An improved framework based on tracking-by-detection for simultaneous estimation of yield and maturity level in cherry tomatoes

Abstract

Automated and accurate counting and ripeness level estimation play key roles in the precision management of cherry tomatoes in greenhouses. This study presents an enhanced framework based on tracking by detection to simultaneously estimate the count and ripeness levels for cherry tomato bunches in greenhouses. Ripeness value is determined by the proportional relationship between the number of ripe and unripe fruits in a tomato bunch. To accurately detect fruit kernels in small sizes and blurred ROIs, a lightweight detector called NanoDet is employed. To reduce ripeness estimation errors caused due to shading, the ripeness value with the highest frequency is selected as the estimate when multiple detections of the same bunch are detected. The ripeness values are categorized into five levels and combined with the counting results. The study’s results demonstrate that it achieves 92.79% accuracy in counting and 90.84% accuracy in estimating ripeness levels. The improved algorithm has a processing rate of 15 frames per second. These results demonstrate the potential and value of the enhanced method for counting cherry tomatoes and estimating their ripeness levels.