首页|Application of automated image colour analyses for the early-prediction of strawberry development and quality.
Application of automated image colour analyses for the early-prediction of strawberry development and quality.
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NSTL
Elsevier
? 2022Labour and production costs are considered major production challenges for strawberry (Fragaria sp.) farmers, due to the reliance on manual harvesting methods. Automation has been proposed as a desirable solution, in particular robotic-driven harvesting with in-built decision making for determination of fruit ripeness and early-prediction of harvest timing and conformity to industry quality parameters (fruit weight and length). To support the development of these automated processes, the work presented herein explored the capacity to utilise automated image analysis for the prediction of strawberry quality measures. This involved the hydroponic growth of strawberry plants under controlled conditions and the daily collection of photographs of individual flowers and fruit. Machine learning (ML)-driven image colour extraction from the collected 1685 strawberry images utilised object detection to identify flowers and fruit within images, followed by cropping and counting of remaining image pixels, which were assigned based on pixel RGB to one of 10 pre-defined groups: achromatic, blue, cyan, green, orange, pink, purple, red, white, and yellow. These colour measures were utilised as inputs for general regression with 10-fold cross-validation to generate 3 models: for the prediction of current-state fruit developmental stage (R2 = 0.9071), current-state fruit length (R2 = 0.8565), and days remaining until harvest (R2 = 0.8694). Additionally, current-state fruit development stage and current-state length were utilised as inputs for general regression with 10-fold cross-validation to develop predictive models for the endpoint (harvest) key quality measures: fruit harvest-length (R2 = 0.8817) and fruit harvest-weight (R2 = 0.7252). Noting that days to harvest could be accurately predicted up to 15 days prior to harvest, and the harvest quality measures could be accurately predicted up to 22 days prior to harvest, the models presented herein may be utilised to increase automation and thereby improve efficiency in the scheduling of harvesting and quality control of strawberry farming.
AgritechBBCHBiologische Bundesanstalt Bundessortenamt und Chemische IndustrieFragariaHarvest timingMachine learning
NSTL
Elsevier
