Comprehensive Analysis of Model Errors in Blueberry Detection and Maturity Classification: Identifying Limitations and Proposing Future Improvements in Agricultural Monitoring

Cristhian A. Aguilera (), Carola Figueroa-Flores, Cristhian Aguilera and Cesar Navarrete
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Cristhian A. Aguilera: Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Lago Panguipulli 1390, Puerto Montt 5501842, Chile
Carola Figueroa-Flores: Departamento de Ciencias de la Computación y Tecnologías de la Información, Facultad de Ciencias Empresariales, Universidad del Bío-Bío, Chillan 3800708, Chile
Cristhian Aguilera: Departamento de Ingeniería Eléctrica y Electrónica, Facultad de Ingeniería, Universidad del Bío-Bío, Concepción 4051381, Chile
Cesar Navarrete: Departamento de Ingeniería Eléctrica y Electrónica, Facultad de Ingeniería, Universidad del Bío-Bío, Concepción 4051381, Chile

Agriculture, 2023, vol. 14, issue 1, 1-14

Abstract: In blueberry farming, accurately assessing maturity is critical to efficient harvesting. Deep Learning solutions, which are increasingly popular in this area, often undergo evaluation through metrics like mean average precision (mAP). However, these metrics may only partially capture the actual performance of the models, especially in settings with limited resources like those in agricultural drones or robots. To address this, our study evaluates Deep Learning models, such as YOLOv7, RT-DETR, and Mask-RCNN, for detecting and classifying blueberries. We perform these evaluations on both powerful computers and embedded systems. Using Type-Influence Detector Error (TIDE) analysis, we closely examine the accuracy of these models. Our research reveals that partial occlusions commonly cause errors, and optimizing these models for embedded devices can increase their speed without losing precision. This work improves the understanding of object detection models for blueberry detection and maturity estimation.

Keywords: blueberry detection; maturity estimation; edge computing; smart agriculture; computer vision; machine learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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