Dataset Information

MangoLeafBD: A comprehensive image dataset to classify diseased and healthy mango leaves.

ABSTRACT: Agriculture is one of the few remaining sectors that is yet to receive proper attention from the machine learning community. The importance of datasets in the machine learning discipline cannot be overemphasized. The lack of standard and publicly available datasets related to agriculture impedes practitioners of this discipline to harness the full benefit of these powerful computational predictive tools and techniques. To improve this scenario, we develop, to the best of our knowledge, the first-ever standard, ready-to-use, and publicly available dataset of mango leaves. The images are collected from four mango orchards of Bangladesh, one of the top mango-growing countries of the world. The dataset contains 4000 images of about 1800 distinct leaves covering seven diseases. Although the dataset is developed using mango leaves of Bangladesh only, since we deal with diseases that are common across many countries, this dataset is likely to be applicable to identify mango diseases in other countries as well, thereby boosting mango yield. This dataset is expected to draw wide attention from machine learning researchers and practitioners in the field of automated agriculture.

SUBMITTER: Ahmed SI

PROVIDER: S-EPMC9932726 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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MangoLeafBD: A comprehensive image dataset to classify diseased and healthy mango leaves.

Ahmed Sarder Iftekhar SI Ibrahim Muhammad M Nadim Md M Rahman Md Mizanur MM Shejunti Maria Mehjabin MM Jabid Taskeed T Ali Md Sawkat MS

Data in brief 20230130

Agriculture is one of the few remaining sectors that is yet to receive proper attention from the machine learning community. The importance of datasets in the machine learning discipline cannot be overemphasized. The lack of standard and publicly available datasets related to agriculture impedes practitioners of this discipline to harness the full benefit of these powerful computational predictive tools and techniques. To improve this scenario, we develop, to the best of our knowledge, the first ...[more]

PMID: 36819904

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Project description:Agriculture is the art and science of cultivating the soil, growing crops and raising livestock. Enhancing crop yield becomes essential to improve economy in agriculture sector. Crops need 16 essential nutrients in balanced factor for their proper growth. Deficiency among these essential nutrients causes stunted growth often leading to significant crop loss. Symptoms of nutrient deficiency in plants can be observed visually which needs to be diagnosed correctly to rectify the problem, so that plants can grow healthily, increasing its yield. Among various crops, banana is one of the staple foods for millions of people across country and world. They contain essential nutrients that can have a defending impact on health of the human beings. The year-round availability, affordability, varietal range, taste, nutritive of banana and medicinal value makes it the favorite fruit among all classes of people. In addition, it also has good export potential. Few of the symptoms due to nutrient's deficiency in banana leaves are like curling of leaves, appearance of yellow strips, yellowing of the leaves, bluish color of young leaves. Deficiency symptoms can be visualized prominently on the leaves of the plant. Further, Machine Learning models can be developed to detect nutrient deficiency in leaves and help farmers in taking relevant measures. Thus, a fully labelled dataset becomes essential to train and test these models to detect nutrient deficiency accurately. The dataset created consists of banana leaf images of various categories like Musa acuminata (Dwarf Cavendish), Robusta, Rasthali, Poovan, Monthan, Elakkibale. Images depict deficiency in eight class of nutrients: boron, calcium, iron, potassium, manganese, magnesium, sulphur and zinc. Table 1 summarizes the essential nutrients and their deficiency symptoms visible on the leaves. Dataset also contains images of healthy leaves. Machine Learning Models can be developed by researchers and students and train them by using the created banana dataset to obtain high accuracy.

Project description:ObjectiveThe purpose of this systematic bioinformatics analysis was to describe the compositions and differences in submucosal microbial profiles of peri-implants' diseases and healthy implant.Material and methodsPubMed, Embase, ETH Z, Scopus, CNKI, and Wanfang databases were searched to screen relevant literature on the analysis of peri-implant microflora based on the sequencing analysis technique of 16S ribosomal RNA (16S rRNA) gene. High-throughput sequencing of the 16S rRNA gene of microorganisms from healthy implants, peri-implant mucositis, and peri-implantitis was downloaded from the screened articles. EasyAmplicon and Usearch global algorithm were used to match the reads from each dataset to a full length of 16S rRNA or ITS gene sequence. The microorganisms based on the Human Oral Microbiome Database (HOMD) were re-classified, and the microbial diversity, flora composition, and differential species of the samples were re-analyzed, including taxonomic classification and alpha and beta diversity calculations. The co-occurrence network was also re-analyzed.ResultsA total of seven articles with 240 implants were included. Among them, 51 were healthy implants (HI), 43 were in the peri-implant mucositis (PM) group, and 146 were in the peri-implantitis (PI) group. A total of 26,483 OTUs were obtained, and 877 microorganisms were annotated. The alpha diversity including Chao1 (healthy implants, 121.04 ± 92.76; peri-implant mucositis, 128.21 ± 66.77; peri-implantitis, 131.15 ± 84.69) and Shannon (healthy implants, 3.25 ± 0.65; peri-implant mucositis, 3.73 ± 0.61; peri-implantitis, 3.53 ± 0.67) of the samples from the three groups showed a significant difference. The beta diversity of the three samples was statistically different among groups. The genera of Treponema and Fretibacterium were significantly more abundant in the PI group than in the other two groups, and the genus of Streptococcus was more abundant in the HI group. The relative abundance of Porphyromonas in the peri-implantitis group was 6.1%. The results of the co-occurrence network showed differences in the network topology among the three groups of samples. The most connected three genera in the healthy implants were Halomonas, Fusobacterium, and Fretibacterium. The most connected three genera in peri-implant mucositis were Alistipes, Clostridia UCG-014, and Candidatus Saccharimonas. The most connected three genera in the peri-implantitis group were Lachnoanaerobaculum, Fusobacterium, and Atopobium. The betweenness of Porphvromonas gingivalis (red complex) in the PI group (7,900) was higher than in the HI group (23).ConclusionsThe community compositions of peri-implant submucosal microorganisms were significantly different in healthy implants, peri-implant mucositis, and peri-implantitis. The submucosal microbial communities in peri-implantitis were characterized by high species richness and diversity compared with the healthy implants; the relative abundance of red complex, some members of the yellow complex, and some novel periodontal pathogens was higher in the peri-implantitis and peri-implant mucositis groups than in the healthy implant group. The core flora of the co-occurrence network of healthy implants, peri-implant mucositis, and peri-implantitis varied considerably. The peri-implantitis site presented a relative disequilibrium microbial community, and Porphyromonas may play an important role in the co-occurrence network.

Dataset Information

MangoLeafBD: A comprehensive image dataset to classify diseased and healthy mango leaves.

Publications

MangoLeafBD: A comprehensive image dataset to classify diseased and healthy mango leaves.

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