eugenio, Gerald Glenn A. Hernandez, Franz Marielle N. Garcia, Ariel G. Mactal, Fernan T. Fiegalan, Maria Luisa T. Mason and Juvy J. Monserate

Biol. Life Sci. Forum 2025, 54(1), 26; https://doi.org/10.3390/blsf2025054026 - 20 Feb 2026

Viewed by 1161

Abstract

Ultramafic soils, particularly those affected by mining, often contain toxic nickel (Ni) levels that hinder plant growth and ecosystem recovery. This study assessed engineered biochar–nanocomposite amendments to improve vetiver (Chrysopogon zizanioides) growth, biomass, and Ni phytoextraction in Ni-rich ultramafic soils from [...] Read more.

Ultramafic soils, particularly those affected by mining, often contain toxic nickel (Ni) levels that hinder plant growth and ecosystem recovery. This study assessed engineered biochar–nanocomposite amendments to improve vetiver (Chrysopogon zizanioides) growth, biomass, and Ni phytoextraction in Ni-rich ultramafic soils from Santa Cruz, Zambales, the Philippines. Seven samples were tested: T₁—control (no application); T₂—biochar; T₃—nanocomposite; T₄—biochar + nano-silica; T₅—biochar + nano-calcium; T₆—biochar + nano-chitosan; and T₇—biochar + nanocomposite. Biochar combined with nano-silica (T4) significantly enhanced vetiver growth, producing the highest root, shoot, and total biomass (469.97 g plant⁻¹), indicating improved plant tolerance under Ni stress. The highest shoot Ni concentration (24.52 mg kg⁻¹) and translocation factor (0.56) were observed in the biochar + nano-chitosan treatment (T6), suggesting increased Ni bioavailability and uptake. However, translocation factor values remained below unity across all treatments, indicating limited Ni transfer from roots to shoots and a dominant phytostabilization behavior. Overall, nano-silica-engineered + biochar primarily enhanced biomass production, while nano-chitosan influenced Ni uptake dynamics, highlighting the potential of engineered biochar–nanomaterial amendments for sustainable rehabilitation of Ni-contaminated ultramafic soils. Full article

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

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11 pages, 1400 KB

Open AccessProceeding Paper

A Comparative Study of Plant Growth Affected by Soil Amendments with Recovered Nutrients, Drought Conditions, and Seasonal Temperatures

by Jackson Lee Sauers, Kambham Raja Reddy and Veera Gnaneswar Gude

Biol. Life Sci. Forum 2025, 54(1), 27; https://doi.org/10.3390/blsf2025054027 - 24 Feb 2026

Viewed by 777

Abstract

Nutrients recovered from municipal and dairy wastewaters in a bioelectrochemical system constructed with terracotta and biochar were used in different soil amendments. These amendments included addition of terracotta (TS), biochar (BS), terracotta and biochar nutrient-rich mixtures from bioelectrochemical systems, DWW (dairy wastewater), and [...] Read more.

Nutrients recovered from municipal and dairy wastewaters in a bioelectrochemical system constructed with terracotta and biochar were used in different soil amendments. These amendments included addition of terracotta (TS), biochar (BS), terracotta and biochar nutrient-rich mixtures from bioelectrochemical systems, DWW (dairy wastewater), and SWW (synthetic wastewater), respectively. Corn growth affected by these amendments was compared with control, termed straight soil (SS). The first experimental setup consisted of 60 plants, four replications per group, and nutrient loadings (0%, 50%, and 100% Hoagland Nutrient Solution, HNS) in the fall season. After harvesting, the plants and soil were analyzed for agro-physical characteristics by various methods. At the 100% nutrient treatment, the TS soil had the best yielding plants. Overall, plants grown in DWW and SWW soil amendments with 0% and 50% nutrient treatments had the best results in plant height, total plant dry weight, the average number of leaves per plant, leaf surface area, shoot dry weight, root/shoot ratio, root surface area, and NBI when compared to the control group. Another test was carried out with 80 corn plants grown using five different soil mediums and using four different nutrient treatments in the spring season. Twenty of the plants were put through a simulated drought to evaluate drought resistance (as measured by plant growth) in different soil amendments. In this test, the SWW soil amendment had a negative effect at 0% HNS and in warm weather. The SWW soil medium had large retention in soil moisture, which had a negative growth effect. It is recommended that the irrigation be monitored closely when applying the SWW soil amendment to avoid overwatering. This research provides critical insights into nutrient reuse in crop production. Full article

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

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6 pages, 376 KB

Open AccessProceeding Paper

Biotic and Abiotic Factors Affecting Cistus ladanifer Production in Cultivated Plots from Mainland Spain

by José Plaza, Lilyana Tihomirova-Hristova, Esther Morate-Gutiérrez, Marta Adalia-Mínguez, Belén Álvarez and Pedro V. Mauri

Biol. Life Sci. Forum 2025, 54(1), 28; https://doi.org/10.3390/blsf2025054028 - 27 Feb 2026

Viewed by 392

Abstract

Interest in the cultivation of rockrose (Cistus ladanifer L.) is focused not only on its commercial products but also on its role in maintaining ecosystems. Very few pests and diseases are known to affect it. However, in cultivated plots in mainland Spain, [...] Read more.

Interest in the cultivation of rockrose (Cistus ladanifer L.) is focused not only on its commercial products but also on its role in maintaining ecosystems. Very few pests and diseases are known to affect it. However, in cultivated plots in mainland Spain, a high number of plants with symptoms of decline and death were observed. This damage occurred after a period of rain of 21 days in late spring, in tilled soil, and with plants grown from cuttings. Laboratory analysis revealed the presence of the phytopathogenic fungal species Macrophomina phaseolina, Fusarium acuminatum, F. equiseti, and F. tricinctum, which were identified by ITS sequencing. These fungi, along with the agronomic and edaphic–climatic conditions, suggest an association of factors involved in the damage observed in the rockroses, pointing out the need to develop an integrated management strategy for this type of agroecosystem. Full article

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

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10 pages, 847 KB

Open AccessProceeding Paper

Enhancing Precision Farming Security Through IoT-Driven Adaptive Anomaly Detection Using a Hybrid CNN–PSO–GA Framework

by Faruk Salihu Umar and Nurudeen Mahmud Ibrahim

Biol. Life Sci. Forum 2025, 54(1), 29; https://doi.org/10.3390/blsf2025054029 - 28 Feb 2026

Viewed by 756

Abstract

The adoption of Internet of Things (IoT) technologies has significantly enhanced precision farming by enabling continuous environmental monitoring and data-driven agricultural management. However, the increasing reliance on distributed sensor networks introduces critical challenges, including sensor faults, data anomalies, and cyber-physical security threats, which [...] Read more.

The adoption of Internet of Things (IoT) technologies has significantly enhanced precision farming by enabling continuous environmental monitoring and data-driven agricultural management. However, the increasing reliance on distributed sensor networks introduces critical challenges, including sensor faults, data anomalies, and cyber-physical security threats, which can undermine system reliability and decision accuracy. This study proposes an IoT-driven anomaly detection framework for smart agriculture that integrates a Convolutional Neural Network (CNN) optimized using a hybrid Particle Swarm Optimization and Genetic Algorithm (PSO–GA). The CNN learns complex spatio-temporal patterns from multivariate sensor data, while the PSO–GA strategy automatically tunes CNN hyperparameters to improve detection accuracy and model stability. To enhance adaptability under dynamic agricultural conditions, the proposed framework incorporates an online learning mechanism that incrementally updates the CNN model using newly arriving sensor data, enabling continuous adaptation to environmental changes and concept drift without full model retraining. Experiments conducted on a publicly available smart agriculture dataset demonstrate that the proposed CNN–PSO–GA framework achieves an accuracy of 74%, precision of 74%, recall of 100%, and an F1-score of 85%, outperforming baseline methods such as One-Class Support Vector Machine and Isolation Forest, particularly in reducing missed anomaly events. The results confirm the robustness, adaptability, and reliability of the proposed approach. Overall, the framework provides a practical and scalable solution for enhancing security, resilience, and operational effectiveness in precision farming systems. Full article

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

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8 pages, 956 KB

Open AccessProceeding Paper

Socioeconomic Determinants of Drip Irrigation Adoption in Semi-Arid India: Evidence from Sangamner, Maharashtra

by Anshika Parihar

Biol. Life Sci. Forum 2025, 54(1), 30; https://doi.org/10.3390/blsf2025054030 - 2 Mar 2026

Viewed by 684

Abstract

The Sangamner Block in Ahilyanagar District (formerly Ahmednagar), Maharashtra, India, is a semi-arid region that receives low rainfall and is experiencing declining groundwater levels, exacerbating the vulnerability of farmers. This study employed a cross-sectional survey covering 159 farming households from six villages to [...] Read more.

The Sangamner Block in Ahilyanagar District (formerly Ahmednagar), Maharashtra, India, is a semi-arid region that receives low rainfall and is experiencing declining groundwater levels, exacerbating the vulnerability of farmers. This study employed a cross-sectional survey covering 159 farming households from six villages to examine the socioeconomic factors influencing the adoption of drip irrigation. Binary logistic regression identified education, social group, FPO membership, land size, and age as significant predictors. It shows that lower education, smaller landholdings, scheduled tribe status, and lack of farmer producer organisations (FPOs) membership can significantly reduce the likelihood of drip adoption while age and OBC (social group) status increase it. In contrast, neither gender nor government schemes had a significant effect. Full article

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

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10 pages, 362 KB

Open AccessProceeding Paper

Reinvigoration of Deteriorated Seeds of Two Okra Varieties (Abelmoschus esculentus var ‘Smooth Green’ and var ‘Red Ruby’) Using Atmospheric Pressure Plasma Activated Water

by Alangelico San Pascual, Catherine Joy Dela Cruz, Maurice Gravidez, Annalissa Aquino and Glaisa Garcia

Biol. Life Sci. Forum 2025, 54(1), 31; https://doi.org/10.3390/blsf2025054031 - 3 Mar 2026

Viewed by 678

Abstract

Deteriorated seeds of okra varieties ‘Smooth Green’ and ‘Red Ruby’ were reinvigorated by soaking in plasma-activated water (PAW). After PAW treatment, germination and seedling characteristics were determined and compared with hydroprimed deteriorated seeds, unprimed deteriorated seeds, and healthy untreated seeds. Differences among treatments [...] Read more.

Deteriorated seeds of okra varieties ‘Smooth Green’ and ‘Red Ruby’ were reinvigorated by soaking in plasma-activated water (PAW). After PAW treatment, germination and seedling characteristics were determined and compared with hydroprimed deteriorated seeds, unprimed deteriorated seeds, and healthy untreated seeds. Differences among treatments were analyzed using ANOVA and Tukey’s HSD test at α = 0.05. PAW-treated deteriorated seeds exhibited higher vigor and faster germination in both varieties compared with untreated seeds. Furthermore, PAW-treated seedlings developed longer and heavier shoots and roots. It is concluded that soaking deteriorated seeds in PAW enhanced germination by 12–16%. These findings demonstrate that PAW treatment can improve the germination and seedling characteristics of deteriorated okra seeds. Full article

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

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11 pages, 592 KB

Open AccessProceeding Paper

Genetically Modified Crops as a Strategy for Reducing Pesticide Dependence in Sub-Saharan Africa: Exploring Benefits, Adoption Constraints and Policies

by Chijioke Christopher Uhegwu and Christian Kosisochukwu Anumudu

Biol. Life Sci. Forum 2025, 54(1), 32; https://doi.org/10.3390/blsf2025054032 - 11 Mar 2026

Viewed by 1585

Abstract

The overreliance on chemical pesticides in sub-Saharan African (SSA) for agriculture poses major challenges to sustainable agriculture, ecosystem and human health, biodiversity, and environmental sustainability. While genetically modified (GM) crops have demonstrated potential to lower pesticide use and increase crop yield, their widespread [...] Read more.

The overreliance on chemical pesticides in sub-Saharan African (SSA) for agriculture poses major challenges to sustainable agriculture, ecosystem and human health, biodiversity, and environmental sustainability. While genetically modified (GM) crops have demonstrated potential to lower pesticide use and increase crop yield, their widespread adoption remains limited across SSA, with gaps in knowledge on their yield, benefits and policies impacting their uptake. In this study, a literature-based approach was used to synthesize evidence from peer-reviewed articles and government reports published between 2010 and 2025 on pesticide use, farm productivity, and wellbeing of farmers across three focus countries: Nigeria, South Africa, and Burkina Faso. The summary of approved GM crops, events and utilisation across the three focus countries was also retrieved from the International Service for the Acquisition of Agri-biotech Applications (ISAAA) database. Cross-country comparisons were conducted to highlight lessons learned from successful and stalled GM crop programs and to identify regulatory, socio-cultural, and economic factors shaping adoption. It is shown that while GM crops can significantly reduce pesticide usage and production costs, challenges such as public hesitancy, regulatory hurdles, limited farmer awareness, and concerns about ecological consequences continue to hinder wider uptake across the continent. Similarly, weak seed systems and the lack of regionally harmonized biosafety regulations also constrain adoption. In areas where GM crops have been successfully adopted, it was demonstrated that supportive policy frameworks, transparent biosafety regulations, effective seed certification and distribution systems, and sustained community engagement increased farmer confidence and accelerated adoption. Hence, for GM crops to be more widely adopted for sustainable crop protection in sub-Saharan Africa, governments and stakeholders must strengthen biosafety systems, invest in farmer education, promote regional regulatory coordination, and facilitate public–private partnerships. Full article

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

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8 pages, 4783 KB

Open AccessProceeding Paper

A Hybrid Machine Learning Approach for Monitoring Wheat Crop Traits Using Proximal Hyperspectral Remote Sensing

by Rajan G. Rejith, Rabi N. Sahoo, Tarun Kondraju, Amrita Bhandari and Rajeev Ranjan

Biol. Life Sci. Forum 2025, 54(1), 33; https://doi.org/10.3390/blsf2025054033 - 23 Mar 2026

Viewed by 600

Abstract

This study employs a hybrid methodology that integrates a physical process-based radiative transfer (RT) model and machine learning regression to assess three key wheat crop traits: leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC). The non-imaging hyperspectral data [...] Read more.

This study employs a hybrid methodology that integrates a physical process-based radiative transfer (RT) model and machine learning regression to assess three key wheat crop traits: leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC). The non-imaging hyperspectral data collected proximally using the ASD FieldSpec Spectroradiometer were spectrally resampled to 269 spectral bands ranging from 400 to 1000 nm for the retrieval of these crop traits. Upon validating against in situ measurements, good accuracies in terms of NRMSE values, 10.65%, 11.63%, and 13.85%, were achieved for LAI, LCC, and CCC, respectively. Full article

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

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5 pages, 185 KB

Open AccessProceeding Paper

Evaluating the Nutritional Value of Fruits of Two Edible Wild Monkey Kola Species of West African Origin

by Effiom Eyo Ita, Peggy Obaseojei Willie, Ayobami Daniel Abodunrin, Julius Oyohosuho Phillip, Anthony Agbor and Michael Bissong

Biol. Life Sci. Forum 2025, 54(1), 34; https://doi.org/10.3390/blsf2025054034 - 1 Apr 2026

Viewed by 487

Abstract

Monkey kola is a common name given to the edible wild relatives of the West African kolanut. These are neglected and underutilized indigenous tropical fruit species growing in the West and Central African forests. Knowledge of the nutrient and antinutrient composition of the [...] Read more.

Monkey kola is a common name given to the edible wild relatives of the West African kolanut. These are neglected and underutilized indigenous tropical fruit species growing in the West and Central African forests. Knowledge of the nutrient and antinutrient composition of the fruits is highly inadequate. In this study, mature fruits of two wild monkey kola species (Cola pachycarpa and Cola lepidota) were collected and evaluated for phytochemical, proximate, vitamin and mineral composition. There were significant (p < 0.05) differences in the proximate, mineral, and vitamin composition of the two species. C. lepidota was richer in moisture, protein, fat, alkaloids, phosphorus, calcium, and iron, while C. pachycarpa was richer in ash content, crude fibre, flavonoids, magnesium, potassium, sodium, zinc, vitamin B2, vitamin B3, vitamin C, vitamin A, and vitamin E. However, there were no significant (p > 0.05) differences between the two monkey kola species in their saponins, tannins and phytate composition. The abundance of a wide variety of minerals, vitamins and other bioactive compounds in both kola species justifies their wide consumption by natives in regions where they grow. The fruits of these species should be fully exploited for their potential health benefits. Full article

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

13 pages, 2093 KB

Open AccessProceeding Paper

Monitoring Agricultural Vegetation Health Under Climate Stress Using NDVI and LST Indices in the Sylhet Region

by Sk. Tanjim Jaman Supto and Md. Nurjaman Ridoy

Biol. Life Sci. Forum 2025, 54(1), 35; https://doi.org/10.3390/blsf2025054035 - 15 Apr 2026

Viewed by 493

Abstract

Agricultural ecosystems in northeastern Bangladesh are increasingly vulnerable to climate-induced stressors, particularly rising temperatures and seasonal droughts. While previous research has examined the climate’s impact on agriculture in broader contexts, no study has specifically investigated long-term seasonal vegetation and thermal dynamics in Sylhet. [...] Read more.

Agricultural ecosystems in northeastern Bangladesh are increasingly vulnerable to climate-induced stressors, particularly rising temperatures and seasonal droughts. While previous research has examined the climate’s impact on agriculture in broader contexts, no study has specifically investigated long-term seasonal vegetation and thermal dynamics in Sylhet. This study addresses this gap by assessing spatio-temporal variations in vegetation health under climate stress in the Sylhet region from 2005 to 2025 using remote sensing techniques. To investigate this problem, the study derived the Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) from Landsat satellite imagery and evaluated their seasonal behavior across the major cropping periods Rabi, Kharif I, and Kharif II. The relationship between vegetation health and surface temperature was examined using Pearson’s correlation matrix along with a statistical comparison to identify change patterns, transitions among vegetation and thermal stress classes, and the seasonal intensity of climate stress. The findings indicate that increased LST generally corresponds with reduced vegetation cover in lowland agricultural zones, whereas elevated areas with forest or tree covers show an opposite response. Distinct spatial hotspots of thermal stress and drought-prone zones were also identified, particularly during the dry Rabi season. These results highlight the idea that rising LST corresponds with declining NDVI values, indicating that increasing thermal stress and potential reductions in agricultural vegetation productivity and climate stress across Sylhet’s agricultural landscape have intensified markedly from 2005 to 2025, with clear seasonal differences in vulnerability. NDVI analysis reveals a consistent decline in vegetation health, while LST patterns show widespread transitions from moderate to high and severe thermal stress, particularly during the Kharif seasons. The observed NDVI decline under elevated LST conditions indicates reduced vegetation vigor and potential productivity within agricultural lands, rather than a direct reduction in cultivated areas, since NDVI primarily captures vegetation density and physiological condition. The strongest NDVI–LST inverse relationship occurs in Rabi and Kharif I, indicating vegetation’s cooling role, whereas this linkage weakens in Kharif II due to dominant monsoon-driven atmospheric controls. Full article

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

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8 pages, 17820 KB

Open AccessProceeding Paper

Development of an Empirical Model for Estimating Quinoa Canopy Cover from NDVI Under Different Irrigation and Fertilization Stress Conditions

by Lamia Jallal, Salah Er-Raki, Saïd Khabba, Jamal Ezzahar, Zaineb Bouswir, Hiba Ait Ben Ahmed, Abdelilah Meddich and Abdelghani Chehbouni

Biol. Life Sci. Forum 2025, 54(1), 36; https://doi.org/10.3390/blsf2025054036 - 1 Apr 2026

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Abstract

Canopy cover (CC) is crucial for crop monitoring and model calibration. This study developed an empirical equation relating NDVI to CC for quinoa under four treatments with different irrigation and fertilization levels in Morocco’s water-scarce Chichaoua region. Treatments ranged from optimal (100% irrigation, [...] Read more.

Canopy cover (CC) is crucial for crop monitoring and model calibration. This study developed an empirical equation relating NDVI to CC for quinoa under four treatments with different irrigation and fertilization levels in Morocco’s water-scarce Chichaoua region. Treatments ranged from optimal (100% irrigation, 100% fertilization) to severe stress (40% irrigation, 25% fertilization), tested from March to June 2023, showing strong NDVI-CC correlations (0.77–0.98). Cross-validation identified the best-performing model, CC (%) = 141.75 × (NDVI) − 30.913, derived from moderate stress conditions. This linear equation demonstrated good predictive accuracy across all treatments (R² = 0.60–0.96, RMSE = 8.79–14.99 (% CC), NRMSE = 0.26–0.36, EF = 0.54–0.74, d = 0.77–0.90), providing a practical tool for estimating quinoa canopy cover in water-limited environments. Full article

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

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8 pages, 1175 KB

Open AccessProceeding Paper

Influence of Cow Parity on the Precision of Near-Infrared Spectroscopic Sensing System for Assessing Milk Quality During Milking

by Patricia Iweka, Shuso Kawamura, Tomohiro Mitani and Takashi Kawaguchi

Biol. Life Sci. Forum 2025, 54(1), 37; https://doi.org/10.3390/blsf2025054037 - 19 May 2026

Viewed by 335

Abstract

This study examined how cow parity (number of calvings) affects the accuracy of near-infrared (NIR) spectroscopy for real-time milk assessment. Using two cows in their second calving at Hokkaido University, milk spectra (700–1050 nm) were analyzed alongside reference measurements of fat, lactose, and [...] Read more.

This study examined how cow parity (number of calvings) affects the accuracy of near-infrared (NIR) spectroscopy for real-time milk assessment. Using two cows in their second calving at Hokkaido University, milk spectra (700–1050 nm) were analyzed alongside reference measurements of fat, lactose, and somatic cell count (SCC). Calibration models were built with data from first, second, and combined parities using partial least squares regression. Results showed similar prediction accuracy for fat and SCC across parities but notable differences for lactose. Validation across parities revealed that parity significantly influences NIR system precision, particularly in lactose measurement accuracy. Full article

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

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