Power take-off rotation and operation quality of peanut mechanized digging

Ferezin, Evaldo; Voltarelli, Murilo Aparecido; Silva, Rouverson Pereira da; Zerbato, Cristiano; Cassia, Marcelo Tufaile

doi:10.5897/ajar2013.8032

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Thus, the peanuts digging operation could represent the PC1. It is noteworthy that the high impact of losses on the gathering of pods directly affected the final yield of the peanut crop (Ferezin et al, 2015).…”

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confidence: 99%

Multivariate Analysis of Peanut Mechanized Harvesting

et al. 2018

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The peanuts harvesting mechanization is affected by the soil physical characteristics and it may increase the losses due to the production of pods in subsurface. The objective of the experiment was to identify the clusters through multivariate exploratory approaches from similarity in six soil textures (very clayey, clayey, silty clayey loam, clayey loam, sandy loam and sandy) in the state of São Paulo, Brazil, determining the main agronomic variables that most influenced the clustering division to assist the decision-making process in peanuts mechanized harvesting. The data were analyzed by the multivariate exploratory that is performed to simplify the description of a set of interrelated variables, using: yield, maturity, soil and pod moisture content, windrow width and height, visible and invisible digging losses, and gathering losses, as agronomic indicators of quality. The low and high clay content were grouped into clusters I and III, respectively, according to the agronomic traits of the peanut crop. The principal components analysis (PC) allowed a single distribution of accesses since only two eigenvalues were higher than "one": the highest eigenvalues of 4.51 and 1.79, resulted in a Biplot that explained 70% of the original variability, 50.11% and 19.89% of which in the PC1 and PC2, respectively. The multivariate analysis indicated that high peanut yields in soils with low clay are correlated with the losses during the peanut mechanized harvesting operation.

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Section: San San San San San San San San San San Sal Sal Sal Sal Sal mentioning

confidence: 99%

Multivariate Analysis of Peanut Mechanized Harvesting

et al. 2018

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Monitoring of Soybean Mechanical Harvesting as a Function of Plot Shapes

et al. 2017

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Information on harvester performance is important in the management and optimization of mechanized agricultural systems. In this sense, the aim of this study was to monitor the operation quality of soybean mechanical harvesting in different plot shapes by means of statistical process control. The harvest was carried out in an agricultural area of a farm located in Uberaba, MG, Brazil, with an experimental design based on the assumptions of statistical quality control and 18, 28, and 24 replications for irregular, rectangular, and trapezoidal plots, respectively. The variables and/or quality indicators of harvester performance were forward speed, engine speed, cylinder speed, and concave opening. The lowest variation occurred in the irregular plot for the quality indicators forward speed and engine speed of harvester. Engine speed and the concave opening of harvester were unstable for all plot shapes. Only the cylinder speed of the soybean harvester presented process stability during operation for the three plot shapes.

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Automated Windrow Profiling System in Mechanized Peanut Harvesting

Senni,

Tronco,

Pedrino

et al. 2024

AgriEngineering

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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.

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Power take-off rotation and operation quality of peanut mechanized digging

Cited by 3 publications

References 7 publications

Multivariate Analysis of Peanut Mechanized Harvesting

Multivariate Analysis of Peanut Mechanized Harvesting

Monitoring of Soybean Mechanical Harvesting as a Function of Plot Shapes

Automated Windrow Profiling System in Mechanized Peanut Harvesting

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