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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. [ABSTRACT FROM AUTHOR]

Integrating advanced technologies such as artificial intelligence (AI) with traditional agricultural practices has changed how activities are developed in agriculture, with the aim of automating manual processes and improving the efficiency and quality of farming decisions. With the advent of deep learning models such as convolutional neural network (CNN) and You Only Look Once (YOLO), many studies have emerged given the need to develop solutions to problems and take advantage of all the potential that this technology has to offer. This systematic literature review aims to present an in-depth investigation of the application of AI in supporting the management of weeds, plant nutrition, water, pests, and diseases. This systematic review was conducted using the PRISMA methodology and guidelines. Data from different papers indicated that the main research interests comprise five groups: (a) type of agronomic problems; (b) type of sensor; (c) dataset treatment; (d) evaluation metrics and quantification; and (e) AI technique. The inclusion (I) and exclusion (E) criteria adopted in this study included: (I1) articles that obtained AI techniques for agricultural analysis; (I2) complete articles written in English; (I3) articles from specialized scientific journals; (E1) articles that did not describe the type of agrarian analysis used; (E2) articles that did not specify the AI technique used and that were incomplete or abstract; (E3) articles that did not present substantial experimental results. The articles were searched on the official pages of the main scientific bases: ACM, IEEE, ScienceDirect, MDPI, and Web of Science. The papers were categorized and grouped to show the main contributions of the literature to support agricultural decisions using AI. This study found that AI methods perform better in supporting weed detection, classification of plant diseases, and estimation of agricultural yield in crops when using images captured by Unmanned Aerial Vehicles (UAVs). Furthermore, CNN and YOLO, as well as their variations, present the best results for all groups presented. This review also points out the limitations and potential challenges when working with deep machine learning models, aiming to contribute to knowledge systematization and to benefit researchers and professionals regarding AI applications in mitigating agronomic problems. [ABSTRACT FROM AUTHOR]

The production of forest species that present seedlings with exogenous dormancy is limited by low germination rates, which can be overcome by rupturing or weakening the tegument. This study aimed to evaluate the efficiency of low-cost methods of overcoming dormancy and determine the effect of storage on the seeds of Enterolobium contortisiliquum. Four storage periods were used, freshly collected seeds (0), stored for 6, 12 and 18 months, and four methods were applied, mechanical scarification and immersion in water (EI), mechanical scarification (ES), immersion in water (IM) and control (TE). We obtained the percentage values of seedling emergence (EP) and the mean emergence time (TME). Conductivity and moisture were obtained as indicators of the physical and physiological quality of seeds, respectively. Variations in moisture content and electrical conductivity during storage did not have effect on the percentage of germination. EI treatment was more efficient for overcoming dormancy than the ES was, regardless of storage time. IM and TE treatments did not lead to germination. EI was the most efficient treatment for decreasing the TME. The results indicate that alternative and low-cost methods are efficient at overcoming exogenous dormancy and thus can be used to optimize the process of seedling production. [ABSTRACT FROM AUTHOR]

Information regarding the correlation between characters are of great importance to simultaneous traits selection in breeding programs. However, quantification and interpretation of the correlations magnitude do not imply in direct and indirect effects. In this context, the path analysis is presented as an alternative and viable tool for the selection process. The objective of this study was to evaluate the phenotypic, genotypic and environmental correlations between eight major agronomic characters in pepper lines (C. chinense L.) and its consequences in direct and indirect effects by path analysis. Eight agronomic traits were assessed under field conditions in 22 C. chinense L lines. According to the results, there is a genetic variability among the evaluated lines, whose yield per plant (PY) and total number of fruits (NF) variables are highly correlated with high direct effect on pepper lines yield. The selection of pepper lines with high yield can be accomplished through direct selection or using a suitable selection index, being advantageous to select lines with other characteristics, such as appropriate scion height and diameter for conducting manual and mechanized harvesting. The use of the constant k is efficient to reduce the variance of path coefficients, providing better estimation of the effects that affects yield. [ABSTRACT FROM AUTHOR]