Human activities are directly impacted by weather occurrences. Forest fires, high air temperatures that lead to drought, and extreme weather events caused by global warming all make life more difficult for humans. Pre...
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Artificial neural networks give more promising and accurate results than other methods for prediction, classification, and segmentation engineering problems. The accuracy of the artificial neural network is affected b...
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In the field of aquaculture, numerous studies indicate that traditional fishkeeping faces significant challenges due to the manual maintenance required, leading to inefficiencies such as inconsistent cleaning, feeding...
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Despite India's global leadership in dairy production, the industry faces persistent challenges in animal productivity due to healthcare gaps and remote veterinary access. This hinders farmer profits and industry ...
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Simultaneous and dynamic detection of diseases from infield images of citrus plants is the primary objective of this investigation. Refraining from discarding the disease-related issues would reduce agricultural yield...
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Spiking Neural Networks (SNN) are biologically inspired networks working on the principle of communication triggered while crossing of threshold potentials. During the COVID-19 pandemic, immunity has been acquired by ...
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The coronavirus disease, or COVID19, characters "CO" stand for for the corona, "VI" as virus, & "D" for disease in the context of the word COVID. It describes an infectious disease br...
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Visual impairment takes a major toll in a person's life for them to act independently without others help. This paper introduces an assistive technology that makes use of the YOLO (You Only Look Once) algorithm in...
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Epilepsy is the second most prevalent neurological disorder. It poses significant challenges to both diagnosis and treatment. The information obtained from electroencephalography serves as crucial for understanding th...
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The most serious disease that can affect paddy plants is blast disease. All over the world, it results in enormous yield losses. A fungus that attacks the plant's leaves, nodes, and grains is the main culprit behi...
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ISBN:
(纸本)9789819726134
The most serious disease that can affect paddy plants is blast disease. All over the world, it results in enormous yield losses. A fungus that attacks the plant's leaves, nodes, and grains is the main culprit behind the disease. Fungicides are frequently used to stop blast disease, but this approach has some drawbacks, including environmental pollution and the development of fungicide-resistant disease strains. An effective tool for modeling and managing complex systems, such as the blast disease in paddy plants, is fuzzy logic. In this study, we investigate the modeling and management of blast disease in paddy plants using fuzzy logic. We'll discuss the input variables, fuzzy sets, rule base, and output variables, among other components, that make up the fuzzy logic system. The various phases of fuzzy logic, including fuzzification, inference, and defuzzification, will also be covered. The advantages of using fuzzy logic to manage blast disease in paddy plants, including its capacity to deal with ambiguous and imprecise data and its potential to integrate with other control systems, will be discussed in the final section. The fungal disease known as rice leaf blast, which is having a devastating effect on rice production and quality throughout the globe, thrives in warm, humid environments. Management of rice production relies on precise and non-destructive diagnostic techniques. The use of hyper spectral imaging technologies for diagnosing plant diseases has much promise. The problem with using hyper spectral data to build an effective illness classification model is that it contains a lot of duplicated information. However, a lack of representative features has been gathered due to the complexity and limited scope of agricultural hyper spectral imaging data collection. This paper discussed the four models DenseNet169-MLP, CNN, EfficientNetB3, and DNN JOA. DenseNet169-MLP achieves the highest accuracy 96.52, precision of 100, F1-score 94.29 compared to other model
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