Application of computer vision and machine learning in morphological characterization of Adansonia digitata fruits

Abstract

Measuring fruit mass and volume is a time-consuming and tedious task that can affect production planning. This study developed a computer vision system to estimate the volume and mass of baobab fruits from single-view images captured from inexpensive and readily available cameras such as those in smartphones. The baobab fruits were collected from two study fields within the savanna ecological zone. Their images were captured, and subsequently, they were detected and segmented with over 97 % accuracy. The segmented images were binarized, and two-dimensional (2D) features such as the segmented area, centroid, bounding box, equivalent diameter, and major diameter were extracted from them. The volumes of the fruits were estimated from the 2D features using random forest, linear, polynomial, and radial support vector machine models. All the models achieved high goodness of fit; however, the random forest model delivered the best performance, with an R² value of 99.8 %. The relationship between mass and volume was a quadratic equation (mass = 38.23 + 0.25 × volume + 4.49e−05 × volume²) and had an R² value of 92 %. Correlations were validated via plots and statistical tests, and credible intervals of point estimates were determined from the posterior distributions of their samples. This highlights the potential of artificial intelligence methods to be applied in a less constrained environmental setting for ecological research and agricultural management. Commercial companies producing baobab powder and seed oil should apply these models for effective production planning. To enhance the model, it would be beneficial to gain a better understanding of how climate gradients affect the morphological characteristics of baobab fruits.