Detection of fusarium head blight in wheat under field conditions using a hyperspectral camera and machine learning

Almoujahed, Muhammad Baraa; Rangarajan, Aravind Krishnaswamy; Whetton, Rebecca; Vincke, Damien; Eylenbosch, Damien; Vermeulen, Philippe; Mouazen, Abdul Mounem

doi:10.1016/j.compag.2022.107456

Cited by 25 publications

(11 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, the model was pre-trained on the Global Wheat Head Dataset 2021 of wheat heads of different varieties with different morphologies in different periods to solve the problem of poor generalization ability caused by a single sample set. Furthermore, the small size of wheat heads and frequent occurrences of occlusion pose challenges for wheat ear detection models [38,41]. These factors lead to a limited amount of wheat ear feature information that can be acquired by the detection model, which constrains the model's detection accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…In summary, several studies have shown that models trained on a single dataset collected from a specific region lack generalizability and exhibit significantly reduced accuracy when applied to wheat head detection in other regions [38][39][40][41]. Additionally, the presence of overlapping and small-sized wheat heads in field conditions poses limitations on the accuracy of wheat head detection.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

YOLOv7-MA: Improved YOLOv7-Based Wheat Head Detection and Counting

Meng,

Li,

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Detection and counting of wheat heads are crucial for wheat yield estimation. To address the issues of overlapping and small volumes of wheat heads on complex backgrounds, this paper proposes the YOLOv7-MA model. By introducing micro-scale detection layers and the convolutional block attention module, the model enhances the target information of wheat heads and weakens the background information, thereby strengthening its ability to detect small wheat heads and improving the detection performance. Experimental results indicate that after being trained and tested on the Global Wheat Head Dataset 2021, the YOLOv7-MA model achieves a mean average precision (MAP) of 93.86% with a detection speed of 35.93 frames per second (FPS), outperforming Faster-RCNN, YOLOv5, YOLOX, and YOLOv7 models. Meanwhile, when tested under the three conditions of low illumination, blur, and occlusion, the coefficient of determination (R2) of YOLOv7-MA is respectively 0.9895, 0.9872, and 0.9882, and the correlation between the predicted wheat head number and the manual counting result is stronger than others. In addition, when the YOLOv7-MA model is transferred to field-collected wheat head datasets, it maintains high performance with MAP in maturity and filling stages of 93.33% and 93.03%, respectively, and R2 values of 0.9632 and 0.9155, respectively, demonstrating better performance in the maturity stage. Overall, YOLOv7-MA has achieved accurate identification and counting of wheat heads in complex field backgrounds. In the future, its application with unmanned aerial vehicles (UAVs) can provide technical support for large-scale wheat yield estimation in the field.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

YOLOv7-MA: Improved YOLOv7-Based Wheat Head Detection and Counting

Meng,

Li,

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…For monitoring purposes, image collection has to be carried out. The camera, which can be an RGB [12,20,25], a multispectral [23,26], or a hyperspectral camera [27,28], influences the quality and quantity of data collected and mounted on the drone.…”

Section: Introductionmentioning

confidence: 99%

Yield Prediction Using NDVI Values from GreenSeeker and MicaSense Cameras at Different Stages of Winter Wheat Phenology

Zsebő,

Bede,

Kukorelli

et al. 2024

Drones

View full text Add to dashboard Cite

This work aims to compare and statistically analyze Normalized Difference Vegetation Index (NDVI) values provided by GreenSeeker handheld crop sensor measurements and calculate NDVI values derived from the MicaSense RedEdge-MX Dual Camera, to predict in-season winter wheat (Triticum aestivum L.) yield, improving a yield prediction model with cumulative growing degree days (CGDD) and days from sowing (DFS) data. The study area was located in Mosonmagyaróvár, Hungary. A small-scale field trial in winter wheat was constructed as a randomized block design including Environmental: N-135.3, P2O5-77.5, K2O-0; Balance: N-135.1, P2O5-91, K2O-0; Genezis: N-135, P2O5-75, K2O-45; and Control: N, P, K 0 kg/ha. The crop growth was monitored every second week between April and June 2022 and 2023, respectively. NDVI measurements recorded by GreenSeeker were taken at three pre-defined GPS points for each plot; NDVI values based on the MicaSense camera Red and NIR bands were calculated for the same points. Results showed a significant difference (p ≤ 0.05) between the Control and treated areas by GreenSeeker measurements and Micasense-based calculated NDVI values throughout the growing season, except for the heading stage. At the heading stage, significant differences could be measured by GreenSeeker. However, remotely sensed images did not show significant differences between the treated and Control parcels. Nevertheless, both sensors were found suitable for yield prediction, and 226 DAS was the most appropriate date for predicting winter wheat’s yield in treated plots based on NDVI values and meteorological data.

show abstract

“…In recent years, some new technologies for phenotypic analysis have been rapidly and accurately developed to improve plant yield and quality [18,19] . Plant phenotype detection has developed rapidly, especially hyperspectral imaging (HSI) technology, which integrates imaging technology and spectroscopy technology and provides methods for the detection of plants and the identification of plant diseases [20,21] .…”

Section: Introductionmentioning

confidence: 99%

Wheat FHB resistance assessment using hyperspectral feature band image fusion and deep learning

Kun,

Zhizhou,

Jinpeng

et al. 2024

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

The breeding and selection of resistant varieties is an effective way to minimize wheat Fusarium head blight (FHB) hazards, so it is important to identify and evaluate resistant varieties. The traditional resistance phenotype identification is still largely dependent on time-consuming manual methods. In this paper, the method for evaluating FHB resistance in wheat ears was optimized based on the fusion feature wavelength images of the hyperspectral imaging system and the Faster R-CNN algorithm. The spectral data from 400-1000 nm were preprocessed by the multiple scattering correction (MSC) algorithm. Three feature wavelengths (553 nm, 682 nm and 714 nm) were selected by analyzing the X-loading weights (XLW) according to the absolute value of the peaks and troughs in different principal component (PC) load coefficient curves. Then, the different fusion methods of the three feature wavelengths were explored with different weight coefficients. Faster R-CNN was trained on the fusion and RGB datasets with VGG16, AlexNet, ZFNet, and ResNet-50 networks separately. Then, the other detection models SSD, YOLOv5, YOLOv7, CenterNet, and RetinaNet were used to compare with the Faster R-CNN model. As a result, the Faster R-CNN with VGG16 was best with the mAP (mean Average Precision) ranged from 97.7% to 98.8%. The model showed the best performance for the Fusion Image-1 dataset. Moreover, the Faster R-CNN model with VGG16 achieved an average detection accuracy of 99.00%, which was 23.89%, 1.21%, 0.75%, 0.62%, and 8.46% higher than SSD, YOLOv5, YOLOv7, CenterNet, and RetinaNet models. Therefore, it was demonstrated that the Faster R-CNN model based on the hyperspectral feature image fusion dataset proposed in this paper was feasible for rapid evaluation of wheat FHB resistance. This study provided an important detection method for ensuring wheat food security.

show abstract

Detection of fusarium head blight in wheat under field conditions using a hyperspectral camera and machine learning

Cited by 25 publications

References 57 publications

YOLOv7-MA: Improved YOLOv7-Based Wheat Head Detection and Counting

YOLOv7-MA: Improved YOLOv7-Based Wheat Head Detection and Counting

Yield Prediction Using NDVI Values from GreenSeeker and MicaSense Cameras at Different Stages of Winter Wheat Phenology

Wheat FHB resistance assessment using hyperspectral feature band image fusion and deep learning

Contact Info

Product

Resources

About