Examinando por Autor "Loayza, Hildo"

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Development of an open-source thermal image processing software for improving irrigation management in potato crops (Solanum tuberosum L.)
(MDPI, 2020-01-14) Cucho Padin, Gonzalo; Rinza, Javier; Ninanya, Johan; Loayza, Hildo; Quiroz, Roberto; Ramirez, David A.
Accurate determination of plant water status is mandatory to optimize irrigation scheduling and thus maximize yield. Infrared thermography (IRT) can be used as a proxy for detecting stomatal closure as a measure of plant water stress. In this study, an open-source software (Thermal Image Processor (TIPCIP)) that includes image processing techniques such as thermal-visible image segmentation and morphological operations was developed to estimate the crop water stress index (CWSI) in potato crops. Results were compared to the CWSI derived from thermocouples where a high correlation was found (𝑟𝑃𝑒𝑎𝑟𝑠𝑜𝑛 = 0.84). To evaluate the effectiveness of the software, two experiments were implemented. TIPCIP-based canopy temperature was used to estimate CWSI throughout the growing season, in a humid environment. Two treatments with different irrigation timings were established based on CWSI thresholds: 0.4 (T2) and 0.7 (T3), and compared against a control (T1, irrigated when soil moisture achieved 70% of field capacity). As a result, T2 showed no significant reduction in fresh tuber yield (34.5 ± 3.72 and 44.3 ± 2.66 t ha−1), allowing a total water saving of 341.6 ± 63.65 and 515.7 ± 37.73 m3 ha−1 in the first and second experiment, respectively. The findings have encouraged the initiation of experiments to automate the use of the CWSI for precision irrigation using either UAVs in large settings or by adapting TIPCIP to process data from smartphone-based IRT sensors for applications in smallholder settings.
Implementing cloud computing for the digital mapping of agricultural soil properties from high resolution UAV multispectral imagery
(MDPI, 2023-06-20) Pizarro Carcausto, Samuel Edwin; Pricope, Narcisa G.; Figueroa Venegas, Deyanira Antonella; Carbajal Llosa, Carlos Miguel; Quispe Huincho, Miriam Rocío; Vera Vilchez, Jesús Emilio; Alejandro Méndez, Lidiana Rene; Achallma Mendoza, Lino; González Tovar, Izamar Estrella; Salazar Coronel, Wilian; Loayza, Hildo; Cruz Luis, Juancarlos Alejandro; Arbizu Berrocal, Carlos Irvin
The spatial heterogeneity of soil properties has a significant impact on crop growth, making it difficult to adopt site-specific crop management practices. Traditional laboratory-based analyses are costly, and data extrapolation for mapping soil properties using high-resolution imagery becomes a computationally expensive procedure, taking days or weeks to obtain accurate results using a desktop workstation. To overcome these challenges, cloud-based solutions such as Google Earth Engine (GEE) have been used to analyze complex data with machine learning algorithms. In this study, we explored the feasibility of designing and implementing a digital soil mapping approach in the GEE platform using high-resolution reflectance imagery derived from a thermal infrared and multispectral camera Altum (MicaSense, Seattle, WA, USA). We compared a suite of multispectral-derived soil and vegetation indices with in situ measurements of physical-chemical soil properties in agricultural lands in the Peruvian Mantaro Valley. The prediction ability of several machine learning algorithms (CART, XGBoost, and Random Forest) was evaluated using R2, to select the best predicted maps (R2 > 0.80), for ten soil properties, including Lime, Clay, Sand, N, P, K, OM, Al, EC, and pH, using multispectral imagery and derived products such as spectral indices and a digital surface model (DSM). Our results indicate that the predictions based on spectral indices, most notably, SRI, GNDWI, NDWI, and ExG, in combination with CART and RF algorithms are superior to those based on individual spectral bands. Additionally, the DSM improves the model prediction accuracy, especially for K and Al. We demonstrate that high-resolution multispectral imagery processed in the GEE platform has the potential to develop soil properties prediction models essential in establishing adaptive soil monitoring programs for agricultural regions.
Unraveling ecophysiological mechanisms in potatoes under different irrigation methods: a preliminary field evaluation
(MDPI, 2020-06-11) Silva Díaz, Cecilia; Ramírez, David A.; Rodríguez Delfín, Alfredo; De Mendiburu, Felipe; Rinza, Javier; Ninanya, Johan; Loayza, Hildo; Quiroz, Roberto
Potatoes—a global food security and staple crop—is threatened by dry spells in drought-prone areas. The use of physiological thresholds to save water while maintaining a reasonable tuber yield has been proposed, but their effects on physiological performances and usefulness under different irrigation methods are yet to be evaluated. In this study, photosynthetic traits were monitored to assess the effect of water restriction and rewatering under drip (DI) and furrow (FI) irrigations. The treatments consisted of two maximum light-saturated stomatal conductance (g𝑠_𝑚𝑎𝑥) irrigation thresholds (T2: 0.15 and T3: 0.05 mol H2O m−2 s−1) compared with a fully irrigated control (g𝑠_𝑚𝑎𝑥 > 0.3 mol H2O m−2 s−1). DI used less water than FI but promoted early senescence and low percentage of maximum assimilation rate (PMA) at late developmental stages. FI caused no yield penalization in T2 and higher recovery of carbon isotope discrimination and PMA than DI. It is suggested that moderate water quantities of early and frequently water pulses in the irrigation, promote short-term water stress memory improvement, senescence delay and more capability of recovery at late stages.