18 research outputs found
A novel dataset and deep learning object detection benchmark for grapevine pest surveillance
No full textFlavescence dorée (FD) poses a significant threat to grapevine health, with the American grapevine leafhopper, Scaphoideus titanus, serving as the primary vector. FD is responsible for yield losses and high production costs due to mandatory insecticide treatments, infected plant uprooting, and replanting. Another potential FD vector is the mosaic leafhopper, Orientus ishidae, commonly found in agroecosystems. The current monitoring approach, which involves periodic human identification of yellow sticky traps, is labor-intensive and time-consuming. Therefore, there is a compelling need to develop an automatic pest detection system leveraging recent advances in computer vision and deep learning techniques. However, progress in developing such a system has been hindered by the lack of effective datasets for training. To fill this gap, our study contributes a fully annotated dataset of S. titanus and O. ishidae from yellow sticky traps, which includes more than 600 images, with approximately 1500 identifications per class. Assisted by entomologists, we performed the annotation process, trained, and compared the performance of two state-of-the-art object detection algorithms: YOLOv8 and Faster R-CNN. Pre-processing, including automatic cropping to eliminate irrelevant background information and image enhancements to improve the overall quality of the dataset, was employed. Additionally, we tested the impact of altering image resolution and data augmentation, while also addressing potential issues related to class detection. The results, evaluated through 10-fold cross validation, revealed promising detection accuracy, with YOLOv8 achieving an [email protected] of 92%, and an F1-score above 90%, with an mAP@[0.5:0.95] of 66%. Meanwhile, Faster R-CNN reached an [email protected] and mAP@[0.5:0.95] of 86% and 55%, respectively. This outcome offers encouraging prospects for developing more effective management strategies in the fight against Flavescence dorée
DigiAgriApp
No full textDigiAgriApp è una soluzione software gratuita e open source rivolta agli agricoltori e a chiunque abbia terreni coltivati. DigiAgriApp è in grado di salvare le operazioni svolte in campo e tutti i dati provenienti da diversi dispositivi (telefoni cellulari, stazioni meteo, telecomandi come droni o satelliti) in modo capillare, dal campo alla singola pianta. DigiAgriApp è composta da un server API RESTful (sviluppato in Django), come interfaccia al database, e da un client multipiattaforma (sviluppato in Flutter) in grado di essere eseguito nella maggior parte dei sistemi operativi. DigiAgriApp è inoltre dotato di un pannello di amministrazione web che può essere utilizzato da utenti selezionati della vostra organizzazione per avere un controllo completo sui dati del vostro database.DigiAgriApp is a Free and Open Source Software solution aimed at farmers and anyone with cultivated land. DigiAgriApp is able to save the operations carried out in the field and all the data from different devices (mobile phones, weather stations, remotes such as drones or satellites) in a capillary manner, from the field to the individual plant. DigiAgriApp is composed by a RESTful API server (developed in Django), as interface to the database, and a multi-platform client (developed in Flutter) able to be run in most of the operitive systems. DigiAgriApp also comes with a web administration panel that can be used by selected users in your organisation to have complete control over the data in your database
A novel dataset and deep learning object detection benchmark for grapevine pest surveillance
No full textInherited thrombophilia and in vitro fertilization implantation failure. Fertil Steril. 2001, 76(1):201-2.
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Tessuti digitali a Matera. Un contributo per una progettualità non invasiva ma pervasiva / Digital fabrics in Matera. A contribution to a non-invasive pervasive design.
No full textThe contribution describes an artistic installation at Palazzo Viceconte in Matera intertwinig digital data collected from the digital world (three different tools: NUVI, Talkwalker, Human Ecosystems were utilized) and their aesthetic representations
2-class Grapevine Pest Dataset of Scaphoideus titanus and Orientus ishidae on yellow Sticky traps for Insect Detection
No full textFruitlet image dataset for apple phenotyping during early development
No full textThis dataset originates from an experimental study conducted between April 24 and May 29, 2024, aimed at providing agronomists with an automated vision tool to accelerate data collection of apple fruitlets during early development. Excluding corymbs that experienced total fruit abscission, the data acquisition process resulted in 234 video files in .bag format and a total of 1,054 fruitlet measurements. The dataset includes three primary data sources: bag_videos.zip: a collection of videos recorded using the Intel® RealSenseTM Depth Camera D435i. Each video captures target fruitlets from multiple orientations, with an average duration of 10 seconds, at a distance of approximately 30 cm, and a resolution of 640 × 480 pixels; ground_truth_caliper_measurements.csv: the corresponding ground-truth measurements of selected corymbs, collected across 7 monitoring sessions. Measurements were categorized by date and bud type to analyze growth differences over time. Metadata such as the orientation of the vegetative wall and the presence of the king fruit was also recorded; FruitletDetectionDataset.zip: a dataset for model training, validation, and testing, comprising 481 images and corresponding oriented bounding box annotations. The images were obtained through stratified random sampling after RGB frame extraction, ensuring balanced representation across videos. The dataset is split into training (60%), validation (20%), and test (20%) subset
Apple phenotyping using deep learning and 3D depth analysis: An experimental study on fruitlet sizing during early development
No full textCurrent research in apple-growing focuses on collecting extensive biometric data to better understand physiological processes, improve orchard productivity and predict yields. In this context, fruit thinning has emerged as a key horticultural practice to enhance fruit size and quality while preventing alternate bearing. Despite the growing role of plant imaging technologies in agronomic management, fruitlet sizing remains challenging, particularly in early phenological stages. To address this challenge, we developed an RGB-D-based vision pipeline that combines YOLO models with depth information and relies on the statistical analysis of frame series to detect and cluster fruitlets into flower corymbs, providing both fruitlet counting and diameter estimates for each video acquisition. After obtaining an [email protected] and AP@[0.5:0.95] of respectively 0.894 and 0.77 in fruitlet detection, along with a precision of 0.881 and a recall of 0.846, our approach efficiently processed video frames, extracting the most reliable data for each labeled cluster. While the comparison of true positive estimates with calibrated caliper measurements showed a mean RMSE of 1.05 mm, challenges remain in achieving the correct fruitlet count, with a mean counting error of 0.63 fruitlets per video. Additionally, the proposed workflow retrieved the exact number of fruitlets as the ground truth in 56.4% of the videos, increasing to 75% when excluding those videos where the correct fruitlet count was never detected in any frame by the YOLO model. Despite these limitations, our results are promising, proposing a potential data acquisition tool without compromising the reliability of traditional practices. This approach could pave the way for future applications, including the evaluation of plant growth regulator trials and the development of predictive models for yield and productivity optimization
