Detecting sorghum aphid infestation in grain sorghum using leaf spectral response.

Sorghum aphid, Melanaphis sorghi (Theobald) have become a major economic pest in sorghum causing 70% yield loss without timely insecticide applications. The overarching goal is to develop a monitoring system for sorghum aphids using remote sensing technologies to detect changes in plant-aphid density interactions, thereby reducing scouting time. We studied the effect of aphid density on sorghum spectral responses near the feeding site and on distal leaves from infestation and quantified potential systemic effects to determine if aphid feeding can be detected. A leaf spectrometer at 400-1000 nm range was used to measure reflectance changes by varying levels of sorghum aphid density on lower leaves and those distant to the caged infestation. Our study results demonstrate that sorghum aphid infestation can be determined by changes in reflected light, especially between the green-red range (550-650 nm), and sorghum plants respond systemically. This study serves as an essential first step in developing more effective pest monitoring systems for sorghum aphids, as leaf reflection sensors can be used to identify aphid feeding regardless of infestation location on the plant. Future research should address whether such reflectance signatures can be detected autonomously using small unmanned aircraft systems or sUAS equipped with comparable sensor technologies.

A New Dataset and Comparative Study for Aphid Cluster Detection and Segmentation in Sorghum Fields.

Aphid infestations are one of the primary causes of extensive damage to wheat and sorghum fields and are one of the most common vectors for plant viruses, resulting in significant agricultural yield losses. To address this problem, farmers often employ the inefficient use of harmful chemical pesticides that have negative health and environmental impacts. As a result, a large amount of pesticide is wasted on areas without significant pest infestation. This brings to attention the urgent need for an intelligent autonomous system that can locate and spray sufficiently large infestations selectively within the complex crop canopies. We have developed a large multi-scale dataset for aphid cluster detection and segmentation, collected from actual sorghum fields and meticulously annotated to include clusters of aphids. Our dataset comprises a total of 54,742 image patches, showcasing a variety of viewpoints, diverse lighting conditions, and multiple scales, highlighting its effectiveness for real-world applications. In this study, we trained and evaluated four real-time semantic segmentation models and three object detection models specifically for aphid cluster segmentation and detection. Considering the balance between accuracy and efficiency, Fast-SCNN delivered the most effective segmentation results, achieving 80.46% mean precision, 81.21% mean recall, and 91.66 frames per second (FPS). For object detection, RT-DETR exhibited the best overall performance with a 61.63% mean average precision (mAP), 92.6% mean recall, and 72.55 on an NVIDIA V100 GPU. Our experiments further indicate that aphid cluster segmentation is more suitable for assessing aphid infestations than using detection models.

Aphid cluster recognition and detection in the wild using deep learning models.

Aphid infestation poses a significant threat to crop production, rural communities, and global food security. While chemical pest control is crucial for maximizing yields, applying chemicals across entire fields is both environmentally unsustainable and costly. Hence, precise localization and management of aphids are essential for targeted pesticide application. The paper primarily focuses on using deep learning models for detecting aphid clusters. We propose a novel approach for estimating infection levels by detecting aphid clusters. To facilitate this research, we have captured a large-scale dataset from sorghum fields, manually selected 5447 images containing aphids, and annotated each individual aphid cluster within these images. To facilitate the use of machine learning models, we further process the images by cropping them into patches, resulting in a labeled dataset comprising 151,380 image patches. Then, we implemented and compared the performance of four state-of-the-art object detection models (VFNet, GFLV2, PAA, and ATSS) on the aphid dataset. Extensive experimental results show that all models yield stable similar performance in terms of average precision and recall. We then propose to merge close neighboring clusters and remove tiny clusters caused by cropping, and the performance is further boosted by around 17%. The study demonstrates the feasibility of automatically detecting and managing insects using machine learning models. The labeled dataset will be made openly available to the research community.

Assessment of variations in air quality in cities of Ecuador in relation to the lockdown due to the COVID-19 pandemic.

This study analyzes the effect of lockdown due to COVID-19 on the spatiotemporal variability of ozone (O3), sulfur dioxide (SO2), and nitrogen dioxide (NO2) concentrations in different provinces of continental Ecuador using satellite information from Sentinel - 5P. The statistical analysis includes data from 2018 to March 2021 and was performed based on three periods defined a priori: before, during, and after lockdown due to COVID-19, focusing on the provinces with the highest concentrations of the studied gases (hotspots). The results showed a significant decrease in NO2 concentrations during the COVID-19 lockdown period in all the study areas: the Metropolitan District of Quito (DMQ) and the provinces of Guayas and Santo Domingo de los Tsáchilas. In the period after lockdown, NO2 concentrations increased by over 20% when compared to the pre-lockdown period, which may be attributable to a shift towards private transportation due to health concerns. On the other hand, SO2 concentrations during the lockdown period showed irregular, non-significant variations; however, increases were observed in the provinces of Chimborazo, Guayas, Santa Elena, and Morona Santiago, which could be partly attributed to the eruptive activity of the Sangay volcano during 2019-2020. Conversely, O3 concentrations increased by 2-3% in the study areas; this anomalous behavior could be attributed to decreased levels of NOx, which react with ozone, reducing its concentration. Finally, satellite data validation using the corresponding data from monitoring stations in the DMQ showed correlation values of 0.9 for O3 data and 0.7 for NO2 data, while no significant correlation was found for SO2.

Detecting common coccinellids found in sorghum using deep learning models.

Increased global production of sorghum has the potential to meet many of the demands of a growing human population. Developing automation technologies for field scouting is crucial for long-term and low-cost production. Since 2013, sugarcane aphid (SCA) Melanaphis sacchari (Zehntner) has become an important economic pest causing significant yield loss across the sorghum production region in the United States. Adequate management of SCA depends on costly field scouting to determine pest presence and economic threshold levels to spray insecticides. However, with the impact of insecticides on natural enemies, there is an urgent need to develop automated-detection technologies for their conservation. Natural enemies play a crucial role in the management of SCA populations. These insects, primary coccinellids, prey on SCA and help to reduce unnecessary insecticide applications. Although these insects help regulate SCA populations, the detection and classification of these insects is time-consuming and inefficient in lower value crops like sorghum during field scouting. Advanced deep learning software provides a means to perform laborious automatic agricultural tasks, including detection and classification of insects. However, deep learning models for coccinellids in sorghum have not been developed. Therefore, our objective was to develop and train machine learning models to detect coccinellids commonly found in sorghum and classify them according to their genera, species, and subfamily level. We trained a two-stage object detection model, specifically, Faster Region-based Convolutional Neural Network (Faster R-CNN) with the Feature Pyramid Network (FPN) and also one-stage detection models in the YOLO (You Only Look Once) family (YOLOv5 and YOLOv7) to detect and classify seven coccinellids commonly found in sorghum (i.e., Coccinella septempunctata, Coleomegilla maculata, Cycloneda sanguinea, Harmonia axyridis, Hippodamia convergens, Olla v-nigrum, Scymninae). We used images extracted from the iNaturalist project to perform training and evaluation of the Faster R-CNN-FPN and YOLOv5 and YOLOv7 models. iNaturalist is an imagery web server used to publish citizen's observations of images pertaining to living organisms. Experimental evaluation using standard object detection metrics, such as average precision (AP), AP@0.50, etc., has shown that the YOLOv7 model performs the best on the coccinellid images with an AP@0.50 as high as 97.3, and AP as high as 74.6. Our research contributes automated deep learning software to the area of integrated pest management, making it easier to detect natural enemies in sorghum.

Sensibilidad y Especificidad de la Citología Cervicouterina y la prueba PCRhrHPV con el diagnóstico Histopatológico, en el Hospital "Solon Espinosa Ayala", Solca-Quito / Sensitivity and Specificity of cervical cytological diagnoses, PCR-hrHPV test and histopathological diagnoses, at the "Solón Espinosa Ayala" Hospital, Solca Quito

Introducción: La implementación de las pruebas moleculares para la detección de la infección por hrHPV ha generado cambios en las directrices del tamizaje en la detección oportuna del carcinoma cervicouterino. El objetivo del estudio es presentar la sensibilidad y especificidad de los estudios citológicos y las pruebas moleculares con los estudios histológicos. Métodos: Se realizó un estudio transversal, retrospectivo en el hospital de Solca-Quito de enero a diciembre 2014. Se recolectaron los casos con los diagnósticos citológicos cervicouterinos, los resultados de la prueba de PCR tiempo real de hrHPV (Hibribio®) y los diagnósticos histopatológicos en las pacientes a las que se realizó biopsia. El análisis realizado fue "de prueba diagnóstica" para medir la sensibilidad y especificidad de las pruebas. Resultados: 730 estudios moleculares de hrHPV conjuntamente con estudios citológicos fueron realizados. Los casos positivos para hrHPV fueron 301/730 casos (41.2 %). La mayoría de casos hrHPV positivos corresponde a los genotipos 16/18 (59.5 %) y se encuentra en los rangos de edad entre 30 y 49 años (58.8 %). En 168 casos se realizó además estudio histopatológico, en los que se determinó la sensibilidad (S) de la citología Vs Histología la cual fue de 76 %, la especificidad (E) fue de 48 %, con un valor predictivo positivo (VPP) de 90 %. La S de HrHPV vs Histología fue de 74%, E 39 %, VPP 89 %; la S de Citología + HrHVP vs Histología fue de 91 %, E 40 %, VPP 90 %. Conclusión: La mayor sensibilidad para el diagnóstico de cáncer cervicouterino la realización de la Citología y la presencia de HrHVP. La mayor especificidad se consiguió con el estudio de Citología.

Introduction: The implementation of molecular tests for the detection of hrHPV infection has generated changes in the screening guidelines in the timely detection of cervical carcinoma. The aim of the study is to present the sensitivity and specificity of cytological studies and molecular tests with histological studies. Methods: A cross-sectional, retrospective study was carried out in the Solca-Quito hospital from January to December 2014. Cases were collected with cervical cytological diagnoses, the results of the real-time PCR test of hrHPV (Hibribio®) and the diagnoses Histopathological findings in patients who underwent a biopsy. The analysis performed was "diagnostic test" to measure the sensitivity and specificity of the tests. Results: 730 molecular studies of hrHPV in conjunction with cytological studies were performed. The positive cases for hrHPV were 301/730 cases (41.2 %). The majority of hrHPV positive cases correspond to genotypes 16/18 (59.5 %) and are in the age ranges between 30 and 49 years (58.8 %). In 168 cases, a histopathological study was also carried out, in which the sensitivity (S) of the cytology Vs Histology was determined, which was 76 %, the specificity (E) was 48 %, with a positive predictive value (PPV) of 90 % The S of HrHPV vs Histology was 74%, E 39%, PPV 89 %; S for Cytology + HrHVP vs Histology was 91 %, E 40 %, PPV 90 %. Conclusion: The highest sensitivity for the diagnosis of cervical cancer is the completion of Cytology and the presence of HrHVP. The highest specificity was obtained with the Cytology study.

Lesiones precursoras del cáncer de cervix: etiología y nomenclatura

En las últimas décadas la incidencia del cáncer infiltrante del cuello uterino, aligual que la mortalidad producida por este tumor, han disminuído significativamente en los países desarrollados, gracias a la detección de la enfermedad en estadios tempranos y al diagnostico y tratamiento de sus lesiones precursoras. El estudio del papanicolaou u citología cervical, realizado a las mujeres en forma períodica, ha sido el factor más importante para este cambio. (1-2). En nuestro país y en general en los países en "vías de desarrollo" el cáncer cervical infiltrante sigue siendo el más frecuente en la mujer y causa una alta mortalidad por su detección en estadíos tardíos. (3).