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

Moshia Matshwene E., Mzini Loyiso L.
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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.
基于深度学习神经网络的柑橘果林青果检测新视角
整个柑橘产业正受到黄龙病(HLB)、柑橘绿化(Candidatus Liberibacter asiaticus)和柑橘溃疡病(Xanthomonas axonopodis pv.)等柑橘绝症的侵袭。citri)。有必要开发果实质量评价技术,在生长季节,当青果还在树上时,就能对青果的质量进行评价。学习技术是果实品质评价的基本技术之一。开发高效的自动化水果分类系统一直是行业的优先事项,因为通过人工目测进行水果评估具有主观性、高劳动力成本、低效率等缺点,最重要的是,由于繁琐而导致的不一致性。在水果发育的早期阶段估算柑橘类水果的产量,有助于种植者在可能的情况下调整特定地点的管理做法,提高水果产量,并及时计划收获作业,以最大限度地降低收获成本。此外,还要(i)估计感染和果实缺陷,(ii)在收获前估计柑橘林中的果实数量和潜在的果实大小,(iii)产量预测,以及(iv)进行经济估计,如收入规划和净利润计算。一种较好的识别方法是将柑桔绿果与背景中的柑桔绿叶区分开来。以前的方法在检测年轻水果和绘制地图或进行经济估算方面存在困难,因为年轻或未成熟的柑橘水果是绿色的,类似于树叶。机器学习的一些最新进展产生了训练深度神经网络的新技术,这些技术有可能成功识别物体的模式,如柑橘水果的形态结构,并将水果与主要作物区分开来。该系统采用前向传播的神经网络方法对一组图像进行分类并提高其性能。
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