Rouverson Pereira da Silva

Seed moisture content (SMC) fluctuations and machine adjustments during mechanical harvesting are associatedwith soybean seed quality and process variability. This study aimed to monitor the physical and physiological quality of soybean seeds and evaluate process variability during mechanical harvesting using Statistical Process Control (SPC) tools. Seed samples were collected at pre-established intervals from two harvesting systems operating in the same production field. Seed moisture content, purity, mechanical damage, germination, and vigor were evaluated throughout the harvesting operation. The average SMC was 15.8%, exceeding the range commonly recommended for soybean seed harvesting. Temporal fluctuations in SMC were associated with adjustments in Cylinder Rotation Speed (CRS) and Cylinder Opening (CO), which coincided with differences in process stability between the evaluated harvesting systems. Control charts identified periods of instability and out-of-control observations associated with changes in moisture conditions, machine adjustments, and operator interventions. Under the evaluated conditions, the harvesting system represented by Combine A was associated with greater process stability and higher values for purity, germination, accelerated aging, and seedling emergence. Although mechanical damage did not differ between harvesting systems, physiological tests were more sensitive in detecting differences associated with harvesting conditions. Statistical Process Control effectively identified temporal variability and process instability throughout the harvesting operation, providing a comprehensive approach for monitoring soybean seed quality and supporting decision-making during mechanical harvesting.

Mechanized sugarcane harvesting can have negative implications, depending on soil moisture and climate conditions. Among the associated challenges is soil compaction, caused by heavy machinery traffic under inadequate moisture and speed conditions, which limits sugarcane yield and longevity. Therefore, the objective of this study was to evaluate sugarcane yield and agroindustrial performance as a function of harvesting speed during dry and rainy seasons. A two-year experiment was conducted in the eastern region of Paraíba state, Brazil, during the dry and rainy seasons, using a randomized block design. Harvester speeds (2, 3, 4, 5, and 6 km h-1) were used in two harvesting seasons (dry season – Site 1; and rainy season – Site 2), with four replicates. The results indicated that harvesting in the dry season resulted in a greater number of culms per meter, culm diameter, internode length, and leaf area at the end of the ratoon cycle. Conversely, the rainy season favored culm height and internode number. Harvester speed of 5 km h-1 increased leaf area and culm diameter in ratoon cane. It was concluded that sugarcane harvested in the dry season exhibited better vegetative performance throughout the ratoon cycle. Furthermore, different harvesting speeds did not significantly affect the industrial quality of the cane, regardless of the harvest season.

The mechanized harvesting of peanut crops results in both visible and invisible losses. Therefore, monitoring and accurately determining pod maturation are essential to minimizing such losses. The objectives of this study were to (i) identify the most relevant variables for estimating peanut pod maturation and (ii) estimate two maturation indices (brown and black classes; orange, brown, and black classes) using Remote Sensing (RS) and Artificial Neural Networks (ANN), while assessing the generalization potential of the models across different areas. The experiment was carried out in two commercial peanut fields in the state of São Paulo, Brazil, during the 2021/2022 and 2022/2023 growing seasons, using the IAC 503 cultivar. Data collection began one month before the expected harvest date, with weekly intervals. Spectral variables and vegetation indices were obtained from orbital remote sensing (PlanetScope), while climatic data were retrieved from NASA POWER. For analysis, two ANN architectures were employed: Multilayer Perceptron (MLP) and Radial Basis Function (RBF). The dataset from the Cândido Rodrigues site was split into 80% for training and 20% for testing. The model was then evaluated and generalized using data from the Guariba site. Variable selection involved filtering via Principal Component Analysis (PCA) followed by the Stepwise method. Both models demonstrated high accuracy (R² ≥ 0.90; MAE between 0.06 and 0.07). Generalization tests yielded promising results (R² between 0.59 and 0.64; MAE between 0.13 and 0.17), confirming the robustness of the approach under different conditions.

The operational stability of agricultural tractors is directly influenced by the mass distribution between axles, particularly when using mounted implements with variable loads. This study aimed to evaluate how different masses of a mounted sprayer (550 kg, 850 kg, and 1150 kg) and tire inflation pressures (151.7–193.1 kPa) affect the load distribution between axles, tire contact area, center of gravity (CG) displacement, and tractor lead ratio. A 3 × 4 factorial design was adopted with a statistical analysis of key parameters across 12 experimental combinations. The results demonstrated that increasing implement mass significantly shifted the load toward the rear axle, reducing the front axle load by up to 46% and displacing the CG rearward by more than 11 cm, thereby compromising stability. Tire pressure, as well as the interaction between mass and pressure, also exhibited statistically significant influence on load distribution and CG positioning while modulating the tire contact area. The lead ratio increased linearly with mass, exceeding the recommended 5% threshold when the sprayer was at full capacity. These findings indicate that while the implement mass exerts a dominant effect, tire pressure management represents a statistically relevant factor for stability, requiring integrated management that considers the interaction between ballasting and tire inflation to mitigate operational risks.

This study aims to enhance the efficiency of mechanized sugarcane (Saccharum spp.) harvesting by optimizing the maneuvers of the auxiliary vehicle (infield wagon) operating in tandem with the harvester. The optimization focuses on increasing productivity and reducing soil compaction in high-traffic maneuver areas. A comprehensive three-year experimental study was conducted in agricultural fields at a Brazilian mill, where extensive field data and economic parameters were collected to simulate and compare maneuvering techniques on both flat and sloped terrain. To evaluate the synchronization between the harvester and the infield wagon under non-optimized scenarios, a Maneuver Simultaneity Index (MSI) was developed. The results demonstrated that the optimized “P-optimized” technique, which utilizes dedicated maneuvering spaces, significantly increased economic returns per hour worked by reducing maneuver times across various terrain types. Additionally, fuel consumption decreased with the adoption of the optimized maneuver pattern, leading to lower operational costs and reduced environmental impact. These findings highlight the economic and operational feasibility of optimizing maneuver patterns, particularly in flat areas, and provide a sustainable framework adaptable for improving efficiency and productivity in diverse mechanized agricultural operations.

In peanut cultivation, the fact that the fruits develop underground presents significant challenges for mechanized harvesting, leading to high loss rates, with values that can exceed 30% of the total production. Since the harvest is conducted indirectly in two stages, losses are higher during the digging/inverter stage than the collection stage. During the digging process, losses account for about 60% to 70% of total losses, and this operation directly influences the losses during the collection stage. Experimental studies in production fields indicate a strong correlation between losses and the height of the windrow formed after the digging/inversion process, with a positive correlation coefficient of 98.4%. In response to this high correlation, this article presents a system for estimating the windrow profile during mechanized peanut harvesting, allowing for the measurement of crucial characteristics such as the height, width and shape of the windrow, among others. The device uses an infrared laser beam projected onto the ground. The laser projection is captured by a camera strategically positioned above the analyzed area, and through advanced image processing techniques using triangulation, it is possible to measure the windrow profile at sampled points during a real experiment under direct sunlight. The technical literature does not mention any system with these specific characteristics utilizing the techniques described in this article. A comparison between the results obtained with the proposed system and those obtained with a manual profilometer showed a root mean square error of only 28 mm. The proposed system demonstrates significantly greater precision and operates without direct contact with the soil, making it suitable for dynamic implementation in a control mesh for a digging/inversion device in mechanized peanut harvesting and, with minimal adaptations, in other crops, such as beans and potatoes.