AIMC Topic: Plant Diseases

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Real-time plant health assessment via implementing cloud-based scalable transfer learning on AWS DeepLens.

PloS one
The control of plant leaf diseases is crucial as it affects the quality and production of plant species with an effect on the economy of any country. Automated identification and classification of plant leaf diseases is, therefore, essential for the ...

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance.

Scientific reports
Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust re...

Disease Detection in Plum Using Convolutional Neural Network under True Field Conditions.

Sensors (Basel, Switzerland)
The agriculture sector faces crop losses every year due to diseases around the globe, which adversely affect food productivity and quality. Detecting and identifying plant diseases at an early stage is still a challenge for farmers, particularly in d...

Classification Accuracy Improvement for Small-Size Citrus Pests and Diseases Using Bridge Connections in Deep Neural Networks.

Sensors (Basel, Switzerland)
Due to the rich vitamin content in citrus fruit, citrus is an important crop around the world. However, the yield of these citrus crops is often reduced due to the damage of various pests and diseases. In order to mitigate these problems, several con...

Apple Leaf Diseases Recognition Based on An Improved Convolutional Neural Network.

Sensors (Basel, Switzerland)
Scab, frogeye spot, and cedar rust are three common types of apple leaf diseases, and the rapid diagnosis and accurate identification of them play an important role in the development of apple production. In this work, an improved model based on VGG1...

Diagnosis of the Severity of Fusarium Head Blight of Wheat Ears on the Basis of Image and Spectral Feature Fusion.

Sensors (Basel, Switzerland)
head blight (FHB), one of the most prevalent and damaging infection diseases of wheat, affects quality and safety of associated food. In this study, to realize the early accurate monitoring of FHB, a diagnostic model of disease severity was proposed...

Detection of rice plant diseases based on deep transfer learning.

Journal of the science of food and agriculture
BACKGROUND: As the primary food for nearly half of the world's population, rice is cultivated almost all over the world, especially in Asian countries. However, the farmers and planting experts have been facing many persistent agricultural challenges...

Disease Classification in Eggplant Using Pre-trained VGG16 and MSVM.

Scientific reports
Currently, the application of deep learning in crop disease classification is one of the active areas of research for which an image dataset is required. Eggplant (Solanum melongena) is one of the important crops, but it is susceptible to serious dis...

A Recognition Method for Rice Plant Diseases and Pests Video Detection Based on Deep Convolutional Neural Network.

Sensors (Basel, Switzerland)
Increasing grain production is essential to those areas where food is scarce. Increasing grain production by controlling crop diseases and pests in time should be effective. To construct video detection system for plant diseases and pests, and to bui...

Occurrence prediction of pests and diseases in cotton on the basis of weather factors by long short term memory network.

BMC bioinformatics
BACKGROUND: The occurrence of cotton pests and diseases has always been an important factor affecting the total cotton production. Cotton has a great dependence on environmental factors during its growth, especially climate change. In recent years, m...