AIMC Topic: Plant Leaves

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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...

TOMMicroNet: Convolutional Neural Networks for Smartphone-Based Microscopic Detection of Tomato Biotic and Abiotic Plant Health Issues.

Phytopathology
The image-based detection and classification of plant diseases has become increasingly important to the development of precision agriculture. We consider the case of tomato, a high-value crop supporting the livelihoods of many farmers around the worl...

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...

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...

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 ...

Deep Learning-Based Barley Disease Quantification for Sustainable Crop Production.

Phytopathology
Net blotch disease caused by is a major fungal disease that affects barley () plants and can result in significant crop losses. In this study, we developed a deep learning model to quantify net blotch disease symptoms on different days postinfection...

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...

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...