Advanced Preprocessing Technique for Tomato Imagery in Gravimetric Analysis Applied to Robotic Harvesting

Abstract

In this study, we improve the efficiency of automated tomato harvesting by integrating deep learning into state-of-the-art image processing techniques, which improves the accuracy and efficiency of detection algorithms for robotic systems. We develop a hybrid model that combines convolutional neural networks’ dual two-dimensional matrices for classification and part affinity fields. We use data augmentation to improve the robustness of the model and reduce overfitting. Additionally, we apply transfer learning to solve the challenging problem of improving the accuracy of identifying a tomato’s center of gravity. When tested on 2260 diverse images, our model achieved a recognition accuracy of 96.4%, thus significantly outperforming existing algorithms. This high accuracy, which is specific to the environmental conditions and tomato varieties used, demonstrates the adaptability of the model to real-world agricultural conditions. Our results represent a significant advancement in the field of agricultural autotomization by demonstrating an algorithm that not only identifies ripe tomatoes for robotic harvesting with high accuracy, but also adapts to various agricultural conditions. This algorithm should reduce manual labor in agriculture and offer a more efficient and scalable approach for the future agricultural industry.