LocAnalyzer : A computer vision method to count locules in tomato fruits

SPETALE, FLAVIO E.; MURILLO, JAVIER; VAZQUEZ, DANA V.; CACCHIARELLI, PAOLO; RODRIGUEZ, GUSTAVO R; TAPIA, ELIZABETH

COMPUTERS AND ELETRONICS IN AGRICULTURE

ELSEVIER SCI LTD

Lugar: Amsterdam; Año: 2020 vol. 173

0168-1699

Fruit production represents an important economic resource for many countries. The size and shape of fruits arecrucial in production systems because they affect the market value. Particularly, in tomatoes (Solanum lyco-persicum), the number of seed-containing cavities within each fruit, called locules, affects these attributes. Thenumber of locules is also relevant for genotype-phenotype association studies designed to accelerate tomatobreeding programs. Traditionally, the determination of the number of locules was performed through a visualinspection of a cross section of the fruit, a laborious, time-consuming, and highly subjective task. In this work, anautomatic computer vision method for the identification and counting of the number of locules in tomato fruitimages, called LocAnalyzer , is presented. The aim of LocAnalyzer is to speed up the processing time and reducethe subjectivity relative to the traditional manual approach for locule counting. The method was tested on tworeal tomato datasets. Promising results in terms of accuracy, precision, and recall measures were obtained,suggesting the potential usefulness of the approach for the development of a tool for the automatic measurementof other internal tomato attributes. Additionally, an experiment comparing the capacity of domain experts andLocAnalyzer in the identification of the number of locules was performed. The fact that expert- LocAnalyzer inter-group dispersion is smaller than expert-expert dispersion suggests that LocAnalyzer could be used as a goldstandard for counting the number of locules. The proposed method, which is freely available, was implementedin the R programming language, and a web-based application was developed for online tests.