棉田土壤养分分析与土壤墒情监测系统应用

【目的】研究土壤养分的空间变异程度及分布规律,为该区域科学施肥提供依据。【方法】以新疆生产建设兵团第八师石河子总场六分场数字农业示范田为研究区域,应用土壤墒情监测系统、GIS与地统计学的方法,对棉田土壤含水量与温度进行实时采集、分析并存储在服务器里面,分析石河子总场土壤含水量和温度变化规律、棉田土壤养分空间分布特点及变异规律。【结果】(1)根据监测数据分析,随着灌水量增加和棉花生育期推进,上层0~30 cm比下层40~60 cm的土壤含水量变化趋势明显。0~20 cm土层土壤补偿水比较充分,各个监测点土壤含水量基本维持在比较适宜的范围内。土壤各层温度受大气温度影响并随着土层深度的加深而减弱,随着土层深度的逐渐加深滞后时间相对延长;受棉株逐渐长高变大以后遮阴等造成的影响,7月以后各土层温度逐渐持平,波动不大。(2)土壤全氮、速效磷和速效钾均呈现出中等程度变异;(3)土壤速效钾的块金值在25%~75%(块金值为0.497)表现为中等空间自相关性外,土壤全氮、速效磷指标的块金系数小于25%表现为强烈的空间自相关性。【结论】应用土壤墒情实时监测系统指导棉田灌溉,较往年没有任何减产减质的情况下,棉花灌溉在全生育期内比以往灌溉次数下降了1~3次,节约水资源约20%左右。研究区域内土壤全氮、速效磷和速效钾变异呈现中等程度变异特征,全氮、速效磷表现为极强空间自相关性,速效钾表现为中等强度的空间相关性。     

安徽沿江地区油麦后直播棉的品种选用策略

在目前安徽沿江地区不能实现棉花机采的情况下,提出了“植株高度既适于机采也适于人工采收,含絮力适中(不易落花便于集中采收),生育期适中(播种到采收为180天左右),纤维品质及产量性状较优(纤维长度30.0 mm以上,断裂比强度30.0 cN/tex以上,马克隆值5.0以下,籽棉产量3750 kg/hm 2以上,衣分在40%左右)”的油(或麦)后直播棉模式的人工采收品种选用策略。旨在促进安徽沿江地区棉花生产稳定发展。     

化学打顶对阿拉尔垦区主栽品种光合冠层的影响

化学打顶是指利用植物生长调节剂来打破作物顶端优势,进而保障作物的正常生长和生产。本文为探究化学打顶对作物的影响,利用摇钱素和棉花打顶剂处理新陆中70和新陆中82不同品种,同时测定不同化学打顶剂处理条件下,棉花的净光合速率、气孔导度、蒸腾速率、胞间CO₂浓度及棉花冠层叶面积指数指标。研究发现：不同化学打顶处理条件下,与人工打顶相比化学打顶的棉花的五种指标变化均不明显,但新陆中70和新陆中82品种的净光合速率平均值与对照相比分别高出3 μmol/(m2.s)和2 μmol/(m2.s),且冠层叶面积指数值比人工打顶的略高。试验结果表明化学打顶处理后没有造成棉花疯长,提高了光能利用率,进而增加作物群体的光合作用。     

基于迁移学习的GoogLeNet果园病虫害识别与分级

为提高果树病虫害危害程度分级精度进而更好地指导果园病虫害防治，采用迁移学习技术与GoogLeNet模型相结合的方法，对6种果园作物的25类病虫害样本进行识别与危害程度分级研究；同时，探究不同数据集大小以及不同优化算法对模型性能的影响；基于MATLAB平台设计了一款可视化的病虫害识别与分级系统。结果表明:1)基于迁移学习的GoogLeNet模型，对病虫害识别精度可达99.35%，危害程度分级精度可达92.78%；2)在相同训练参数下，本研究模型比AlexNet、VGG-16、ResNet-18、SqueezeNet、原GoogLeNet及MobileNet-v2模型验证精度提高了2.38%~11.44%，并且收敛速度最快；3)本研究模型识别精度随着数据集的增大而提高；在3种优化算法中SGDM算法耗时最短且精度最高，更适合本研究模型。通过拍摄果树叶片病害区域图像，本研究设计的系统能够在0.43 s左右准确识别出果树种类、病害类型以及危害等级等信息。     

陇南核桃病虫害无公害综合防治策略

针对陇南核桃栽培中病虫害发生问题进行分析论述,提出无公害综合防治策略,对陇南生态环境保护及绿色无公害核桃生产有一定指导意义。     

茶树植保机械及减量施药技术研究进展

农药防治是茶树病虫害综合防治的重要组成部分,其在病虫害突发或爆发时具有快速高效的防治优势。茶树叶片表面具有亲水性,常量施药会造成茶叶农残超标、生态环境破坏等问题,实现茶树减量施药是减少茶叶农残的有效手段。系统综述了茶树生物特性、茶树病虫害预测诊断及防治方法、茶树植保机械及施药技术,强调提高茶树低容量喷雾的农药有效利用率是实现茶树减量施药的关键。针对目前茶园地面工况复杂及农药利用率低的问题,本文从低容量仿形喷雾机、茶树病虫害喷雾决策及智能终端等六个方面提出茶树病虫害施药技术及装备的研究建议,指出低量化、精准化及智能化是未来茶树植保喷雾机械及施药技术的发展方向。     

南充地区晚熟柑桔主要病虫害发生特点及综合防控措施

本文介绍了南充地区晚熟柑桔主要病虫害的种类及为害发生特点,并提出了相应的病虫害综合防控措施,保证晚熟柑桔的正常生长,以期为当地晚熟柑桔生产、种植户节本增收提供参考。     

Relationships Among the Feeding Behaviors of a Mirid Bug on Cotton Leaves of Different Ages and Plant Biochemical Substances

Apolygus lucorum (Meyer-Dür) (Hemiptera: Miridae) tends to feed on young plant tissues. To explore the relationship between stylet probing behaviors of adult A. lucorum and conditions of cotton leaves, we conducted an experiment using electropenetrography (EPG). Behaviors were recorded on four cotton varieties, in relation to thickness and biochemical traits of differently-aged leaves. Cotton leaf age had a significant effect on the probing behavior of A. lucorum but cotton variety did not. One-day-old leaves of A. lucorum received the highest mean number of stylet probes (penetrations) per insect, and longest mean durations per insect of combined stylet probing or its components, cell rupture and ingestion behaviors. All of the leaf traits (thickness and biochemical substances) were similar among these four cotton varieties. Leaf thickness had a significantly negative effect on the same four variables above. Gossypol and tannin also had a negative impact on combined probing duration. Redundancy analysis showed that the four EPG variables were closely related to nutrient substances (amino acids, sugar, and water) while they had the opposite relationship with plant defense substances (gossypol and tannin). On cotton in the seedling stages, A. lucorum fed more readily on the youngest, thinnest leaves in our no-choice EPG experiments. Nutrients and chemical resistance substances determined the probing duration of A. lucorum. Our findings can contribute to better understanding of patterns of feeding and host consumption by A. lucorum, ultimately improving cotton resistance to A. lucorum.     

Cotton Image Segmentation Algorithm Based on Fusion Method of Markov Random Field and Quantum Particle Swarm Cluster

[Objective] The aim of this study was to improve the cotton image segmentation accuracy in a picking robot image processing system. [Method] An image segmentation algorithm based on a fusion method of Markov random field and quantum particle swarm optimization clustering was proposed. The process of the proposed algorithm is as follows: first, transform the RGB (red, green, blue) images into grayscale; second, use it to segment these images; finally, the threshold of the connected area is set on the basis of the segmented image to obtain the target area. Then, the cotton front image and the cotton side image are selected from the images collected from different angles. The segmentation experiment was carried out by using this algorithm, and compared with the Otsu algorithm, the fuzzy C-means algorithm, the quantum particle swarm image segmentation algorithm and the Markov random field image segmentation algorithm. [Result] The results showed that the segmentation accuracy and peak signal to noise ratio of the proposed algorithm were 98.94% and 77.48 dB. When compared with the Otsu algorithm, fuzzy C-means algorithm, quantum particle swarm optimization algorithm and Markov random field algorithm, the average segmentation accuracy and peak signal to noise ratio of the proposed algorithm increased by 2.47%–4.56%, and 9.81–13.11 dB, respectively. [Conclusion] The proposed algorithm had higher segmentation accuracy and higher peak signal to noise ratio than the other algorithms tested.     

SSCP Marker Development and Analysis of Candidate Restorer Gene Rf1 for Cotton Cytoplasmic Male Sterility

[Objective] Locating the cotton cytoplasmic male sterility (CMS) restorer gene Rf₁ is important for investigating restorer gene mechanisms and improving restorer lines. In our previous study, a gene cluster, with nine Pentatricopeptide repeat(PPR) genes and nine other genes, was found within the 160-kb Rf₁ target region in Scaffold 333. The objective here was to improve the density of Rf1-linked markers in the target region and determine the expression profiles of candidate genes. [Method] Using the sequences of the 18 genes, we designed 155 single-strand conformation polymorphism (SSCP) primers covering all of the gene sequences to identify the polymorphic SSCP markers between the fertile and sterile pools. Additionally, real-time polymerase chain reaction(PCR) was performed to analyze the expression profiles of eight candidate genes in the four developmental stages of buds of sterile, maintainer and restoring lines, respectively. [Result] In total, 15 polymorphic primers were identified. A genotype analysis of the F₂ population was conducted using the 15 primers and 3 other polymorphic simple sequence repeats (SSR) markers. The markers were distributed in a 4.8 cM range. In addition, owing to the influence of sterile cytoplasm or restorer genes, most of the genes showed different expression patterns in the four developmental stages of the three lines' buds. [Conclusion] SSCP markers tightly linked to Rf₁ were identified and the expression profiles of candidate genes were determined. This study provides a basis for the further fine mapping of restorer genes and for candidate gene screening.