Artificial Intelligence (AI) has changed how processes are developed, and decisions are made in the agricultural area replacing manual and repetitive processes with automated and more efficient ones. This study presents the application of deep learning techniques to detect and segment weeds in agricultural crops by applying models with different architectures in the analysis of images captured by an Unmanned Aerial Vehicle (UAV). This study contributes to the computer vision field by comparing the performance of the You Only Look Once (YOLOv8n, YOLOv8s, YOLOv8m, and YOLOv8l), Mask R-CNN (with framework Detectron2), and U-Net models, making public the dataset with aerial images of soybeans and beans. The models were trained using a dataset consisting of 3021 images, randomly divided into test, validation, and training sets, which were annotated, resized, and increased using the Roboflow application interface. Evaluation metrics were used, which included training efficiency (mAP50 and mAP50-90), precision, accuracy, and recall in the model’s evaluation and comparison. The YOLOv8s variant achieved higher performance with an mAP50 of 97%, precision of 99.7%, and recall of 99% when compared to the other models. The data from this manuscript show that deep learning models can generate efficient results for automatic weed detection when trained with a well-labeled and large set. Furthermore, this study demonstrated the great potential of using advanced object segmentation algorithms in detecting weeds in soybean and bean crops.

The beneficial effects of silicon (Si) on plants have been widely reported for its fruit qualitative improvements, growth gains, and protection against abiotic and biotic stresses. This study aimed to evaluate the combined effect of soil water potential (Ψs) (−30 and −60 kPa) and the foliar application of Si (0.0 (control), 1.0, 2.0, 3.0, and 4.0 g L⁻¹) in the development of tomatoes grown in a greenhouse. We evaluated the biometric parameters and gas exchange in three periods (20, 34, and 48 days after planting). The rates of transpiration (E), stomatal conductance (gs), and net photosynthesis assimilation (A_n) were lower when the plants were subjected to water deficit. The foliar application of Si attenuated the effect of the water deficit in both levels applied to the crop. A high response was observed at −60 kPa, regardless of the evaluated period. However, a significant effect was not observed on the relative chlorophyll index and biomass accumulation when Si was applied. A foliar application up to 2.8 g L⁻¹ promotes increases in A_n,gs, and E. It is highlighted that Si can promote improvements in gas exchange when plants are affected by a water deficit.