Due to the economic differences of chili peppers at different maturity levels and the absence of differentiation of maturity in existing harvesting processes, it is crucial to accurately identify the maturity during harvesting for improving economic benefits. This paper investigated pepper maturity recognition models using hyperspectral technology to realize intelligent pepper harvesting with high accuracy and monitor maturity. Hyperspectral data (400-1000 nm) for field line peppers were collected, preprocessed using normalization, Savitzky-Golay convolutional smoothing, and standard normal variable transformation and subsequently used to train back propagation neural network (BP) and kernel based extreme learning machine (KELM) models. Principal component analysis (PCA) was used to reduce data dimensionality and identify characteristic spectral wavelengths for pepper maturity at 862.2, 676.9, 578.1, and 980.7 nm; and BP, PCA-BP, KELM, and PCA-KELM models were subsequently established. The precision for the KELM and PCA-KELM models (99.5% and 97.3%, respectively) was superior to the other two models; however, the PCA-KELM model used only four of the feature wavelengths as inputs, hence it required only 1/44 the data compared with the KELM model. Thus, the PCA-KELM model achieved high recognition accuracy and training speed, offering an effective method to discriminate pepper ripeness based on hyperspectral features.

为实现烟叶快速准确的识别分级,提出了一种改进的Faster R-CNN分级算法.以VGG16网络为训练原型,通过调整训练图像的尺寸、学习率、mini-batch等全连接层参数,在此基础上将ROI pooling改进为ROI align,再去掉网络模型的第8、第12、第15层卷积层,同时引入Inception结构层为研究的最终分级模型.以准确率和召回率作为分级模型性能的评价指标,利用最终分级模型训练7个等级的烟叶图像,识别准确率最低为90.62%,最高为92.46%,召回率最低为91.72%,最高为92.87%,平均识别准确率达到92.35%,识别速度达到0.2s/幅.改进后的模型平均识别准确率相比原始网络平均识别准确率提高了3.98%.

小型自走式施肥机在田间工作时,由于速度不同、地表不平整等因素,会对施肥机造成较大的振动影响,降低工作效率且对人体产生一定的影响.为此,采用正交试验分析的方法,分析了影响小型自走式施肥机排肥导管和扶手振动因素的主次顺序;采用快速傅里叶变换的方法,分析了排肥导管及扶手的固有频率.结果表明:排肥导管的固有频率为4.39～41.10Hz、速度为v=0.5m/s时,激振频率fe=17.60Hz,排肥导管振动最剧烈.这是由于激振频率接近排肥导管的固有频率所引起的,说明v=0.5m/s时排肥导管的振动频率最接近于排肥导管的固有频率.扶手的固有频率在5.01～17.69Hz、v=1.5m/s、激振频率为6.80Hz时,振动最激烈,说明此速度下扶手的振动频率最接近扶手固有频率.

烟叶含水量的快速检测在烟草种植业中起着关键的作用,检测采摘期烟叶水分含量,对烟草工艺具有重要意义.为了快速、无损地检测采摘期烟叶水分含量,提出一种主成分分析(PCA)结合马氏距离算法(MD)的方法来剔除异常样本,再使用偏最小二乘法(PLS)估测采摘期烟叶水分含量.首先,利用GaiaSky-mini2机载高光谱成像仪获取到141个采摘期烟叶的高光谱数据,采用多元散射校正(MSC)、标准正态变量交换(SNV)和Savitzky-Golay卷积平滑法等对原始光谱进行预处理.然后,应用主成分分析结合马氏距离法对校正集中的异常样品进行剔除.最后,使用偏最小二乘法(PLS)建立采摘期烟叶水分含量分析模型.结果 表明:利用SG卷积平滑法预处理的PCA-MD-PLS模型效果最佳,对烟叶含水量预测能力最好,预测模型相关系数为0.8527,均方差为1.3766.