Smart Farming: Improving Automated Broccoli Head Size Estimation

Abstract

Estimating the size of broccoli heads in the field is an important step towards automating broccoli harvesting, but it depends on first detecting the heads reliably and accurately in field imagery that contains harsh sunlight, camera blur, and leaf occlusion. This work investigates whether image preprocessing can improve such detection. Five common preprocessing techniques, CLAHE, unsharp masking, wavelet transform, median filtering, and bilateral filtering, were selected from the literature and evaluated individually and in five combinations. A systematic comparison was performed in which a YOLOv8n detector was trained and assessed with stratified five-fold cross-validation on a dataset of 394 field images. Performance was measured both over the entire dataset and over subsets representing difficult field conditions. On the full dataset the effect of preprocessing was negligible, as the baseline already performed close to the ceiling and differences between techniques were of the same order as the standard deviation between folds. However, in the difficult subsets preprocessing resulted in clear improvements, with the largest gains in the images on which the baseline struggled most. The most consistent results come from unsharp masking and the wavelet$+$unsharp combination. Combining techniques was not automatically beneficial. These results, obtained without significance testing, indicate that preprocessing is best understood as a tool for specific difficult conditions, rather than as a general improvement.