The present work proposes an image processing algorithm oriented to identify the coloration of the quinoa grains that make up the different samples obtained from the production of a crop field. The objective is to perform quality control of production based on the statistics of grain coloration, which is currently done manually based on subjective visual perception. This generates results that totally depend on the abilities and the particular criteria of each observer, generating considerable errors in the identification of the colors and tonalities. The problem is further complicated by the nonexistence, at present, of a pattern or standard of coloration of quinoa grains that specifically defines a referential color map. In this sense, through this work, an algorithm is proposed oriented to classify the grains of the acquired samples by their color via digital images and provide corresponding statistics for the quality control of the production. The algorithm uses the color models RGB, HSV and YCbCr, thresholding, segmentation by binary masks, erosion, connectivity, labeling and sequential classification based on 8 colors established by agronomists. The obtained results showed a performance of the proposed algorithm of 91.25% in relation to the average success rate.

This study provides a comprehensive mitochondrial genome sequence and a phylogenetic analysis of Jakumaru (Bos taurus), a prominent bull from a Peruvian breeding program. The mitochondrial genome, spanning 16,340bp, comprises 13 protein-coding genes, 22 tRNA genes, and 2 rRNA genes, with a GC content of 40%. The gene arrangement and structural organization are highly conserved, closely resembling those of related Bovine species, indicating a stable genomic architecture essential for mitochondrial function. Phylogenetic analysis, incorporating 35 complete bovine mitochondrial genomes, positions Jakumaru within Subclade I, revealing a close relationship with European Simmental cattle. The study emphasizes the biological diversity and the evolution of the Simmental breed, influenced by natural selection, breeding practices, and genetic drift. These findings enhance our understanding of the breed's genetic variation and its evolution, particularly in the context of its importance in Peru

La quinua (Chenopodium quinoa Willd) es consumida a nivel mundial debido a su composición nutricional. Es importante conocer las características agronómicas que se ven influenciadas por las condiciones edafoclimáticas y evaluar el comportamiento agronómico de 81 genotipos de quinua del Instituto Nacional de Innovación Agraria (INIA) sembradas en las localidades de Cusco, Puno, Ayacucho y Junín, bajo un diseño experimental de bloques completos al azar, con tres repeticiones. Se evaluaron las variables: altura de planta, diámetro de panoja, longitud de panoja, rendimiento, severidad de infección de mildiu (Peronospora farinosa) e índice de selección (IS), en siembras del 2017 y 2018. La comparación de medias se realizó mediante la prueba de Tukey, análisis de conglomerados, componentes principales, correlación de Pearson y la evaluación del índice de selección para identificar la adaptación de los genotipos. Los resultados mostraron que la siembra del 2018 tuvo los mayores rendimientos. El análisis de conglomerados encontró la formación de tres grupos, donde el grupo tres mostró las mejores características en rendimiento, altura de planta, diámetro y longitud de panoja. El análisis de componentes principales mostró correlaciones positivas entre variables altura de planta, diámetro y longitud de panoja. Más del 45% de los tratamientos mostraron un índice de selección mayor a uno y se identificaron 16 genotipos con nivel bajo de severidad de infección a mildiu. Las localidades de Cusco y Puno reportaron el mejor comportamiento agronómico para los 81 genotipos.

Vicia sativa is one of the most important crops for livestock feed. Pods from Vicia sativa seed production are usually discarded. The objective of this study was to report the nutritional value of Vicia sativa pods. Report of the bromatological analysis was 17.58% for crude protein, 44.96% for neutral detergent fiber, and 55.81% for in vitro dry matter digestibility. Estimates of the energy value on dry matter basis (Mcal kg–1) were made for digestible and metabolizable energy, with values of 2.46 and 2.01, respectively. Vicia sativa pods promise enormous potential as a protein supplement on livestock diets when other sources are not available.

Pitahaya (Hylocereus spp.) is an economically significant cactus fruit in Peru, renowned for its rich nutritional profile and antioxidant properties while exhibiting wide biological diversity. This study aimed to morphologically characterize seven pitahaya accessions using qualitative and quantitative descriptors related to the cladodes, flowers, and fruits. Univariate and multivariate (FAMD, PCA, MCA, and clustering) analyses were employed to identify and classify the accessions based on their morphological traits. The analyses revealed three distinct groups: one consisting solely of AC.07; another with AC.02, AC.04, and AC.06; and a third including AC.01, AC.03, and AC.05. The first group exhibited superior characteristics, particularly in fruit traits such as the stigma lobe count (23.3), number of bracts (26.5 mm), and length of apical bracts (15.75 mm). The second group recorded the highest spine count (3.21), bract length (16.95 mm), and awn thickness (5.12 mm). The third group had the highest bract count (37) and an average locule number (23.65). These findings highlight the significant morphological diversity among the accessions, indicating the potential for classification and selection in pitahaya cultivation. The potential of AC.07 stands out in terms of its agronomic qualities, such as its fruit weight (451.93 g) and pulp weight (292.5 g), surpassing the other accessions.

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 índices and multitemporal data into predictive models improved the accuracy in estimating yield and certain morphological traits, offering key opportunities to optimize agricultural management.