The study provides free Python scripts for plant phenotyping, demonstrating their ability to accurately predict lycopene content in tomatoes and chlorophyll content in basil leaves using image analysis.
Plant Methods · 4 authors, 3 centres
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The study provides free Python scripts for plant phenotyping, demonstrating their ability to accurately predict lycopene content in tomatoes and chlorophyll content in basil leaves using image analysis.
The paper presents a set of free, open-source Python scripts for plant phenotyping, including background and colour correction, object detection, and size and colour measurement. The scripts were validated using two examples: predicting lycopene content in tomatoes and chlorophyll content in basil leaves. Tomatoes were imaged under four lighting conditions with three cameras, showing colour correction reduced error. All cameras could predict lycopene, but only the DSLR camera accurately predicted content outside the training set. Basil chlorophyll was accurately predicted with a DSLR camera in a lightbox, but validation showed underprediction for out-of-sample leaves, highlighting the need for comprehensive training data. The method is fast, cheap, and non-destructive.