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Plant Leaves

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Predicting leaf nitrogen content in wolfberry trees by hyperspectral transformation and machine learning for precision agriculture.

PloS one
Leaf nitrogen content (LNC) is an important indicator for scientific diagnosis of the nutrition status of crops. It plays an important role in the growth, yield and quality of wolfberry. This study aimed to develop new spectral indices (NSIs) and con...

Detection of Verticillium infection in cotton leaves using ATR-FTIR spectroscopy coupled with machine learning algorithms.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Verticillium wilt (VW) is a soil-borne vascular disease that affects upland cotton and is caused by Verticillium dahliae Kleb. A rapid and user-friendly early diagnostic technique is essential for the preventing and controlling VW disease. In this st...

Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification.

Scientific reports
Manual identification of tomato leaf diseases is a time-consuming and laborious process that may lead to inaccurate results without professional assistance. Therefore, an automated, early, and precise leaf disease recognition system is essential for ...

Monitoring the leaf damage by the rice leafroller with deep learning and ultra-light UAV.

Pest management science
BACKGROUND: Rice leafroller is a serious threat to the production of rice. Monitoring the damage caused by rice leafroller is essential for effective pest management. Owing to limitations in collecting decent quality images and high-performing identi...

Hyperspectral imaging analysis for early detection of tomato bacterial leaf spot disease.

Scientific reports
Recent advancements in hyperspectral imaging (HSI) for early disease detection have shown promising results, yet there is a lack of validated high-resolution (spatial and spectral) HSI data representing the responses of plants at different stages of ...

Advancing mango leaf variant identification with a robust multi-layer perceptron model.

Scientific reports
Mango, often regarded as the "king of fruits," holds a significant position in Bangladesh's agricultural landscape due to its popularity among the general population. However, identifying different types of mangoes, especially from mango leaves, pose...

Regression prediction of tobacco chemical components during curing based on color quantification and machine learning.

Scientific reports
Color is one of the most important indicators to characteristic the quality of tobacco, which is strongly related to the variations of chemical components. In order to clarify the relationship between the changes of tobacco color and chemical compone...

Synergistic use of handcrafted and deep learning features for tomato leaf disease classification.

Scientific reports
This research introduces a Computer-Aided Diagnosis-system designed aimed at automated detections & classification of tomato leaf diseases, combining traditional handcrafted features with advanced deep learning techniques. The system's process encomp...

Deep learning to capture leaf shape in plant images: Validation by geometric morphometrics.

The Plant journal : for cell and molecular biology
Plant leaves play a pivotal role in automated species identification using deep learning (DL). However, achieving reproducible capture of leaf variation remains challenging due to the inherent "black box" problem of DL models. To evaluate the effecti...

Incremental RPN: Hierarchical Region Proposal Network for Apple Leaf Disease Detection in Natural Environments.

IEEE/ACM transactions on computational biology and bioinformatics
Apple leaf diseases can seriously affect apple production and quality, and accurately detecting them can improve the efficiency of disease monitoring. Owing to the complex natural growth environment, apple leaf lesions may be easily confused with bac...