02. AGRICULTURE AND FORESTRY

CORRELATION ANALYSIS OF GERMINATION PERCENTAGE AND EARLY GROWTH PARAMETERS OF TOMATO (Solanum lycopersicum) UNDER SEED NANO-PRIMING WITH GREEN-SYNTHESIZED CuO NANOPARTICLES

Wed, 01 Jan 2025 00:00:00 GMT

CORRELATION ANALYSIS OF GERMINATION PERCENTAGE AND EARLY GROWTH PARAMETERS OF TOMATO (Solanum lycopersicum) UNDER SEED NANO-PRIMING WITH GREEN-SYNTHESIZED CuO NANOPARTICLES Madusanka, H. K. S.; Aruggoda, A. G. B.; Chathurika, J. A. S.; Weerakoon, S. R. This study explored the effects of seed nano-priming using green-synthesized copper oxide nanoparticles (CuO NPs), prepared from Mimosa pigra leaf extract, on tomato (Solanum lycopersicum) germination and early growth. CuO NPs were synthesized via plant-mediated bioreduction and characterized using UV-visible spectroscopy, SEM, XRD, and FTIR. UV-visible analysis showed a surface plasmon resonance peak at 224 nm, while SEM revealed nanoscale morphology averaging 108 nm. XRD confirmed a crystalline monoclinic structure with peaks at 32.59°, 35.6°, 36.49°, 38.73°, and others, indexed to (110), (002), (111), (202), (311), and (220) planes (JCPDS 45-0937). FTIR spectra showed Cu–O vibrational bands in the fingerprint region (560.42–595.45 cm⁻ ¹), supporting nanoparticle formation. Tomato seeds were treated with CuO NP suspensions (0–1000 ppm) and evaluated for germination percentage, root length, shoot length, and fresh weight. Higher concentrations generally reduced seedling vigour. Pearson correlation analysis revealed a strong positive correlation between fresh weight and root length (r = 0.779, p < 0.05), moderate with germination percentage (r = 0.695), and weak with shoot length (r = 0.248). These results suggest that biomass accumulation is more closely linked to root development than shoot elongation. The study highlights the potential of CuO NPs in seed priming and recommends optimizing concentrations for improved seedling performance and further field-level evaluation.

PREDICTIVE ANALYTICS FOR RICE CANOPY MICROCLIMATE USING WEATHER FORECAST DATA AND BG-358 MONITORING

Wed, 01 Jan 2025 00:00:00 GMT

PREDICTIVE ANALYTICS FOR RICE CANOPY MICROCLIMATE USING WEATHER FORECAST DATA AND BG-358 MONITORING Bandara, K. M. N. P.; Wickramasinghe, B. M. G. S. T. S. K. Rice (Oryza sativa) is a primary food source for over half the global population, yet its yield is increasingly threatened by climate-induced microclimatic variations, particularly during sensitive growth stages like flowering. This study presents the development of a Machine Learning (ML)-based Early Warning System (EWS) designed to predict adverse canopy microclimate conditions, specifically temperature, humidity, and wind that influence rice yield and spikelet sterility. Utilizing high-resolution microclimatic data from the BG-358 rice canopy monitoring system, a locally developed IoT-based platform for real-time monitoring of microclimate, data were collected at 2-minute intervals from 2019 to 2024, resulting in a dataset of approximately 200,000 data points for two flowering seasons (each nearly 1 month) per year at RRDI Bathalagoda. These data, combined with NASA global forecast inputs, were split into 70% for training and 30% for testing, ensuring robust evaluation and reliable predictions to support proactive agricultural decision-making. The study employed Random Forest, Support Vector Machines, and Neural Networks to predict canopy temperature, relative humidity (RH), and wind conditions. After data pre-processing, including normalization and temporal feature engineering, models were evaluated using MAE, RMSE, and R² metrics. The Random Forest model demonstrated superior performance with an MAE of 0.78 °C, RMSE of 1.15 °C, and R² of 0.91 for temperature prediction. RH and wind predictions also showed high accuracy, with RH remaining above the 60% critical threshold in most cases, though wind forecasts, especially above the 20 km/h threshold, exhibited greater variability due to localized microclimatic conditions. Integrating real-time canopy data with weather forecasts enhanced prediction robustness, enabling heat-induced spikelet sterility anticipation up to 48 hours in advance with 85% reliability. The developed EWS incorporates real-time dashboards and automated alerts, demonstrating strong potential in mitigating climate-related rice yield losses and supporting sustainable agriculture. Future work will expand datasets across varieties and regions and foster collaboration with agricultural stakeholders for wider adoption and broader-scale yield forecasting to strengthen food security planning.

AI-POWERED MOBILE ASSISTANT FOR PRECISION CINNAMON FARMING USING REAL-TIME WEATHER AND SOIL DATA IN THE SOUTHERN PART OF SRI LANKA

Wed, 01 Jan 2025 00:00:00 GMT

AI-POWERED MOBILE ASSISTANT FOR PRECISION CINNAMON FARMING USING REAL-TIME WEATHER AND SOIL DATA IN THE SOUTHERN PART OF SRI LANKA Deemantha, L. T.; Wickramasinghe, B. M. G. S. T. S. K. Ceylon cinnamon (Cinnamomum verum) is a globally renowned agricultural product and a key contributor to Sri Lanka’s economy, particularly in the Southern Province. However, cinnamon farmers face persistent challenges due to climate variability, declining soil health, and limited access to real-time, localized agricultural insights, leading to suboptimal decision-making. To address these issues, this study presents the design, development, and evaluation of an Artificial Intelligence (AI)-powered mobile assistant aimed at enhancing precision cinnamon farming through the integration of real-time weather forecasts and soil condition data. The system was developed using the Flutter framework with Firebase backend services, incorporating RESTful Application Programming Interface (API)s for weather and soil data integration, while TensorFlow Lite enables on-device AI inference to ensure low-latency recommendations. Random Forest is used for predictive analytics, providing personalized, location-specific recommendations on irrigation scheduling, fertilization, plantation planning, and pest management. The assistant also supports multilingual interactions in Sinhala and Tamil, improving accessibility for rural farmers. Data were sourced from the Sri Lankan Department of Agriculture, meteorological services, public weather APIs, and scientific literature, complemented by field surveys and controlled experiments across three cinnamon cultivation sites over 6 months. The evaluation involved 20 AI-assisted farmers and a control group of 20 farmers following traditional practices. Field trials showed significant improvements: crop yields increased by 8-12%, water usage decreased by 15%, fertilizer usage reduced by 10%, and pesticide use lowered by 7% without compromising productivity. Over 80% of participants reported high satisfaction, citing usability and multilingual support as key enablers. This solution addresses gaps in existing agricultural platforms by delivering real-time, localized decision support tailored to the cinnamon sector. Future enhancements will integrate pest detection, disease prediction, and broader regional deployment, contributing to climate-resilient, sustainable, and data-driven agriculture in Sri Lanka.

COMPARISON OF ANALYTICAL METHODS FOR DETERMINING NON- EXCHANGEABLE STABLE HYDROGEN RATIOS IN TEA

Wed, 01 Jan 2025 00:00:00 GMT

COMPARISON OF ANALYTICAL METHODS FOR DETERMINING NON- EXCHANGEABLE STABLE HYDROGEN RATIOS IN TEA Binduhewa, K. M.; Amalka, B. A. T.; Dissanayake, D. C. K.; Ranatunga, M. A. B.; Chandrajith, R.; Hettiarachchi, L. S. K. Determining the δ2H value of organic compounds is challenging due to the presence of moisture traces from ambient humidity, hygroscopic compounds, and exchangeable non-bound hydrogen atoms. Several approaches have been developed to avoid the interference of exchangeable hydrogen. However, these approaches are comparatively difficult to use for the δ2H analysis of tea samples due to a lack of suitable solid reference materials for two-point calibration and time-consuming procedures. This study adapts and scales the method for lignin analysis, targeting methoxy groups to measure non-exchangeable hydrogen for the analysis of tea samples using gas chromatography coupled with isotope ratio mass spectrometry (GC-IRMS). It aims to compare the mentioned method with bulk δ²H analysis of tea samples using equilibration techniques that involve different isotopic compositions and laboratory moisture. A total of 45 orthodox black tea samples were collected from three different tea growing regions with varying elevations in Sri Lanka: Nuwara Eliya (n=15), Kandy (n=15), and Ruhuna (n=15), and δ2H values were measured using three different methods: GC-IRMS analysis of methoxy group, water vapor equilibration (enriched δ²H) and laboratory moisture equilibration. One-way analysis of variance (ANOVA) was performed, and the results indicated a statistically significant difference in the averages of δ²H values between the three methods (p < 0.01). The GC-IRMS method for analysing δ²H in the methoxy groups of tea samples was the most reliable and precise method for analysing δ²H in tea, as it offered consistent δ²H values with low variability. There was a slight variability due to natural variations in the δ²H of the non-exchangeable hydrogen in different elevations. It provided a clear separation between regions with minimal overlap. The laboratory moisture equilibrium and water vapor equilibration methods demonstrated high variability and lower precision, which may be due to environmental factors such as temperature and humidity. In addition, these methods were time-consuming compared to the GC-IRMS method for analysing the δ2H in methoxy groups. Therefore, GC-IRMS methoxy group analysis is a robust method for δ²H determination in tea with precision and consistency.