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.

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.

Algal Organic Matter (AOM) is derived from the dissolved organic matter composition of the algal species being observed. In this study, excitation–emission fluorescence spectroscopy was used to determine Chlorella sp.’s AOM and pigment characteristics in varying algal biomass concentrations. The AOM and pigment characteristics were observed at 400–600 nm and 600–800 nm fluorescence emission, respectively, with an excitation spectrum of 300–450 nm. F450/680 was computed based on the ratio between the dissolved organic matter contribution at 450 nm and chlorophyll-a at 680 nm. F450/680 positively correlated with algal biomass (r = 0.96) at an excitation wavelength of 405 nm. This study is a good reference for those interested in algal biomass estimation and production in natural waters.

Precision agriculture is one of the emerging technologies that is promising to solve the problem of food insecurity worldwide. These focus on collecting, analyzing, and taking actions based on data available from the crop and its environment. Building low-cost and reliable plant health-related sensors is critical and helpful in the agriculture industry. This study builds a leaf reflectance sensor comprising a white LED source and an S1133 photodiode detector. The angle between the source and detector varied from 30°, 45°, 60°, and 90° to determine the angle at which it would have an optimal reflectance value. The white LED source was connected to a 3-volt and 0.3-ampere power supply, while the S1133 photodiode detector was connected to an oscilloscope to measure the response voltage. Different green intensities were used using an RGB color scheme that imitates the color of the leaf that characterizes its health status. Reflectance intensities were calibrated using white standard reflectance. The result shows that the 45° angle between the source and detector gives the highest R-squared value (R² = 0.958). This study provides an overview of the effects of varying detection angles for crop reflectance sensors that can be used to assess plant health status and help improve crop yield in the agricultural sector.

This study explores the relationship between nutrient concentration (NC) and epidermal thickness (d) of the leaves of hydroponically grown Brassica rapa and its attenuation coefficients (m) using portable Time-Domain Optical Coherence Tomography (TD-OCT), which is a non-invasive imaging technique that uses low-coherence interferometry to generate axial scans of plants’ leaves by measuring the time delay and intensity of backscattered light. The portable TD-OCT system in this study has an axial and lateral resolution of 7 m and 3 m, respectively, a scanning depth of 12 mm, and a 1310 nm Super Luminescent Diode (SLD). Several studies suggest that the differences in d and m are related to nutritional, physiological, and anatomical status. The study used the Kratky method, a simple non-circulating hydroponic system, to cultivate Brassica rapa with varying NC (25%, 50%, 75%, 100% (control), and 125%). Each treatment group used two plants. The TD-OCT sample probe was placed on a fixed holder and was oriented vertically so that light was directed downward onto the leaf’s surface to obtain the depth profile (A-scan). The distance between the probe and the leaf was adjusted to obtain the optimum interference signal. Five averaged A-scans were obtained per leaf on the 7th, 18th, and 21st days post nutrient exposure. The logarithm of the averaged A-scan is linearly fitted to extract m. The results showed a positive correlation between NC and m, which suggests that plants produce more chlorophyll and develop denser cells and increase m. There was no correlation obtained between NC and d. The study demonstrates the potential of TD-OCT as a non-destructive tool for assessing plant health and monitoring growth dynamics in hydroponic systems and m as a sensitive indicator of plant health as compared to d. The continued exploration of TD-OCT applications in agriculture can contribute to improving crop management strategies and promoting sustainable food production practices.