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Image acquisition, preprocessing and classification of citrus fruit diseases: A systematic literature review
Different kinds of techniques are evaluated and analyzed for various classification models
for the detection of diseases of citrus fruits. This paper aims to systematically review the …
for the detection of diseases of citrus fruits. This paper aims to systematically review the …
Role of artificial intelligence in medical image analysis: A review of current trends and future directions
X Li, L Zhang, J Yang, F Teng - Journal of Medical and Biological …, 2024 - Springer
Purpose This review offers insight into AI's current and future contributions to medical image
analysis. The article highlights the challenges associated with manual image interpretation …
analysis. The article highlights the challenges associated with manual image interpretation …
Comparing inception V3, VGG 16, VGG 19, CNN, and ResNet 50: A case study on early detection of a rice disease
Rice production has faced numerous challenges in recent years, and traditional methods
are still being used to detect rice diseases. This research project developed an automated …
are still being used to detect rice diseases. This research project developed an automated …
Weighted average ensemble deep learning model for stratification of brain tumor in MRI images
Brain tumor diagnosis at an early stage can improve the chances of successful treatment
and better patient outcomes. In the biomedical industry, non-invasive diagnostic procedures …
and better patient outcomes. In the biomedical industry, non-invasive diagnostic procedures …
[HTML][HTML] Harnessing the power of transfer learning in sunflower disease detection: A comparative study
Sunflower is an important crop that is susceptible to various diseases, which can
significantly impact crop yield and quality. Early and accurate detection of these diseases is …
significantly impact crop yield and quality. Early and accurate detection of these diseases is …
Detection and counting of small target apples under complicated environments by using improved YOLOv7-tiny
L Ma, L Zhao, Z Wang, J Zhang, G Chen - Agronomy, 2023 - mdpi.com
Weather disturbances, difficult backgrounds, the shading of fruit and foliage, and other
elements can significantly affect automated yield estimation and picking in small target apple …
elements can significantly affect automated yield estimation and picking in small target apple …
Adversarial approaches to tackle imbalanced data in machine learning
Real-world applications often involve imbalanced datasets, which have different
distributions of examples across various classes. When building a system that requires a …
distributions of examples across various classes. When building a system that requires a …
Enhanced corn seed disease classification: leveraging MobileNetV2 with feature augmentation and transfer learning
M Alkanan, Y Gulzar - Frontiers in Applied Mathematics and Statistics, 2024 - frontiersin.org
In the era of advancing artificial intelligence (AI), its application in agriculture has become
increasingly pivotal. This study explores the integration of AI for the discriminative …
increasingly pivotal. This study explores the integration of AI for the discriminative …
[HTML][HTML] Lightweight one-stage maize leaf disease detection model with knowledge distillation
Y Hu, G Liu, Z Chen, J Liu, J Guo - Agriculture, 2023 - mdpi.com
Maize is one of the world's most important crops, and maize leaf diseases can have a direct
impact on maize yields. Although deep learning-based detection methods have been …
impact on maize yields. Although deep learning-based detection methods have been …
[HTML][HTML] Estimation of the extent of the vulnerability of agriculture to climate change using analytical and deep-learning methods: a case study in Jammu, Kashmir, and …
Climate stress poses a threat to the agricultural sector, which is vital for both the economy
and livelihoods in general. Quantifying its risk to food security, livelihoods, and sustainability …
and livelihoods in general. Quantifying its risk to food security, livelihoods, and sustainability …