CLASSIFIER OF IMAGES OF AGRICULTURAL CROPS SEEDS USING A CONVOLUTION NEURAL NETWORK

Abstract

This article discusses the creation of a convolutional neural network architecture that classifies
images of crops (in particular wheat) for subsequent use in an optical seed separator (photo
separator). Interest in the design of neural networks for classifying images has recently increased
significantly, which is associated both with the development of the theory of deep neural networks
and the increased computing power of desktop computers, as well as the transfer of computing to
graphic processors. The aim of the article is to develop the architecture of a neural network that
allows the separation of the input flow of wheat seeds into two classes: “good” seeds and “bad”
(with defects in shape and color) seeds. The architecture of the resulting neural network is convolutional,
because, unlike a fully connected one, this class of neural networks is within certain limits
immune to changes in the scale and angle of rotation of objects in the input data. In the work,
for the formation of training, validation and test samples, seed images obtained using a household
camera were used, which negatively affected the results of training and testing the neural network
regarding the possible result of application in a real photo separator. The architecture of the developed
neural network is preliminarily optimized for use on FPGAs, however, in the considered
case, the transition from the values of weighting factors from the data type from a floating point to
an integer type has not been made, which can lead to a decrease in the accuracy of the neural
network, while significantly reducing the amount of resources FPGA. Application of the proposed
architecture allows one to obtain a fairly accurate estimate of classified wheat seeds from verification
and test data sets.