Abstract
Abstract: This study investigates the potential use of close-range and low-cost terrestrial RGB imaging sensor for fruit detection in a high-density apple orchard of Fuji Suprema apple fruits (Malus domestica Borkh). The study area is a typical orchard located in a small holder farm in Santa Catarina’s Southern plateau (Brazil). Small holder farms in that state are responsible for more than 50% of Brazil’s apple fruit production. Traditional digital image processing approaches such as RGB color space conversion (e.g., rgb, HSV, CIE L*a*b*, OHTA[I 1 , I 2 , I 3 ]) were applied over several terrestrial RGB images to highlight information presented in the original dataset. Band combinations (e.g., rgb-r, HSV-h, Lab-a, I” 2 , I” 3 ) were also generated as additional parameters (C1, C2 and C3) for the fruit detection. After, optimal image binarization and segmentation, parameters were chosen to detect the fruits efficiently and the results were compared to both visual and in-situ fruit counting. Results show that some bands and combinations allowed hits above 75%, of which the following variables stood out as good predictors: rgb-r, Lab-a, I” 2 , I” 3 , and the combinations C2 and C3. The best band combination resulted from the use of Lab-a band and have identical results of commission, omission, and accuracy, being 5%, 25% and 75%, respectively. Fruit detection rate for Lab-a showed a 0.73 coefficient of determination (R2), and fruit recognition accuracy rate showed 0.96 R2. The proposed approach provides results with great applicability for small holder farms and may support local harvest prediction.
Highlights
Agriculture is crucial for the Brazilian trade balance
Color transformation till the fruit counting takes only 4.2 seconds per image, what resulted in total time of 84 seconds
Bands that presented commission errors with values higher than 20% (0.2) were: rgb-r, HSV-h, I’’2 and I’’3. These high error values exclude the possibility of using a single band for apple fruit detection
Summary
Agriculture is crucial for the Brazilian trade balance. Improvement in management practices helped productivity grow for different cultures and per area. Some studies focused on the use portable sensors in fruit growth monitoring using either active (Berk et al, 2016; Colaço et al, 2017; Escolà et al, 2017) or passive remote sensing sensors (Coelho Filho et al, 2005; Zhou et al, 2012; Linker, 2017). Given their high cost, these systems are usually not affordable for small holder farms and production estimates in several orchards are still based on visual counting techniques. This open new perspectives for the benefits of using low-cost cameras in robotic vision (Gongal et al, 2016 and An et al, 2017), fruit detection using digital image processing techniques (Font et al, 2014a; Wei et al, 2014 and Liu et al, 2016), automatic harvesting (Font et al, 2014b and Kang and Chen, 2019) and yield estimation (Bargoti and Underwood, 2017; Dorj, Lee and Yun, 2017)
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