Identification and documentation of wild plant species with ornamental potentials at Mount Prau, Central Java, Indonesia

Indonesia has diverse flora and fauna, and many species remain largely undiscovered. Documentation and identification of threatened wild ornamental species are increasingly difficult due to ongoing exploitation and land conversion. Mount Prau is one of the popular destinations in Central Java, Indonesia for tourism. Understanding plant biodiversity has enormous value for the economy, ecology, culture, science, and recreation. Our study is aimed to record the diversity and identify the flowering plant species in their native habitat at Mount Prau, Central Java, Indonesia. Our field surveys demonstrated that Mount Prau has abundant wild ornamental plants with wide diversity of taxa, growth habits, and forms. A total of 103 species representing 51 families and 95 genera are identified including trees, shrubs, herbs, and lianas. We also found that among the plant species found in Mount Prau, 24 have morphological characters suitable to be cultivated as ornamental flowers, and 12 as ornamental foliages, and 63 species are medicinal plants. The ornamental criteria of these species were based on the literature describing the morphological and unique characters of leaves and flowers that made them potential to be developed as ornamental plants. In this paper we have provided the current conservation status of the plant species identified and recommendations on their conservation. This study provides baseline data of species found in the Mount Prau areas, and this information could be helpful for further conservations efforts and initiatives.

Identification and documentation of wild plant species with ornamental potentials at Mount Prau, Central Java, Indonesia

Indonesia has diverse flora and fauna, and many species remain largely undiscovered. Documentation and identification of threatened wild ornamental species are increasingly difficult due to ongoing exploitation and land conversion. Mount Prau is one of the popular destinations in Central Java, Indonesia for tourism. Understanding plant biodiversity has enormous value for the economy, ecology, culture, science, and recreation. Our study is aimed to record the diversity and identify the flowering plant species in their native habitat at Mount Prau, Central Java, Indonesia. Our field surveys demonstrated that Mount Prau has abundant wild ornamental plants with wide diversity of taxa, growth habits, and forms. A total of 103 species representing 51 families and 95 genera are identified including trees, shrubs, herbs, and lianas. We also found that among the plant species found in Mount Prau, 24 have morphological characters suitable to be cultivated as ornamental flowers, and 12 as ornamental foliages, and 63 species are medicinal plants. The ornamental criteria of these species were based on the literature describing the morphological and unique characters of leaves and flowers that made them potential to be developed as ornamental plants. In this paper we have provided the current conservation status of the plant species identified and recommendations on their conservation. This study provides baseline data of species found in the Mount Prau areas, and this information could be helpful for further conservations efforts and initiatives.

Impact Analysis of Highway System on Wetland Vegetation in Northern China

Wetlands in northern China are complex ecosystems composed of grasslands, lakes, rivers and swamps, which have immense ecological values. When a highway system passes through a wetland, it has adverse effects on its ecosystem. However, in many cases, it is difficult to avoid a highway system pass through a wetland. Taking the Erka wetland in northern China as an example, nine survey lines, perpendicular to the highway, were set up. According to the distance from the highway, the plant multi-element information was collected. After the analysis of plant growth habits, spatial characteristics and profile features, the following four conclusions were drawn: (1) the highway system divided the plants habitat and made the vegetation communities on both sides develop anisotropically; (2) the highway system interfered with the interspecific competition of the nearby plant populations, making it easier for the plant communities with fast propagation speed, drought resistance and anti-interference to establish advantages; (3) the plant growth within 80 m of the highway was inhibited and (4) the wetland plant community succeeded to grassland plant community. In order to reduce the adverse impact of highway system on wetland ecosystems, it is suggested that in the follow-up highway upgrading project, either diversion of highway or construction of bridge or culvert excavation should be considered.

CNN for Image Processing to Detect Weeds Using IOT

One of the major issues in today’s agriculture fields is detecting weed plants in between the crops. Weeds consume more water, nutrients, and light compared to crop plants. Being hardy and vigorous in growth habits, they grow way to faster than crops and consume a huge amount of water and nutrients, results causing heavy losses in yields, the process of removal of weeds manually is a difficult job and it requires more manpower. To date, weed removal can’t be automated without manpower. Herbicides play a crucial role in removing the weeds but that leads to soil infertile and later the weeds dominate the field automatically. In solution to reduce the weeds is using herbicide in a higher amount than normal day by day. Usage of herbicides in that amount causes the land infertile. This paper deals with detecting the weeds in the crop using a convolutional neural network, Image processing, and IoT. The weeds in the field and between the crops are detected and removed by using the image processing technique. CNN algorithm is implemented in Matlab software to detect the weed areas in the fields. A robot model is connected to the controller through the motor driver which is also used to carry the camera through the field to detect the weed. The videos and images taken by the camera send to the Matlab and they are trained by using the CNN algorithm and that classifies whether it is a weed or a normal crop. And the necessary instructions send to the Arduino through Zigbee. If the camera detects any weed then the cutter is on 10 seconds to cut the weeds. And the robot model moves further until it finds the next weed. Users can also control the robot model whenever itneeds. Keywords: CNN; Weed cutter; Matlab; Zigbee; Image processing.

Adaptive Performance of Five Varieties of Hybrid Citrus in Jingzhou Area Hubei Province

In order to understand the growth habits and freeze resistance of the hybrid citrus variety in Jingzhou area, and to provide reference for the introduction of the variety in fruit farmers, and to promote the development of the local citrus industry, the main biological and freeze resistance characteristics of the five hybrid citrus variety were investigated. The results showed that:(1)The growth potential of ‘Wo gan’ and ‘Daya’ were the strongest, followed by ‘Aiyuan 38’ and ‘Chunjian’, and the weakest growth was ‘Buzhihuo’;(2) On the performance of summer and autumn shoots, ‘Daya’ and ‘Wogan’ were the longest, ‘Chunjian’ was moderate, ‘Aiyuan 38’ and ‘Buzhihuo’ were the weakest; (3) On the aspect of freeze resistance, ‘Aiyuan 38’was the strongest, followed by ‘Daya’ and ‘Wogan’, the freeze resistance of ‘Chunjian’ was more weak and ‘Buzhihuo’ was the weakest;(4) On the other aspects, such as the leaf shape, leaf size, color of tender leaf, thorn density, flower size, inflorescence and so on also had the big difference, but the petal number and the petal color were similar. It could be seen that ‘Daya’ and ‘Wogan’ had the best growth potential in Jingzhou area, but their freeze resistance were weak. The growth potential of ‘Aiyuan 38’ and ‘Chunjian’ were in the middle, ‘Aiyuan 38’ had best freeze resistance. The growth potential and freeze resistance of ’Buzhihuo’ in Jinzhou area was the weakest.

Ground Cover—Biomass Functions for Early-Seral Vegetation

Vegetation biomass is commonly measured through destructive sampling, but this method is time-consuming and is not applicable for certain studies. Therefore, it is necessary to find reliable methods to estimate vegetation biomass indirectly. Quantification of early-seral vegetation biomass in reforested stands in the United States Pacific Northwest (PNW) is important as competition between the vegetation community and planted conifer seedlings can have important consequences on seedling performance. The goal of this study was to develop models to indirectly estimate early-seral vegetation biomass using vegetation cover, height, or a combination of the two for different growth habits (ferns, forbs, graminoids, brambles, and shrubs) and environments (wet and dry) in reforested timber stands in Western Oregon, USA. Six different linear and non-linear regression models were tested using cover or the product of cover and height as the only predicting variable, and two additional models tested the use of cover and height as independent variables. The models were developed for six different growth habits and two different environments. Generalized models tested the combination of all growth habits (total) and sites (pooled data set). Power models were used to estimate early-seral vegetation biomass for most of the growth habits, at both sites, and for the pooled data set. Furthermore, when power models were preferred, most of the growth habits used vegetation cover and height separately as predicting variables. Selecting generalized models for predicting early-seral vegetation biomass across different growth habits and environments is a good option and does not involve an important trade-off by losing accuracy and/or precision. The presented models offer an efficient and non-destructive method for foresters and scientists to estimate vegetation biomass from simple field or aerial measurement of cover and height. Depending on the objectives and availability of input data, users may select which model to apply.

Screening Cotoneaster sp. for Resistance to Fire Blight Using Foliar Inoculation with Two Strains of Erwinia amylovora

The genus Cotoneaster is composed of ≈400 species with a wide variety of growth habits and forms. These hardy landscape shrubs used to be commonplace because of their low maintenance and landscape functionality. However, the interest in and sales of cotoneaster have decreased for a variety of reasons, with the greatest being its susceptibility to a bacterial disease fire blight caused by Erwinia amylovora. The resistances of 15 different genotypes of Cotoneaster to a wild-type strain of Erwinia amylovora (Ea153) and a strain LA635 that has a natural mutation in avrRpt2 that encodes for a type III secretion effector were tested separately by inoculating leaves. Fire blight resistance was assessed by calculating the percent shoot necrosis (PSN) [PSN = 100 × (lesion length ÷ total branch length)] at 6 to 8 weeks after inoculation. Across all experiments, Cotoneaster genotypes H2011-01-002 and C. ×suecicus ‘Emerald Sprite’ consistently had the lowest PSN values when inoculated with either strain. Cotoneaster ×suecicus ‘Emerald Beauty’ was significantly more resistant to Ea153 than to LA635, whereas C. splendens was significantly more susceptible to Ea153 than to LA635.