Automated Windrow Profiling System in Mechanized Peanut Harvesting - Senni, Alexandre Padilha; Tronco, Mario Luiz; Pedrino, Emerson Carlos; Silva, Rouverson Pereira da [UNESP] (MDPI, 2024)
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.
Keywords: 3D reconstruction; Arachis hypogaeaL; computer vision; mechanized peanut harvesting; precision agriculture