Quality Of Natural Rubber Research Articles

Rubber Leaf Disease Recognition Based on Improved Deep Convolutional Neural Networks With a Cross-Scale Attention Mechanism.

Natural rubber is an essential raw material for industrial products and plays an important role in social development. A variety of diseases can affect the growth of rubber trees, reducing the production and quality of natural rubber. Therefore, it is of great significance to automatically identify rubber leaf disease. However, in practice, different diseases have complex morphological characteristics of spots and symptoms at different stages and scales, and there are subtle interclass differences and large intraclass variation between the symptoms of diseases. To tackle these challenges, a group multi-scale attention network (GMA-Net) was proposed for rubber leaf disease image recognition. The key idea of our method is to develop a group multi-scale dilated convolution (GMDC) module for multi-scale feature extraction as well as a cross-scale attention feature fusion (CAFF) module for multi-scale attention feature fusion. Specifically, the model uses a group convolution structure to reduce model parameters and provide multiple branches and then embeds multiple dilated convolutions to improve the model’s adaptability to the scale variability of disease spots. Furthermore, the CAFF module is further designed to drive the network to learn the attentional features of multi-scale diseases and strengthen the disease features fusion at different scales. In this article, a dataset of rubber leaf diseases was constructed, including 2,788 images of four rubber leaf diseases and healthy leaves. Experimental results show that the accuracy of the model is 98.06%, which was better than other state-of-the-art approaches. Moreover, the model parameters of GMA-Net are only 0.65 M, and the model size is only 5.62 MB. Compared with MobileNetV1, V2, and ShuffleNetV1, V2 lightweight models, the model parameters and size are reduced by more than half, but the recognition accuracy is also improved by 3.86–6.1%. In addition, to verify the robustness of this model, we have also verified it on the PlantVillage public dataset. The experimental results show that the recognition accuracy of our proposed model is 99.43% on the PlantVillage dataset, which is also better than other state-of-the-art approaches. The effectiveness of the proposed method is verified, and it can be used for plant disease recognition.

A causal sustainable natural rubber development framework using a hierarchical structure with linguistic preferences in Thailand

Natural rubber development is facing sustainability challenges, such as price volatility, human rights violations, deforestation, and biodiversity loss. There is a lack of valid measurement attributes to assess sustainable natural rubber development. This study contributes to the literature by providing a set of sustainable natural rubber development attributes and determining a model of their causal interrelationships based on qualitative information. A mixed-method approach comprising the fuzzy Delphi method and the fuzzy decision-making trial and evaluation technique is used to validate and map the attributes in the model. The findings indicate that technical support has the most significant influence on preventing the economic and social risks involved in natural rubber development. Furthermore, environmental sustainability and economic sustainability interact, and one cannot be achieved without the other. Promoting value addition, natural rubber quality, capital investment, marketing platforms, alternative income sources, attitudes towards change, and material consumption are significant criteria identified as priorities to enhance smallholder producers’ sustainability. Theoretical and managerial implications are provided.

Genetic diversity associated with natural rubber quality in elite genotypes of the rubber tree

The objective of this study was to evaluate the genetic variability of natural rubber latex traits among 44 elite genotypes of the rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Müell. Arg.]. Multivariate analysis and machine learning techniques were used, targeting the selection of parents that demonstrate superior characters. We analyzed traits related to technological or physicochemical properties of natural rubber latex, such as Wallace plasticity (P0), the plasticity retention index [PRI (%)], Mooney viscosity (VR), ash percentage (Ash), acetone extract percentage (AE), and nitrogen percentage (N), to study genetic diversity. Multivariate [unweighted pair group method with arithmetic means (UPGMA) and Tocher)] and machine learning techniques [K-means and Kohonen’s self-organizing maps (SOMs)] were employed. The genotypes showed high genetic variability for some of the evaluated traits. The traits PRI, Ash, and PO contributed the most to genetic diversity. The genotypes were classified into six clusters by the UPGMA method, and the results were consistent with the Tocher, K-means and SOM results. PRI can be used to improve the industrial potential of clones. The clones IAC 418 and PB 326 were the most divergent, followed by IAC 404 and IAC 56. These genotypes and others from the IAC 500 and 400 series could be used to start a breeding program. These combinations offer greater heterotic potential than the others, which can be used to improve components of rubber latex quality. Thus, it is important to consider the quality of rubber latex in the early stage of breeding programs.

ANALISIS KETERKAITAN PENDAPATAN DENGAN MUTU BOKAR YANG DIHASILKAN PETANI KARET RAKYAT DI PROVINSI JAMBI


 
 
 ome research showed that the quality of natural rubber produced by smallholder in Jambi Province is getting worst, while the international rubber market on the other hand needs higher quality. This research is aimed to find out whether the natural rubber price received by farmer is linked to the rubber quality marketed in Jambi Province. The research was undertaken in four regency main rubber producing samples. Data was both primary and secondary data. The research found that the natural rubber was marketed in monopolistic manner, in which the natural rubber price was mainly controlled by trader. Majority (81,33 %) smallholder farmer sold their natural rubber to certain local trader. The natural rubber quality was range from low (50.09 %) to medium (76,14 %) grade, with the average grade was 62,27 %. Furthermore, the research was also showed that the price ranges from Rp. 5000/Kg to Rp. 10.800/Kg and the average price was Rp. 7.499,02/Kg. The research was also found that there is a weak correlation between price paid to the farmer and the natural rubber quality sold by the farmer in Jambi Province. 
 
 

Study on Natural Rubber Quality of Micro-Cut Tapping with Gas-Stimulation

The natural rubber samples produced by conventional tapping methods were used as a control, then analyzed the dry rubber property of natural rubber, the dynamic property and Physical & mechanical property of vulcanized rubber using Rubber processing analyzer and Thermogravimetric Analyze the difference between the properties of dry rubber and vulcanized rubber produced by Micro-cut Tapping with Gas-stimulation. The results indicated that the tensile strength, tear strength elastic modulus of dry rubber that produced by Micro-cut Tapping with Gas-stimulation are respectively 20%, 7% lower than the control. The protein content of dry rubber materials produced by Micro-cut Tapping with Gas-stimulation is 16.7% higher than control group.

Some Factors Affecting the Interpretation of the Plasticity Retention Index—P.R.I.

Abstract The implications of the PRI test are examined, in order to demonstrate how the values may be interpreted by the rubber goods manufacturer. It is shown that consistency of PRI is desirable, to enable product uniformity through control of processing, in particular high temperature mastication. It is demonstrated that PRI cannot be used to assess the quality of natural rubber, in terms of dynamic properties and vulcanizate aging.