Determination of Lycopersicon maturity using convolutional autoencoders

• Published in: Scientia horticulturae Vol. 256; p. 108538
• Main Authors: Kao, I-Hsi, Hsu, Ya-Wen, Yang, Ya-Zhu, Chen, Ya-Li, Lai, Yi-Horng, Perng, Jau-Woei
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.10.2019
• Subjects: agricultural sciences; Agriculture; algorithms; Artificial neural networks; automatic detection; computer science; Computer vision; fruit maturity; fruits; harvest date; Image processing; neural networks; Solanum; Supervised learning; vegetables; Computer vision; Agriculture; Image processing; Supervised learning; Artificial neural networks
• ISSN: 0304-4238; 1879-1018
• DOI: 10.1016/j.scienta.2019.05.065

The field of computer science is witnessing the development of new and advanced applications in agricultural science and related technologies. Accurate evaluations of the ripeness of fruits and vegetables are very important in agricultural science as fruit growers can profit from the automatic detection and interpretation of fruit maturity levels. In this study, we propose a method of classifying Lycopersicons based on three maturity levels (immature, semi-mature, and mature). Our method includes two artificial neural networks, a convolutional autoencoder (CAE), and a backpropagation neural network with a Softmax layer. A CAE involves the convergence of convolutional neural networks and an autoencoder and has recently gained considerable attention in the field of Engineering. However, a traditional backpropagation neural network also plays an important role in the proposed method. To adapt the classification system to various complex scenarios, the CAE functions as a background filter, and it determines the region of interest (ROI) in an image. A contribution of this study is the use of a CAE to determine the ROI in the Lycopersicon image instead of tuning handcrafted parameters manually to set the ROI. The machine detects the Lycopersicon through self-learning mechanisms. Using the extracted features, the machine employs self-learning mechanisms to determine Lycopersicon maturity. The experimental results demonstrate that our method can recognize maturity levels with an accuracy rate of 100%. Therefore, the proposed algorithm provides objective and useful information concerning maturity to optimize the harvest time of Lycopersicons.