Rice is cataloged as one of the most widely cultivated crops globally, providing food for a large proportion of the global population. Integrating Geographic Information Systems (GISs), such as unmanned aerial vehicles (UAVs), into agricultural practices offers numerous benefits. UAVs, equipped with imaging sensors and geolocation technology, enable precise crop monitoring and management, enhancing yield and efficiency. However, Peru lacks sufficient experience with the application of these technologies, making them somewhat unfamiliar in the context of modern agriculture. In this study, we conducted experiments involving four distinct rice varieties (n = 24) at various stages of growth to predict yield using vegetation indices (VIs). A total of nine VIs (NDVI, GNDVI, ReCL, CIgreen, MCARI, SAVI, CVI, LCI, and EVI) were assessed across four dates: 88, 103, 116, and 130 days after sowing (DAS). Pearson correlation analysis, principal component analysis (PCA), and multiple linear regression were used to build prediction models. The results showed a general prediction model (including all the varieties) with the best performance at 130 days after sowing (DAS) using NDVI, EVI, and SAVI, with a coefficient of determination (adjusted-R² = 0.43). The prediction models by variety showed the best performance for Esperanza at 88 DAS (adjusted-R² = 0.94) using EVI as the vegetation index. The other varieties showed their best performance using different indices at different times: Capirona (LCI and CIgreen, 130 DAS, adjusted-R² = 0.62); Conquista Certificada (MCARI, 116 DAS, R² = 0.52); and Conquista Registrada (CVI and LCI, 116 DAS, adjusted-R² = 0.79). These results provide critical information for optimizing rice crop management and support the use of unmanned aerial vehicles (UAVs) to inform timely decision making and mitigate yield losses in Peruvian agriculture.

Precision agriculture aims to improve crop management using advanced analytical tools. In this context, the objective of this study is to develop an innovative predictive model to estimate the yield and morphological quality, such as the circularity and length–width ratio of potato tubers, based on phenotypic characteristics of plants and data captured through spectral cameras equipped on UAVs. For this purpose, the experiment was carried out at the Santa Ana Experimental Station in the central Peruvian Andes, where advanced potato clones were planted in December 2023 under three levels of fertilization. Random Forest, XGBoost, and Support Vector Machine models were used to predict yield and quality parameters, such as circularity and the length–width ratio. The results showed that Random Forest and XGBoost achieved high accuracy in yield prediction (R² > 0.74). In contrast, the prediction of morphological quality was less accurate, with Random Forest standing out as the most reliable model (R² = 0.55 for circularity). Spectral data significantly improved the predictive capacity compared to agronomic data alone. We conclude that integrating spectral indices and multitemporal data into predictive models improved the accuracy in estimating yield and certain morphological traits, offering key opportunities to optimize agricultural management.

Soils are highly sensitive to the type of tillage practices used, as these practices influence soil properties and affect crops, the environment, and society. However, research on cassava production under different tillage systems during drought conditions in the Peruvian Amazon has not been reported. The objective of this study was to compare soil properties, cassava physiology, and yield under conservation agriculture (CA) and traditional agriculture (TA) practices, with and without mulch, in a water-scarce environment. Soil moisture, earthworm population (Ew), stomatal conductance, leaf area index, and commercial yield under CA were 5.26% (~105.2 m³ ha⁻¹), 83%, 1.2 times, 1.14 times, and 7.3 t ha⁻¹, respectively, higher than under TA. Hydraulic conductivity (Ks) in TA was 2.1 times higher than that in CA. However, Ks, bulk density, and Ew over time showed a gradual recovery under CA. The mulch factor only affected Ew, which was higher without mulch than with mulch. The results indicate that CA practices were superior to TA practices, improving soil properties, cassava physiology, and yield, and, therefore, offer significant benefits in resource conservation and higher production and profitability in a drought-prone environment.