Skrining Fitokimia dan Uji Aktivitas Antibakteri Fraksi Etil Asetat dan n-Heksana dari Daun Mangga Kasturi (Mangifera casturi Kosterm.)

Kasturi mango (Mangifera casturi Kosterm.) is a typical mango of South Kalimantan. Kasturi mango leaves are reported to have antioxidant activity and are potential for treating various diseases, including diseases related to antibacterial. This study examines the antibacterial activity of ethyl acetate fraction and the n-hexane fraction of mango musk leaves on bacteria that cause acne. Experimental research started with plant determination, making extracts and fractions, phytochemical screening, and antibacterial testing against Staphylococcus aureus and Propionibacterium acnes using the disk diffusion method. The study results found that the ethyl acetate and n-hexane fractions had weak antibacterial activity against S. aureus and P. acnes bacteria, which cause acne

Harumanis Mango Leaves Image Segmentation based on Wavelet Transformation with Phansalkar and Sauvola Thresholding

Harumanis mango is one of the economic sources of the Perlis state. It has a sweeter taste compared to other mangoes. However, the Harumanis mango tree required specific weather, soil nutrient contents and pH level. This makes the farmer does not know the health condition of their Harumanis mango tree. Therefore, this project aims to provide the best method of leaves detection to the farmer. The leaves image samples are collecting from the orchard and undergo pre-processing. Then the input image was converted into grayscale with principal component analysis (PCA). Wavelet transformation was implemented to increase the discriminability of the segmentation technique for separating the leaf and background. The leaf segmentation is done by using Phansalkar and Sauvola thresholding techniques. After that, fill hole and area opening techniques are implementing to reduce noise in the image. These two thresholding techniques are comparing and discuss with their segmentation performance. Overall, Phansalkar thresholding has produced better performance in segmenting healthy and unhealthy Harumanis mango leaves with sensitivity, specificity and accuracy of 92.05%, 81.37% and 83.51%, respectively.

Harumanis Mango Leaves Image Segmentation on RGB and HSV Colour Spaces using Fast k-Means Clustering

Harumanis mango is the signature fruit in Perlis due to its delicious taste and its sweet-smelling. A good quality Harumanis tree requires rich in nutrition (healthy), and the tree will grow lots of fruits compared to the trees which are poor in nutrition (unhealthy). The health condition of a tree can be observed through the leaves in term of shape of leaves. For a healthy Harumanis tree, the leaves grow in scattering shapes. Meanwhile, an unhealthy Harumanis tree grows in gathered shapes. Therefore, this research is focusing on Harumanis mango leaves image segmentation by comparing between RGB and HSV colour spaces in order to obtain the best segmentation performance. 100 of Harumanis mango tree leaves images are used in this research. These images have undergo through image pre-processing such as modified linear contrast stretching and colour components extraction based on RGB and HSV colour spaces. Then, the colour component images have been segmented by using fast k-means clustering in order to obtain the leaves segmented images. Finally, quantitative analyses have been performed to measure the segmentation performance based on sensitivity, specificity and accuracy. Overall, the results show that S component of HSV colour space archives the highest accuracy with 85.81%.

Intelligent system for leaf disease detection using capsule networks for horticulture

Horticulture crops take a crucial part of the Indian economy by creating employment, supplying raw materials to different food processing industries. Mangoes are one of the major crops in horticulture. General Infections in Mango trees are common by various climatic and fungal infections, which became a cause for reducing the quality and quantity of the mangos. The most common diseases with bacterial infection are anthracnose and Powdery Mildew. In recent years, it has been perceived that different variants of deep learning architectures are proposed for detecting and classifying the problems in the agricultural domain. The Convolutional Neural Network (CNN) based architectures have performed amazingly well for disease detection in plants but at the same time lacks rotational or spatial invariance. A relatively new neural organization called Capsule Network (CapsNet) addresses these limitations of CNN architectures. Hence, in this work, a variant of CapsNet called Multilevel CapsNet is introduced to characterize the mango leaves tainted by the anthracnose and powdery mildew diseases. The proposed architecture of this work is validated on a dataset of mango leaves collected in the natural environment. The dataset comprises both healthy and contaminated leaf pictures. The test results approved the undeniable level of exactness of the proposed framework for the characterization of mango leaf diseases with an accuracy of 98.5%. The outcomes conceive the higher-order precision of the proposed Multi-level CapsNet model when contrasted with the other classification algorithms such as Support Vector Machine (SVM) and CNNs.

Assessment of Anti-Tyrosinase, Anti-Elastase and Anti-Acetylcholinesterase Properties of Fermented Mango Leaves at Different Maturity Level

Mango leaves are known to possess many health benefits but the industry only focused on mango fruit production, resulting in abundant leaves being underutilized. In this study, we managed to transform mango leaves into a new fermented drink, which has a pleasant taste through the bio-fermentation process. Different maturity levels of mango leaves were selected; premature leaves (light brown, LBML), intermediate mature leaves (light green, LGML) and mature leaves (green, ML), which were subjected to a fermentation process using bacteria and yeast. Tannin content, organic acids profile and various enzymes functionality activities (e.g. inhibition of tyrosinase, elastase and acetylcholinesterase) studies were determined on fermented mango leaves drink. The reduction of tannins content in all fermented mango leaves resulted in a less astringent taste as a consequence of the microbial action to break down tannins. Acetic, oxalic, kojic and quinic acid are some of the organic acids detected in fermented mango leaves that contributed to its slightly acidic taste. In comparison to non-fermented mango leaves, all fermented samples, particularly LBML drink showed a significant improvement (P<0.05) in tyrosinase inhibition (87.96%). Fermented mango leaves also exhibited good inhibition activity towards elastase (>80%) and acetylcholinesterase (>90%). Further histopathology examination on various rat’s organs (kidney, liver, spleen, and stomach) showed no sign of inflammation symptoms. Through limit toxicological evaluation, the safety consumption rate (IC50 value) for fermented mango leaves was 1000 mL/50 kg of human bodyweight. The improvement functionality activities of fermented mango leaves with a higher inhibition rate against tyrosinase, elastase, and acetylcholinesterase indicate its great potential as a food remedy for anti-ageing treatment.

Recognition of mango leaf disease using convolutional neural network models: a transfer learning approach

<p>The acknowledgment of plant diseases assumes an indispensable part in taking infectious prevention measures to improve the quality and amount of harvest yield. Mechanization of plant diseases is a lot advantageous as it decreases the checking work in an enormous cultivated area where mango is planted to a huge extend. Leaves being the food hotspot for plants, the early and precise recognition of leaf diseases is significant. This work focused on grouping and distinguishing the diseases of mango leaves through the process of CNN. DenseNet201, InceptionResNetV2, InceptionV3, ResNet50, ResNet152V2, and Xception all these models of CNN with transfer learning techniques are used here for getting better accuracy from the targeted data set. Image acquisition, image segmentation, and features extraction are the steps involved in disease detection. Different kinds of leaf diseases which are considered as the class for this work such as anthracnose, gall machi, powdery mildew, red rust are used in the dataset consisting of 1500 images of diseased and also healthy mango leaves image data another class is also added in the dataset. We have also evaluated the overall performance matrices and found that the DenseNet201 outperforms by obtaining the highest accuracy as 98.00% than other models.</p>

Spectral denoising in hyperspectral imaging using the discrete wavelet transform

The use of hyperspectral sensors has gained relevance in agriculture due to its potential in the phytosanitary management of crops. However, these sensors are sensitive to spectral noise, which makes their real application difficult. Therefore, this work focused on the analysis of the spectral noise present in a bank of 180 hyperspectral images of mango leaves acquired in the laboratory, and the implementation of a denoising technique based on the discrete wavelet transform. The noise analysis consisted in the identification of the highest noisy bands, while the performance of the technique was based on the PSNR and SNR metrics. As a result, it was determined that the spectral noise was present at the ends of the spectrum (417-421nm and 969-994nm) and that the Neigh-Shrink method achieved a SNR of the order of 1011 with respect to the order of 102 of the original spectrum.