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Early detection of weeds is crucial to manage weeds effectively, support decision-making and prevent potential crop losses. This research presents an innovative approach to develop a specialized cognitive system for classifying and detecting early-stage weeds at the species level. The primary objective was to create an automated multiclass discrimination system using cognitive computing, regardless of the weed growth stage. Initially, the model was trained and tested on a dataset of 31,002 UAV images, including ten weed species manually identified by experts at the early phenological stages of maize (BBCH14) and tomato (BBCH501). The images were captured at 11 m above ground level. This resulted in a classification accuracy exceeding 99.1% using the vision transformer Swin-T model. Subsequently, generative modeling was employed for data augmentation, resulting in new classification models based on the Swin-T architecture. These models were evaluated on an unbalanced dataset of 36,556 UAV images captured at later phenological stages (maize BBCH17 and tomato BBCH509), achieving a weighted average F1-score ranging from 94.8% to 95.3%. This performance highlights the system's adaptability to morphological variations and its robustness in diverse crop scenarios, suggesting that the system can be effectively implemented in real agricultural scenarios, significantly reducing the time and resources required for weed identification. The proposed data augmentation technique also proved to be effective in implementing the detection transformer architecture, significantly improving the generalization capability and enabling accurate detection of weeds at different growth stages. The research represents a significant advancement in weed monitoring across phenological stages, with potential applications in precision agriculture and sustainable crop management. Furthermore, the methodology showcases the versatility of the latest generation models for application in other knowledge domains, facilitating time-efficient model development. Future research could investigate the applicability of the model in different geographical regions and with different types of crops, as well as real-time implementation for continuous field monitoring. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Ciências Agrárias is the property of Sociedade de Ciencias Agrarias de Portugal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista de Ciências Agrárias is the property of Sociedade de Ciencias Agrarias de Portugal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista de Ciências Agrárias is the property of Sociedade de Ciencias Agrarias de Portugal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article describes the technical specifications and configuration of a multirotor unmanned aerial vehicle (UAV) to acquire remote images using a six-band multispectral sensor. Several flight missions were programmed as follows: three flight altitudes (60, 80 and 100 m), two flight modes (stop and cruising modes) and two ground control point (GCP) settings were considered to analyze the influence of these parameters on the spatial resolution and spectral discrimination of multispectral orthomosaicked images obtained using Pix4Dmapper. Moreover, it is also necessary to consider the area to be covered or the flight duration according to any flight mission programmed. The effect of the combination of all these parameters on the spatial resolution and spectral discrimination of the orthomosaicks is presented. Spectral discrimination has been evaluated for a specific agronomical purpose: to use the UAV remote images for the detection of bare soil and vegetation (crop and weeds) for in-season site-specific weed management. These results show that a balance between spatial resolution and spectral discrimination is needed to optimize the mission planning and image processing to achieve every agronomic objective. In this way, users do not have to sacrifice flying at low altitudes to cover the whole area of interest completely. [ABSTRACT FROM AUTHOR]

Precision viticulture has arisen in recent years as a new approach in grape production. It is based on assessing field spatial variability and implementing site-specific management strategies, which can require georeferenced information of the three dimensional (3D) grapevine canopy structure as one of the input data. The 3D structure of vineyard fields can be generated applying photogrammetric techniques to aerial images collected with Unmanned Aerial Vehicles (UAVs), although processing the large amount of crop data embedded in 3D models is currently a bottleneck of this technology. To solve this limitation, a novel and robust object-based image analysis (OBIA) procedure based on Digital Surface Model (DSM) was developed for 3D grapevine characterization. The significance of this work relies on the developed OBIA algorithm which is fully automatic and self-adaptive to different crop-field conditions, classifying grapevines, and row gap (missing vine plants), and computing vine dimensions without any user intervention. The results obtained in three testing fields on two different dates showed high accuracy in the classification of grapevine area and row gaps, as well as minor errors in the estimates of grapevine height. In addition, this algorithm computed the position, projected area, and volume of every grapevine in the field, which increases the potential of this UAV- and OBIA-based technology as a tool for site-specific crop management applications. [ABSTRACT FROM AUTHOR]