Evaluation of the Effectiveness of the UNet Model with Different Backbones in the Semantic Segmentation of Tomato Leaves and Fruits

Timely identification of crop conditions is relevant for informed decision-making in precision agriculture. The initial step in determining the conditions that crops require involves isolating the components that constitute them, including the leaves and fruits of the plants. An alternative method for conducting this separation is to utilize intelligent digital image processing, wherein plant elements are labeled for subsequent analysis. The application of Deep Learning algorithms offers an alternative approach for conducting segmentation tasks on images obtained from complex environments with intricate patterns that pose challenges for separation. One such application is semantic segmentation, which involves assigning a label to each pixel in the processed image. This task is accomplished through training various models of Convolutional Neural Networks. This paper presents a comparative analysis of semantic segmentation performance using a convolutional neural network model with different backbone architectures. The task focuses on pixel-wise classification into three categories: leaves, fruits, and background, based on images of semi-hydroponic tomato crops captured in greenhouse settings. The main contribution lies in identifying the most efficient backbone-UNet combination for segmenting tomato plant leaves and fruits under uncontrolled conditions of lighting and background during image acquisition. The Convolutional Neural Network model UNet is is implemented with different backbones to use transfer learning to take advantage of the knowledge acquired by other models such as MobileNet, VanillaNet, MVanillaNet, ResNet, VGGNet trained with the ImageNet dataset, in order to segment the leaves and fruits of tomato plants. Highest percentage performance across five metrics for tomato plant fruit and leaves segmentation is the MVanillaNet-UNet and VGGNet-UNet combination with 0.88089 and 0.89078 respectively. A comparison of the best results of semantic segmentation versus those obtained with a color-dominant segmentation method optimized with a greedy algorithm is presented.