Sugarcane is one of the main crops in the world due to its economic value promoted by the sale of its derivatives, such as bioethanol and sugar. In order to achieve greater economic performance and productivity in the sugarcane field, several digital image processing studies have been conducted on sugarcane field images. However, mapping and measuring gaps in the planting rows are still being performed manually on-site to determine whether to replant the entire area or only the gaps. High cost of time and manpower is required to perform the manual measurement. Based on that, the aim of this study is to present a novel method to detect crop rows and measure gaps in crop fields. Our method is also able to deal with curved crop rows, which is a real problem and substantially limits numerous solutions in practical applications. The proposed method is evaluated using a mosaic of real scene image that was prepared with the support of a small remotely piloted aircraft. Experimental tests showed a low relative error of approximately $1.65$ % compared to manual mapping in the planting regions, even for regions with gaps in the curved crop rows. It means that our proposal can identify and measure crop rows accurately, which enables automated inspections with high-precision measurements.

This work presents the development and usability evaluation of the MazeVR game, a maze game designed for the Google Cardboard device. The game allows interactions through head movements to control the camera and gestures on the smartwatch to perform additional actions, such as starting and stopping walking, picking up and discarding torches, and clearing the path. Gesture recognition on the smartwatch is performed using the continuous gesture recognition algorithm. The main objective was to create an immersive and challenging experience for players, allowing them to explore the maze and overcome obstacles using only head movements and gestures on the smartwatch. An evaluation was conducted with volunteer experts, using the SUS questionnaire as a data collection method. The results indicated a positive perception of the usability and effectiveness of the method. The experts considered the method intuitive, easy to learn and use, and highlighted the smooth integration of system functionalities. The results demonstrated satisfactory usability and a positive user experience.

Estimating foliar damage is essential in agricultural processes to provide proper crop management, such as monitoring the defoliation level to take preventive actions. Furthermore, it is helpful to avoid the reduction of plant energy production, nutrition decrement, and consequently, the reduction of the final production of the crop and economic losses. In this sense, numerous proposals support the defoliation estimate task, ranging from traditional methodologies to computational solutions. However, subjectivity characteristics, reproducibility limitations, and imprecise results persist. Then, these circumstances justify the search for new solutions, especially in defoliation assessments. The main goal of this paper consists of developing an automatic method to estimate the percentage of damaged leaf areas consumed by insects. As a novelty, our method provides high precision in calculating defoliation severity caused by insect predation on the leaves of various plant species and works effectively to estimate leaf loss in leaves with border damage. We describe our method and evaluate its performance concerning 12 different plant species. Our experimental results demonstrate high accuracy in the determination of leaf area loss with a correlation coefficient superior to 0.84 for apple, blueberry, cherry, corn, grape, bell pepper, potato, raspberry, soybean, and strawberry leaves, and mean absolute error (MAE) less than 4% in defoliation levels up to 54% in soybean, strawberry, potato, and corn leaves. In addition, the method maintains a mean error of less than 50%, even for severe defoliation levels up to 99%.