MaCOnto: A robust maize crop ontology based on soils, fertilizers and irrigation knowledge

Aminu, Enesi Femi; Oyefolahan, Ishaq Oyebisi; Abdullahi, Muhammad Bashir; Salaudeen, Muhammadu Tajudeen

doi:10.1016/j.iswa.2022.200125

Cited by 5 publications

(2 citation statements)

References 41 publications

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“…However, plants are suffering serious problems in the agricultural industry due to illnesses that reduce the quality and quantity of the plants fruits the farming industry also faces problems with shortages of professionals, poor fertilizer management, and a lack of disease and pest awareness. there are many other explanations for the reduced output rate [1]. It can be a supervised or unsupervised learning method for machine learning.…”

Section: Introductionmentioning

confidence: 99%

Ontology-based classification method using statistical and symbolic approaches for plant diseases detection in Agriculture

Ahmed

2023

IJCDS

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Agriculturalists spend the majority of their financial resources managing and diagnosing plant diseases. Manually identifying plant diseases requires a lot of time and effort. This study recommends deep neural networks to automatically detect and identify plant diseases. The datasets are trained using a Random Forest technique to classify images of diseased and healthy leaves. The suggested ontology is modelled based on the Web Ontology Language (OWL). Support vector machines, convolution neural networks, and artificial neural networks are just a few of the classifiers utilized to extract features. A dataset that was self-gathered during the augmentation phase is used to validate the augmented method as part of the validation process. The majority of machine-learning methods for identifying plant diseases rely on custom criteria and rarely handle enormous amounts of data. To address this problem, this paper offers an ontology-based method for modelling plant diseases. According to the suggested method, pre-trained architectures include Alex Net and VGG19 CNNs. In order to extract the most useful features from a dataset, the algorithm modifies the details. Based on the correlation coefficient for each characteristic, the right subset of features is selected Convolutional neural networks, artificial neural networks, deep learning, and other artificial intelligence techniques have made it possible to detect diseases in crops like rice, wheat, maize, cotton, tomatoes, peas, potatoes, cucumbers, cassava, berries, peaches, grapes, apples, sweet paper, tea, and others. This study established that training large, publicly available data sets with machine learning algorithms is an effective method for diagnosing plant diseases.

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Section: Introductionmentioning

confidence: 99%

Ontology-based classification method using statistical and symbolic approaches for plant diseases detection in Agriculture

Ahmed

2023

IJCDS

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“…Fig. 12 Chart for Normalized multiple ontology rule evaluation D.Agro Knowledge Visualization A graphical representation of ontology is shown using the data visualization[15]. This will bring a better understanding…”

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confidence: 99%

Design of Multiple Ontology Based Agro Knowledge Mining Model

Abdullah,

Murali,

et al. 2023

IJRITCC

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Farming is regarded as a major industry in India, accounting for 17% of the country's GDP growth. Agriculture employs 60% of the population hence it is considered an important sector in India. The important factors for agriculture are pest management, disease prevention, irrigation management, soil mineral composition, crop management, location, and the season in which the crop is grown. Hence all this information along with the techniques are well known only by the experienced farmers. Hence it is important to create an agro knowledge management system. As a result, this work makes an attempt to develop a multiple ontology-based agro knowledge management system. The designed system consists of agriculture information related to attributes of soil mineral, moisture, season, location, crop type, and temperature. It consists of multiple ontologies such as soil ontology, crop ontology, location ontology, and crop season ontology to provide agronomy knowledge. Soil ontology is premeditated to classify the soil type in a hierarchical order while crop ontology classifies the crop type, location ontology classifies locations suitable for different crop types and finally, crop season ontology classifies the season that is suitable for different crops. A rule base is built to develop the knowledge base and to validate the truthfulness of the knowledge base. Visualization of a knowledge base is carried out for better understanding and decision-making.

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