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.

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%.

The increasing global population has heightened the demand for food, making it crucial to optimize agricultural practices. As a versatile crop, corn plays a crucial role in global food security, particularly in countries like the United States, Brazil, China, and Argentina, where corn farming is a major economic driver. To meet the growing demand and ensure sustainable practices, innovative technologies such as Unmanned Aerial Vehicles (UAVs) are being increasingly utilized in agriculture. UAVs provide efficient monitoring and sustainable methods, enabling farmers to gather precise data on crop health and yield potential. However, to capture high-resolution images that are essential for accurate analysis, UAVs often need to fly at lower altitudes. This can lead to challenges, such as crop movement caused by the wind generated by the UAV propellers, which can affect the quality of the data collected. On the other hand, images captured at high altitudes present challenges, such as blur and low resolution, which can compromise the extraction of essential characteristics of the crop under analysis. To overcome these limitations, the authors propose employing deep learning-based Super Resolution (SR) algorithms such as MuLUT, LeRF, and Real-ESRGAN to improve image resolution post-flight of corn culture. Experimental results demonstrate significant resolution in the images improvements using these algorithms compared to traditional interpolation methods, with gains of 364.13%. Even though SR techniques have been developed for other purposes, such as enhancing the resolution of images featuring people and landscapes, their considerable performance in agricultural images can be observed. Regarding future investigations, we plan to further refine the accuracy of our approach and extend comparisons with other algorithms. Additionally, we intend to test these techniques with different datasets, including satellite images, to assess their versatility and applicability in various scenarios.