4 research outputs found
Using machine learning for image-based analysis of sweetpotato root sensory attributes
The sweetpotato breeding process involves assessing different phenotypic traits, such as the sensory attributes, to
decide which varieties to progress to the next stage during the breeding cycle. Sensory attributes like appearance,
taste, colour and mealiness are important for consumer acceptability and adoption of new varieties. Therefore,
measuring these sensory attributes is critical to inform the selection of varieties during breeding. Current methods
using a trained human panel enable screening of different sweetpotato sensory attributes. Despite this, such
methods are costly and time-consuming, leading to low throughput, which remains the biggest challenge for
breeders.
In this paper, we describe an approach to apply machine learning techniques with image-based analysis to
predict flesh-colour and mealiness sweetpotato sensory attributes. The developed models can be used as highthroughput methods to augment existing approaches for the evaluation of flesh-colour and mealiness for different
sweetpotato varieties. The work involved capturing images of boiled sweetpotato cross-sections using the DigiEye
imaging system, data pre-processing for background elimination and feature extraction to develop machine
learning models to predict the flesh-colour and mealiness sensory attributes of different sweetpotato varieties.
For flesh-colour the trained Linear Regression and Random Forest Regression models attained 2 values of
0.92 and 0.87, respectively, against the ground truth values given by a human sensory panel. In contrast, the
Random Forest Regressor and Gradient Boosting model attained 2 values of 0.85 and 0.80, respectively, for
the prediction of mealiness. The performance of the models matched the desirable 2 threshold of 0.80 for
acceptable comparability to the human sensory panel showing that this approach can be used for the prediction
of these attributes with high accuracy. The machine learning models were deployed and tested by the sweetpotato
breeding team at the International Potato Center in Uganda. This solution can automate and increase throughput
for analysing flesh-colour and mealiness sweetpotato sensory attributes. Using machine learning tools for analysis
can inform and quicken the selection of promising varieties that can be progressed for participatory evaluation
during breeding cycles and potentially lead to increased chances of adoption of the varieties by consumers