Classification of Harvest - Non Harvest in Rice Plant Image Using Convolutional Neural Network Algorithm

Abstract

The Area Sample Framework (ASF) survey is an area based survey carried out by direct observation of sample parts whose locations have been determined. Every month ASF officers take photos of observation results using an Android based cellphone, where the results of the photos will be classified manually by supervision officers and sent to a central server for processing. The large amount of rice plant image data included can hinder officers in classifying rice growth phases. Therefore, to speed up the classification process, the Convolution Neural Network (CNN) method is used. In this research, the CNN model built consists of 3 convolution layers, 3 pooling, ReLU and Sigmoid activation functions, with several other parameters such as batch size and epoch value. The training results show that the accuracy value for the training data is 92.86% with an epoch value of 120. Meanwhile, the accuracy value for the validation data is 69.01%. Model evaluation shows a precision value of 21.34% and a recall value of 32.20%. This shows that the CNN model has poor performance in predicting harvest and non-harvest in rice plant images.