A method for automatic identification of crop lines in drone images from a mango tree plantation using segmentation over YCrCb color space and Hough transform

Quiroz, Ricardo A. Arango; Guidotti, Fernada Pereira; Bedoya, Albeiro Espinosa

doi:10.1109/stsiva.2019.8730214

Cited by 6 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This was consistent with the outcomes of the weight analysis process (Figure 7). The result of this paper is also in line with previous studies [38][39][40]. The authors offer two reasons as to why the Cr condition achieved the best performance among the others.…”

Section: Results Analysissupporting

confidence: 90%

See 1 more Smart Citation

An Enhanced YOLOv5 Model for Greenhouse Cucumber Fruit Recognition Based on Color Space Features

et al. 2022

View full text Add to dashboard Cite

The identification of cucumber fruit is an essential procedure in automated harvesting in greenhouses. In order to enhance the identification ability of object detection models for cucumber fruit harvesting, an extended RGB image dataset (n = 801) with 3943 positive and negative labels was constructed. Firstly, twelve channels in four color spaces (RGB, YCbCr, HIS, La*b*) were compared through the ReliefF method to choose the channel with the highest weight. Secondly, the RGB image dataset was converted to the pseudo-color dataset of the chosen channel (Cr channel) to pre-train the YOLOv5s model before formal training using the RGB image dataset. Based on this method, the YOLOv5s model was enhanced by the Cr channel. The experimental results show that the cucumber fruit recognition precision of the enhanced YOLOv5s model was increased from 83.7% to 85.19%. Compared with the original YOLOv5s model, the average values of AP, F1, recall rate, and mAP were increased by 8.03%, 7%, 8.7%, and 8%, respectively. In order to verify the applicability of the pre-training method, ablation experiments were conducted on SSD, Faster R-CNN, and four YOLOv5 versions (s, l, m, x), resulting in the accuracy increasing by 1.51%, 3.09%, 1.49%, 0.63%, 3.15%, and 2.43%, respectively. The results of this study indicate that the Cr channel pre-training method is promising in enhancing cucumber fruit detection in a near-color background.

show abstract

Section: Results Analysissupporting

confidence: 90%

“…Therefore, the Cr channel is more suitable for studying images under different exposures. (2) The Cr channel, a chrominance component for red [41,42], eliminates the disturbance of the green chrominance component for segmentation in near-color backgrounds [39].…”

Section: Results Analysismentioning

confidence: 99%

An Enhanced YOLOv5 Model for Greenhouse Cucumber Fruit Recognition Based on Color Space Features

et al. 2022

View full text Add to dashboard Cite

show abstract

“…These color spaces are composed of three channels [16] . Select appropriate color channels and separate them from speeding up image processing and reduce the impact of lighting.…”

Section: Determination Of Crop Boundarymentioning

confidence: 99%

Measurement of the distance from grain divider to harvesting boundary based on dynamic regions of interest

Chen

Song

Guan

et al. 2021

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

Combine harvesters need to work along the crop boundary line during operation. Lateral deviation (the distance between the grain divider and the harvesting boundary line) is important navigation information. Improving the measurement accuracy and real-time performance of lateral deviation is an effective way to improve navigation accuracy. Aiming at the problems of poor real-time performance and low measurement accuracy of existing lateral deviation measurement methods, a method of dynamic selection of the region of interest in the process of image processing was proposed and verified by field experiments. The calculation of lateral deviation includes the following stages: analyzing the average gray value of each column in the field image; drawing dynamic region of interest using maximum average gray value; extracting the rice boundary line by using the probabilistic Hough transform algorithm; predicting the location of the boundary line by using the Kalman filter algorithm; measuring the lateral deviation by using the inverse perspective transform algorithm. The analysis of images under different rice fields showed that the method can effectively identify crop boundary lines. According to the test results of calling cameras in different installation positions, the highest extraction success rate of the boundary line was 96.9%, the average success rate was 94.8%, and the speed of real-time measurement of lateral deviation was 0.065 s/frame. When the driving speed was 0.4 m/s, the detection error of linear tracking detection was less than 4.3 cm. With the increase of speed, the error gradually increased. The algorithm has a good real-time performance and high accuracy during low-speed driving.

show abstract

IoT-based Precision Agriculture: A Review

Diya

Nandan

Dhote

2022

Proceedings of Emerging Trends and Technologies on Intelligent Systems

View full text Add to dashboard Cite

A method for automatic identification of crop lines in drone images from a mango tree plantation using segmentation over YCrCb color space and Hough transform

Cited by 6 publications

References 12 publications

An Enhanced YOLOv5 Model for Greenhouse Cucumber Fruit Recognition Based on Color Space Features

An Enhanced YOLOv5 Model for Greenhouse Cucumber Fruit Recognition Based on Color Space Features

Measurement of the distance from grain divider to harvesting boundary based on dynamic regions of interest

IoT-based Precision Agriculture: A Review

Contact Info

Product

Resources

About