Abstract
In nature, and particularly in agriculture, pollination is fundamental for the sustainability of our society. In this context, pollination is a vital process underlying crop yield quality and is responsible for the biodiversity and the standards of the flora. Bees play a crucial role in natural pollination; however, their populations are declining. Robots can help maintain pollination levels while humans work to recover bee populations. Swarm robotics approaches appear promising for robotic pollination. This paper proposes the cooperation between multiple Unmanned Aerial Vehicles (UAVs) and an Unmanned Ground Vehicle (UGV), leveraging the advantages of collaborative work for pollination, referred to as Pollinationbots. Pollinationbots is based in swarm behaviors and methodologies to implement more effective pollination strategies, ensuring efficient pollination across various scenarios. The paper presents the architecture of the Pollinationbots system, which was evaluated using the Webots simulator, focusing on path planning and follower behavior. Preliminary simulation results indicate that this is a viable solution for robotic pollination. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
This paper introduces a deep learning approach for detecting plant leaf diseases. The objective is to develop robust models capable of accurately identifying plant diseases across various image backgrounds, thereby overcoming the limitations of existing methods that often rely on controlled laboratory conditions. To achieve this, a combination of the PlantDoc dataset and Web-sourced data of plant images from online platforms was used. This paper implemented and compared state-of-the-art *cnn architectures, including EfficientNet-B0, EfficientNet-B3, ResNet50, and DenseNet201, all fine-tuned specifically for leaf disease classification. A significant contribution is the application of enhanced data augmentation techniques, such as adding Gaussian noise, to improve model generalisation. Results indicated varied performance across the datasets, with EfficientNet models generally outperforming others. When trained and tested on the PlantDoc dataset, EfficientNet-B3 achieved the highest accuracy of 73.31%. In cross-dataset evaluation, EfficientNet-B3 reached 76.77% accuracy when trained on PlantDoc and tested on the Web-sourced dataset. The best performance occurred when training on the combined dataset and testing on the Web-sourced data, resulting in an accuracy of 80.19%. Class-wise F1-scores revealed consistently high performance (>0.90) for diseases such as apple rust leaf and grape leaf across models. This paper contributes to the comparative analysis of various datasets and model architectures for effective leaf disease detection

Abstract
A novel non-destructive analytical method for early diagnosis of two bacterial diseases, Pseudomonas syringae and Xanthomonas euvesicatoria, in tomato plants, using ultraviolet-visible (UV-Vis) transmittance spectroscopy and chemometric models, is developed. Plant-pathogen interactions caused tissue damage that generated non-linear data patterns compared to the control set (healthy samples), which challenges traditional discrimination models, even when employing non-linear discriminant approaches. Alternatively, an authentication task to conduct oneclass classification relying on a data-driven version of soft independent modeling of class analogy (DD-SIMCA) is a wise choice due to its quadratic approach, proper to deal with non-linear data. DD-SIMCA detached the target class (control healthy plant leaflet tissues) from all other samples (target class and non-target class of plant leaflet tissues inoculated with two bacteria, even before the manifestation of macroscopic lesions associated with the diseases) by capturing the main similarities within the samples of the target class through the full distance that acts as a classification analytical signal, reaching 100 % sensitivity in the training and validation sets. Multivariate curve resolution - alternating least-squares (MCR-ALS) constrained analysis allowed the description of the bacterial inoculation process on diseased tissues through pure spectral signatures. DD-SIMCA results indicate that non-target class of samples with higher proximity to the acceptance boundary suggested that they were at earlier stages of infection when compared to more distant ones, presenting lower full distance values. These findings reveal that a handheld UV-Vis transmittance spectrometer is sufficiently sensitive to be used in acquiring biological data with suitable chemometric models for early disease diagnosis and prompt intervention.

Abstract
RGB-D sensors face multiple challenges operating under open-field environments because of their sensitivity to external perturbations such as radiation or rain. Multiple works are approaching the challenge of perceiving the three-dimensional (3D) position of objects using monocular cameras. However, most of these works focus mainly on deep learning-based solutions, which are complex, data-driven, and difficult to predict. So, we aim to approach the problem of predicting the three-dimensional (3D) objects’ position using a Gaussian viewpoint estimator named best viewpoint estimator (BVE), powered by an extended Kalman filter (EKF). The algorithm proved efficient on the tasks and reached a maximum average Euclidean error of about 32mm. The experiments were deployed and evaluated in MATLAB using artificial Gaussian noise. Future work aims to implement the system in a robotic system. © 2024 by SCITEPRESS-Science and Technology Publications, Lda.

Abstract
Fertilization is paramount for agriculture productivity and food security. Plant nutrition pre-established recipes and nutrient uptake are rarely managed by changing the fertilizer composition at the different stages of the plant life cycle. Herein we perform a literature review analysis - since the year 2000 and onwards - of the state-of-the-art capabilities of Nitrogen, Phosphorous, and Potassium (NPK) sensors for liquid fertilizers ( e.g. , hydroponics). From the initial search hits of 1660 results, only 53 publications had relevant information for this topic; from these, only 9 had NPK quantitative information. Qualitative analysis was performed by determining the number of publications for each nutrient, according to sample complexity and existing single, multiplexed or hybrid technologies. Quantitative assessment was performed by extracting the bias and linearity, the limit of detection and concentration ranges of sensor operation, framed into the context of the sensor technology development stage and sample compositional complexity. The most common technologies are colorimetry, ionselective electrodes, optrodes, chemosensors, and optical spectroscopy. The most abundant technologies are for nitrate quantification, from which ion-selective electrodes are the most widely used technology, and sensors for phosphate quantification are the less developed. Most are at low technological levels of development, not dealing with the complexity of agriculture samples due to matrix effects and interference. Measuring the fertilizer composition, nutrient uptake, the state of the chemical network, and controlling the release of nutrients using new functional materials, is one of the most important challenges ahead for the existence of precision fertilization. Intelligent sensing and smart materials are today the most successful strategy for dealing with matrix effects and interferences, being led by ion-selective electrodes and spectroscopy technologies.

Abstract
Over recent decades, precision agriculture has revolutionized farming by optimizing crop yields and reducing resource use through targeted applications. Existing portable spray quality assessors lack precision, especially in detecting overlapping droplets on water-sensitive paper. This proposal aims to develop a smartphone application that uses the integrated camera to assess spray quality. Two approaches were implemented for segmentation and evaluation of both the water-sensitive paper and the individual droplets: classical computer vision techniques and a pre-trained YOLOv8 deep learning model. Due to the labor-intensive nature of annotating real datasets, a synthetic dataset was created for model training through sim-to-real transfer. Results show YOLOv8 achieves commendable metrics and efficient processing times but struggles with low image resolution and small droplet sizes, scoring an average Intersection over Union of 97.76% for water-sensitive spray segmentation and 60.77% for droplet segmentation. Classical computer vision techniques demonstrate high precision but lower recall with a precision of 36.64% for water-sensitive paper and 90.85% for droplets. This study highlights the potential of advanced computer vision and deep learning in enhancing spray quality assessors, emphasizing the need for ongoing refinement to improve precision agriculture tools. © 2024 by SCITEPRESS-Science and Technology Publications, Lda.