High-yield phenotyping in evaluating the productivity of a dialell with tomato plant

Abstract

Tomato ( Solanum lycopersicum L.) is one of the most important vegetables in the world economy, consequently, it presents as a model organism for biotechnology research and plant breeding programs. The phenotyping by image is a technique that can be applied in these programs and allows the analysis of the experiment quickly, accurately, objectively and without destroying samples. In this sense, the objective of this study was to establish methodologies for phenotyping of the productivity in tomato plant, through computational analysis of images using the Mask-RCNN algorithm and to test its efficiency in a balanced diallel without reciprocals with the tomato plant. They were evaluated the F1′s at the time of fruit harvest by the traditional phenotyping for productivity and, in parallel, images were captured for evaluation by Mask-RCNN. They were estimated six variables by Mask-RCNN, namely the number of green fruits, number of ripe fruits, number of total fruits, percentage of the area in the image occupied by ripe fruits (PFM), percentage of the area occupied by green fruits (PFV) and percentage of area occupied by all fruits (PFT). Accuracy for recognition was 85 % for ripe fruits and 88 % for green fruits. The model achieved recall of 92 % for ripe fruits and 84 % for green fruits. It was observed a significant correlation between the characters evaluated in traditional and computational way, all with positive values and close to one. The ranks correlation between fruit productivity, obtained by traditional phenotyping and the variables estimated by Mask-RCNN, was high, especially for the number of ripe and total fruits. It was concluded from the results that image analysis is efficient and can be used in high-throughput phenotyping in tomato plant genetic improvement.

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