Coffee Robusta (Coffea canephora) increased its total production by 73.5% during the first quarter of 2023. In this study, twenty (20) samples each of healthy and infected coffee leaves were measured for their transmittance properties in the UV-Vis and NIR regions. Coffee Leaf Miner (CLM)-infected leaves were identified based on translucent patches on the plant foliage. The results showed that a healthy coffee leaf has a mean transmittance of 41.53 μW for the NIR region, while for the infected leaves, the mean transmittance is 47.06 μW. Healthy coffee Robusta leaves showed significant differences in their transmittance properties compared to infected coffee Robusta leaves in the UV (r = −0.15, p = 0.021, F = 5.8, t = −0.286), visible (r = −0.15, p = 0.018, F = 6.11, t = −2.88), and NIR (r = −0.14, p = 0.027, F = 5.28, t = −2.99) regions. A CLM index was introduced based on the intensity ratio of green and red wavelengths. I535/575 showed positive correlation with the estimated chlorophyll-a concentration for healthy (r = 0.94, p = 0.227) and infected (r = 0.56, p = 0.622) leaves. This method leads to the development of portable sensors for the early detection of CLM pests in plants.

Moisture content regulation of root crops is crucial in post-harvest processing operations, not only in the price stipulation but also to avoid aflatoxin contamination. To prolong their storage life, they are processed into dried chips to extend their usability in feed formulations and starches. In this study, we use the capacitance-based method to evaluate the performance of an analogue-based electronic meter for the cassava, sweet potato, and taro chips. The meter was calibrated against the oven-drying method, yielding high R 2 values of the different root crops. The established calibration models were validated and revealed high R 2 values with 0.9580 for the cassava, 0.9958 for the sweet potato, and 0.9798 for the taro. The trendline equations are y = 59.44x 0.56 , y = 54.38x 0.47 , and y = 52.94x 0.62 , respectively. The results revealed that the moisture meter is capable of reading the moisture content on a weight basis (% MC wb) with accuracy and reliability at specified limits of 8% < x < 69% for the cassava, 15% < x < 59% for the sweet potato, and 9% < x < 57% for the taro. This study presents the performance of a portable analogue-based moisture meter as a reliable and accessible solution to small-scale operations, especially for farmers, offering an on-site rapid moisture content measurement in root crop processing.

The use of Vegetation Indices (VIs) is important in remote sensing for inferring biophysical parameters and monitoring environmental processes. Since VIs are derived from reflectance measurements, the scale and platform of acquisition-satellite, commercial multispectral drone, or simulation model—can potentially influence their derived values and inter-relationships. This study addresses this influence by presenting a multiscale comparison of four common indices: MPRI, NDVI, NDWI, and OSAVI. Data were sourced from three distinct platforms: Sentinel-2 satellite imagery, high-resolution data acquired via a multispectral drone, and a simulated spectral library generated using the PROSPECT-4 via ARTMO software. The core objective was to assess the consistency of the correlational structure among these VIs across the three scale measurements. Results indicate a statistically significant similarity in the overall correlational pattern of the indices, suggesting that the intrinsic mathematical relationships between these VIs are largely scale-invariant. However, the study identifies a critical need to standardize the data acquisition and processing protocol for drone measurements to match the known consistency of the Sentinel-2 mission and the rigorous parameters of the PROSPECT-4 simulation. This standardization is essential for future multi-sensor integration and calibration efforts.

Rapid urbanization and industrialization affected the air quality in the Philippines. Fine particulate matter (PM2.5) are of particular concern due to their health, environmental, as well as climate effects. Due to the lack of active and available air quality monitoring in the Philippines, air quality monitoring and mitigation cannot be performed. Satellite air quality data can be utilized to provide extensive spatial and temporal coverage. In this study, aerosol optical depth (AOD) data from the Geostationary Environment Monitoring Spectrometer (GEMS) onboard the GEO-COMPSAT-2B satellite was used to estimate PM2.5 and compared with data from a ground monitoring station in Manila, Philippines along with meteorological data from the European Centre for Medium- Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5). Random forest (RD), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) were evaluated for their accuracy in predicting ground-level PM2.5. SVM achieved the highest accuracy (R2: 0.998) followed by RF (R2: 0.997), and then XGBoost (R2: 0.673). SHAP analysis showed that wind speed has the highest contribution in predicting PM2.5 This study shows that satellite air quality data can be used for ground-level PM2.5 estimation.

Philippine inland waters experience increasing pressures of nutrient enrichment and algal blooms which threaten water quality, biodiversity, and human health. Chlorophyll monitoring, the most critical indicator of algal biomass, plays a key role in eutrophication evaluation and identification of harmful algal blooms (HABs). Satellite and unmanned aerial vehicle (UAV)-based remote sensing technology provides large area chlorophyll-a mapping, with accuracy dependent on precise ground-truth measurements for calibration and validation. This paper presents the design and verification of a low-cost fluorescence spectrometer for potential in-situ chlorophyll-a measurement that supports geospatial benchmarking and the validation of remote sensing. The spectrometer consists of a Hamamatsu C12880MA mini spectrometer contained in a Raspberry Pi microcontroller with an LED-based excitation source to detect chlorophyll a fluorescence emission in Chlorella vulgaris samples. Laboratory tests compared the developed spectrometer with a commercial Ocean Optics fluorescence spectrometer, and with a high correlation between excitation spectra (R2 = 0.9304) and moderate correlation between emission spectra (R2 = 0.7269), validating that the system can detect chlorophyll-a fluorescence signals as relevant to the spectral bands used in satellite-based water quality indexes. Though proven under laboratory conditions, the low-cost, compact design provides for prospective field deployment for real-time monitoring. The built fluorescence spectrometer thus provides an emerging, scalable approach to ground-based spectral measurement, bridging laboratory instrumentation and geospatial remote sensing applications to improve the accuracy and temporal resolution of freshwater quality monitoring.

Fast-developing countries, particularly in Southeast Asia, are critically susceptible to high concentrations of inhalable fine particulate pollution (PM2.5), which threatens public health and economic development. This study evaluates the incremental reduction in PM2.5 concentrations and its potential health and economic benefits, focusing on sustainable air quality management in vulnerable communities, particularly in the fisheries sector in the Philippines. Using satellite-derived PM2.5 data and the Environmental Benefits Mapping and Analysis Program–Community Edition (BenMAP-CE) model, the estimated premature mortality rates and the associated costs under various concentration reduction scenarios (25%, 50%, 75%, and 100%) for the regions of Navotas, Bohol, and Davao Del Sur revealed substantial health and economic benefits. Under 25–50% reduction scenarios, it could prevent annual premature mortalities of 55–104 in the three regions, generating approximately USD 1.15 million in monetary benefits. A more considerable 75–100% reduction scenario could prevent up to 206 mortalities annually, yielding USD 2.07 million in monetary benefits. These benefits were notable in areas with higher baseline PM2.5 concentrations, such as Navotas and Davao Del Sur, which experienced significant reductions in premature mortality within the range of 1–3% of the fisherfolk population. These findings highlight the incremental reduction strategies in a sector-specific approach to protect vulnerable communities crucial for economic development. The developed approach aims to improve the air quality in fishing-dependent regions to ensure sustainable livelihoods across the Philippines while meeting national and global health targets.