Wavelet and Pyramid Histogram Features for Image-Based Leaf Detection

2018 ◽  
pp. 269-278
Author(s):  
Al Amin Neaz Ahmed ◽  
H. M. Fazlul Haque ◽  
Abdur Rahman ◽  
Md. Susam Ashraf ◽  
Swakkhar Shatabda
Keyword(s):  
Leaf Detection

scholarly journals A coarse-to-fine leaf detection approach based on leaf skeleton identification and joint segmentation

2021 ◽  
Vol 206 ◽  
pp. 94-108
Author(s):  
Liankuan Zhang ◽  
Chunlei Xia ◽  
Deqin Xiao ◽  
Paul Weckler ◽  
Yubin Lan ◽  
...  
Keyword(s):  
Detection Approach ◽  
Leaf Detection ◽  
Coarse To Fine

scholarly journals Plant Leaf Detection and Counting in a Greenhouse during Day and Nighttime Using a Raspberry Pi NoIR Camera

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6659
Author(s):  
Aryuanto Soetedjo ◽  
Evy Hendriarianti
Keyword(s):  
Execution Time ◽  
Low Cost ◽  
Infrared Camera ◽  
Raspberry Pi ◽  
Camera System ◽  
Detection And Counting ◽  
Outdoor Environments ◽  
Static Images ◽  
Leaf Detection ◽  
Non Destructive

A non-destructive method using machine vision is an effective way to monitor plant growth. However, due to the lighting changes and complicated backgrounds in outdoor environments, this becomes a challenging task. In this paper, a low-cost camera system using an NoIR (no infrared filter) camera and a Raspberry Pi module is employed to detect and count the leaves of Ramie plants in a greenhouse. An infrared camera captures the images of leaves during the day and nighttime for a precise evaluation. The infrared images allow Otsu thresholding to be used for efficient leaf detection. A combination of numbers of thresholds is introduced to increase the detection performance. Two approaches, consisting of static images and image sequence methods are proposed. A watershed algorithm is then employed to separate the leaves of a plant. The experimental results show that the proposed leaf detection using static images achieves high recall, precision, and F1 score of 0.9310, 0.9053, and 0.9167, respectively, with an execution time of 551 ms. The strategy of using sequences of images increases the performances to 0.9619, 0.9505, and 0.9530, respectively, with an execution time of 516.30 ms. The proposed leaf counting achieves a difference in count (DiC) and absolute DiC (ABS_DiC) of 2.02 and 2.23, respectively, with an execution time of 545.41 ms. Moreover, the proposed method is evaluated using the benchmark image datasets, and shows that the foreground–background dice (FBD), DiC, and ABS_DIC are all within the average values of the existing techniques. The results suggest that the proposed system provides a promising method for real-time implementation.

Multispectral-based leaf detection system for spot sprayer application to control citrus psyllids

2013 ◽  
Vol 116 (4) ◽  
pp. 509-517 ◽  
Author(s):  
Peter A. Larbi ◽  
Reza Ehsani ◽  
Masoud Salyani ◽  
Joe M. Maja ◽  
Ashish Mishra ◽  
...  
Keyword(s):  
Detection System ◽  
Leaf Detection

A learning-based approach for leaf detection in traffic surveillance video

2017 ◽  
Vol 29 (4) ◽  
pp. 1895-1904
Author(s):  
Li Chen ◽  
Xiaoping Peng ◽  
Jing Tian ◽  
Jiaxiang Liu
Keyword(s):  
Surveillance Video ◽  
Traffic Surveillance ◽  
Leaf Detection

scholarly journals Plant leaf detection using modified active shape models

2013 ◽  
Vol 116 (1) ◽  
pp. 23-35 ◽  
Author(s):  
Chunlei Xia ◽  
Jang-Myung Lee ◽  
Yan Li ◽  
Yoo-Han Song ◽  
Bu-Keun Chung ◽  
...  
Keyword(s):  
Plant Leaf ◽  
Active Shape Models ◽  
Shape Models ◽  
Active Shape ◽  
Leaf Detection

scholarly journals Centroid Distance Neighbourhood Features and Genetic Algorithm Optimization for Leaf Disease Detection

2019 ◽  
Vol 8 (11) ◽  
pp. 2072-2076
Keyword(s):  
Genetic Algorithm ◽  
Detection Algorithm ◽  
Disease Detection ◽  
Feature Points ◽  
Algorithm Optimization ◽  
Algorithm Performance ◽  
Leaf Disease ◽  
Centroid Distance ◽  
Disease Region ◽  
Leaf Detection

Leaf disease detection algorithm using Centroid Distance Neighbourhood Features (CDNF) and Genetic Algorithm (GA) optimization is presented in this paper. This method initially segment the disease affected regions from the leaf. The disease affected region is applied for identifying the best feature points using SURF (Speeded Up Robust Feature) algorithm. From a single SURF point four features are extracted by forming a 5×5 neighbourhood across the SURF feature point. The feature extracted using Centroid Distance Neighbour (CDN) is optimized using genetic algorithm to find best features that are able to classify multiple diseases. During testing phase, the disease region is identified and features points are selected using the SURF points. The features are extracted using the CDN and the necessary features that are optimized by genetic algorithm are sorted out as test features. The test features are classified from the trained features using K-Nearest Neighbour (KNN) algorithm. Performance of the proposed leaf disease detection algorithm is evaluated using metrics such as specificity, sensitivity and accuracy. Experimental results shows that the proposed leaf detection algorithm outperforms the state of-the-art methods and it can be used in real time disease detection

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