Towards autonomous cross-pollination: Portable multi-classification system for in situ growth monitoring of tomato flowers

Abstract

A fundamental step towards an autonomous cross-pollination process for tomato plants involves classifying tomato flowers in line with their maturity. This paper is concerned with the development of a novel, portable, and deep learning enabled multi-classification system for classifying maturity levels of tomato flowers. The multi-class classification is powered by individual tomato flower images. As a dataset with individual tomato flowers is currently unavailable commercially, a dataset with more than 2000 images was created ensuring diversity among maturity levels and several other factors. A Convolutional Neural Network (CNN) was trained and tested with this dataset to be integrated with a feasible technology to institute portability in the system. Given the common use of smartphones, an Android based mobile application was developed to enable portable access to the neural network. A user would be able to capture or insert a tomato flower image and receive the predicted level of maturity using the mobile application. Rigorous testing show that the portable system generates comprehensive predictions reflecting the 75% classification accuracy possessed by the neural network. This paper also highlights the introductory work leading to the above-mentioned initiatives, culminated with future work paving the way towards autonomous tomato flower cross-pollination.

Keywords

• Android development
• Convolutional neural networks
• Deep learning
• Machine learning
• Multi-class image classification
• Neural networks