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Author: search.filters.author.Lesidan, James Roy

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    Transmittance Properties of Healthy and Infected Coffee Robusta Leaves with Coffee Leaf Miner (CLM) Pests
    bulan, Jejomar; Cadondon, Jumar; Lesidan, James Roy; Vallar, Edgar; Galvez, Maria Cecilia (Multidisciplinary Digital Publishing Institute (MDPI), 2023)
    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. © 2023 by the author. Licensee MDPI, Basel, Switzerland.
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    Development of Crop Reflectance Sensor for Precision Agriculture †
    Bulan, Jejomar; Cadondon, Jumar; Lesidan, James Roy; Galvez, Maria Cecilia; Vallar, Edgar; Shiina, Tatsuo (Multidisciplinary Digital Publishing Institute (MDPI), 2024)
    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. © 2024 by the authors.
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    Algal organic matter fluorescence analysis of Chlorella sp. for biomass estimation
    Cadondon, Jumar; Lesidan, James Roy; Bulan, Jejomar; Vallar, Edgar; Shiina, Tatsuo; Galvez, Maria Cecilia (Multidisciplinary Digital Publishing Institute (MDPI), 2023-11-15)
    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.