Localization of Garlic Apex by Digital Image Analysis Techniques
Sowing and harvesting are the most costly operations in the garlic production (Allium sativum L.), because they are carried out by hand. In order for mechanized sowing to be feasible, the garlic clove must be placed in the soil with the apex facing upwards because placing it randomly reduces its yield in up to 23 %. The objective of this study was to develop an algorithm to identify the garlic apex through artificial vision. For this purpose, a video camera and illumination lamps were used, with which digital images of the cloves of garlic were obtained. The images were processed to identify the apex in four steps: 1) capturing the image; 2) detecting the perimeter edge of the garlic cloves; 3) calculating the angles on the inside edge of the cloves and localizing the apex under the hypothesis that it coincides with the smallest angle on the inside edge; 4) identifying the need of reorienting the apex. The impact of the location and size in the correct identification of the apex was evaluated statistically and the border detection methods Canny, Roberts and Sobel were compared. The results did not show significant statistical differences (p0.10) between the different sizes and positions of the garlic when localizing the apex, which is convenient. However, there were significant statistical differences (p0.10) between the border detection methods, because Canny’s had a better performance in the localization of the apex. The algorithm developed in this research could be used to design a mechanical system to reorient the apex through artificial vision in garlic sowing.