Broccoli for the fresh market in the Netherlands is still harvested manually, which is labor-intensive and increasingly difficult to sustain as seasonal labor declines. Existing mechanized harvesters cut entire fields at once and cannot account for plant-to-plant variation, leading to substantial losses when heads differ in maturity. These constraints motivate plant-level, data-driven growth modeling to support selective and more efficient harvesting.

This thesis investigates how field-based measurements can be used to model broccoli growth at the level of individual plants. The work addresses four problems: converting raw field video into plant-level growth curves, investigating which environmental parameters best describe the growth, determining whether cumulative temperature or thermal time better represents broccoli development, and evaluating how different growth models capture head diameter growth. A preliminary study using an external dataset creates the methodological foundation by analysing broccoli growth and benchmarking classical and neural models. A field study in a Dutch production environment expands this work through the development of a data-processing pipeline that includes head detection, plant identification, tracking, diameter estimation, and integration with local weather data. Across both studies, thermal time provides a more biologically meaningful predictor of development than cumulative temperature. Model comparison shows that classical parametric models capture general developmental trends, while a multi-layer perceptron achieves the highest predictive accuracy, with a mean absolute error of 0.583~cm, when multiple environmental variables are included. The results demonstrate that field-based predictive modeling can support precision-agriculture applications such as harvest planning and selective mechanized harvesting.