CAJ | 학술논문

常规的施药方法,由于不考虑树与树之间的间距、树冠的大小与形状,大部分农药未能发挥效用,流失非常严重,喷撒出去的农药只有极少部分能到达要防治的靶标上,从而造成了巨大的农药浪费,且带来了农药安全、环境污染等诸多问题.笔者在分析农药的喷药量与树形之间关系的基础上,对原有的基于BP神经网络的树形识别系统进行了改进设计,重点对该网络的输入层、隐含层及输出层进行了理论分析和试验研究,最终确定了由15个节点组成输入层,分别代表树冠的形状特征和树木图像的分形维数特征;6个节点组成隐含层,6个节点组成输出层,分别代表要判断树形的种类.对16个样本实时处理的试验表明,除了1个样本因未能分割外,其他样本均可识别,而且稳定性有了很大提高,适合于农药的精确施用.
상규적시약방법,유우불고필수여수지간적간거、수관적대소여형상,대부분농약미능발휘효용,류실비상엄중,분살출거적농약지유겁소부분능도체요방치적파표상,종이조성료거대적농약낭비,차대래료농약안전、배경오염등제다문제.필자재분석농약적분약량여수형지간관계적기출상,대원유적기우BP신경망락적수형식별계통진행료개진설계,중점대해망락적수입층、은함층급수출층진행료이론분석화시험연구,최종학정료유15개절점조성수입층,분별대표수관적형상특정화수목도상적분형유수특정;6개절점조성은함층,6개절점조성수출층,분별대표요판단수형적충류.대16개양본실시처리적시험표명,제료1개양본인미능분할외,기타양본균가식별,이차은정성유료흔대제고,괄합우농약적정학시용.
Pesticides have been applied homogeneously no matter whether there were some spaces among sprayed target trees. The individual differences of tree crowns were considered as a key problem. The improved tree crown recognition system based on BP artificial neural networks was used to increase estimated precision of target trees in this paper. It also emphasized the experimental and theoretical studies on determination of nodes in layers of ANN. There are 15 nodes in the input layer, 6 nodes in the hidden layer and 6 nodes in the output layer. The experiments in the real time showed that the improved tree crown recognition system was more practical than previous one.