A New Perspective on Detection of Green Citrus Fruit in the Grove Using Deep Learning Neural Networks

Abstract

The entire citrus industry is under attack by terminal citrus diseases such as Huanglongbing (HLB) or citrus greening ( Candidatus Liberibacter asiaticus ), and citrus canker ( Xanthomonas axonopodis pv. citri). There is a need to develop fruit quality assessment techniques that can assess the quality of green citrus fruits during the growing season when green fruits are still on the trees. Learning technique is one of the fundamental techniques for fruit quality evaluation. Efforts to develop an efficient automated fruit classification system continued as an industry priority since fruit evaluation through human visual inspection has drawbacks such as subjectivity, high labour costs, inefficiency, and most importantly, the in consistency caused by tediousness. Estimating citrus fruit yield at an earlier stage of fruit development can benefit grow ers to adjust site-specific management practices while it is possible, to increase fruit yield, and plan harvest operations on time to minimize harvesting costs. Also, to, (i) Estimate the infections and fruit defects, (ii) Estimate the number of fruits in the citrus grove and potential fruit size before harvesting, (iii) Yield prediction, and (iv) Make economic estimates such as planning of incomes, and calculation of net profit. An excellent recognition method would be the one that can separate green citrus fruits from background green leaves of citrus trees in the grove. The previous methods had diffi culty in detecting young fruits and creating maps or make economic estimates because young or immature citrus fruits are green and resemble tree leaves. Some recent advances in machine learning yielded new techniques to train deep neural networks, which has the potential to successfully recognize patterns of objects such as citrus fruit’s morphological structure and differentiate fruits from the main crop. This system uses forward propagation, which is a neural network way of classifying a set of images and to improve their performance.