A deep learning approach for early detection of drought stress in maize using proximal scale digital images

Goyal, Pooja; Sharda, Rakesh; Saini, Mukesh; Siag, Mukesh

doi:10.1007/s00521-023-09219-z

Cited by 7 publications

(4 citation statements)

References 41 publications

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“…An et al [10] (2019) presented a maize drought identification and classification method based on deep convolutional neural networks (DCNNs), achieving accuracies of 98.14% and 95.95% in drought stress identification and classification, respectively. Goyal et al [11] (2024) proposed a customized convolutional neural network for in situ maize drought stress identification and classification, achieving accuracies of 98.71% and 98.53% on the training and test sets, respectively, surpassing the latest technology architecture with 0.65 million parameters. Transfer learning with ResNet50 and EfficientNetB1 achieved accuracies of 99.26% on the test set.…”

Section: Research Work By Relevant Scholarsmentioning

confidence: 99%

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Early Drought Detection in Maize Using UAV Images and YOLOv8+

Niu,

Nie,

et al. 2024

Drones

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The escalating global climate change significantly impacts the yield and quality of maize, a vital staple crop worldwide, especially during seedling stage droughts. Traditional detection methods are limited by their single-scenario approach, requiring substantial human labor and time, and lack accuracy in the real-time monitoring and precise assessment of drought severity. In this study, a novel early drought detection method for maize based on unmanned aerial vehicle (UAV) images and Yolov8+ is proposed. In the Backbone section, the C2F-Conv module is adopted to reduce model parameters and deployment costs, while incorporating the CA attention mechanism module to effectively capture tiny feature information in the images. The Neck section utilizes the BiFPN fusion architecture and spatial attention mechanism to enhance the model’s ability to recognize small and occluded targets. The Head section introduces an additional 10 × 10 output, integrates loss functions, and enhances accuracy by 1.46%, reduces training time by 30.2%, and improves robustness. The experimental results demonstrate that the improved Yolov8+ model achieves precision and recall rates of approximately 90.6% and 88.7%, respectively. The mAP@50 and mAP@50:95 reach 89.16% and 71.14%, respectively, representing respective increases of 3.9% and 3.3% compared to the original Yolov8. The UAV image detection speed of the model is up to 24.63 ms, with a model size of 13.76 MB, optimized by 31.6% and 28.8% compared to the original model, respectively. In comparison with the Yolov8, Yolov7, and Yolo5s models, the proposed method exhibits varying degrees of superiority in mAP@50, mAP@50:95, and other metrics, utilizing drone imagery and deep learning techniques to truly propel agricultural modernization.

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Section: Research Work By Relevant Scholarsmentioning

confidence: 99%

“…Compared to conventional 2D convolutions, the position coordinate offset endows deformable convolutions with stronger feature extraction capabilities. Since the offset pn may be non-integer, bilinear interpolation is employed in practical implementation, as shown in Equation (11).…”

Section: Bifpn Model Structurementioning

confidence: 99%

Early Drought Detection in Maize Using UAV Images and YOLOv8+

Niu,

Nie,

et al. 2024

Drones

View full text Add to dashboard Cite

show abstract

“…In recent years, there has been a growing interest in leveraging computer vision and deep learning techniques to automate the process of drought-stress analysis in maize. [2][3][4][5][6]. These technologies offer the potential to provide rapid, non-destructive, and objective assessments of drought-stress, enabling farmers and researchers to make informed decisions in a timely manner.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Visual Differences in Drought Stressed Maize through Reflectance and Data-Driven Analysis

Banerjee,

Reynolds,

Taggart

et al. 2024

Preprint

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Environmental factors, such as drought-stress, significantly impact maize growth and productivity worldwide. To improve yield and quality, effective strategies for early detection and mitigation of drought-stress in maize are essential. This paper presents a detailed analysis of three imaging trials conducted to detect drought-stress in maize plants using an existing, custom-developed, low cost, high throughput phenotyping platform. We propose a pipeline for early detection of water stress in maize plants using a Vision Transformer classifier and analysis of distributions of near-infrared (NIR) reflectance from the plants. We also explored suitable regions on the plant that are more sensitive to drought-stress and show that the region surrounding the youngest expanding leaf (YEL) and the stem can be used as a more consistent alternative to analysis involving just the YEL. Our results show good separation between well-watered and drought-stressed trials for two out of the three imaging trials both in terms of classification accuracy from data-driven features as well as through analysis of histograms of NIR reflectance.

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“…Traditional methods for assessing drought stress in maize, such as visual inspection and manual measurements, are labor-intensive, time-consuming, and often subjective. In recent years, there has been growing interest in leveraging computer vision and deep learning techniques to automate the process of drought-stress analysis in maize [2][3][4][5][6]. These technologies offer the potential to provide rapid, non-destructive, and objective assessments of drought stress, enabling farmers and researchers to make informed decisions in a timely manner.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Visual Differences in Drought-Stressed Maize through Reflectance and Data-Driven Analysis

Banerjee,

Reynolds,

Taggart

et al. 2024

View full text Add to dashboard Cite

Environmental factors, such as drought stress, significantly impact maize growth and productivity worldwide. To improve yield and quality, effective strategies for early detection and mitigation of drought stress in maize are essential. This paper presents a detailed analysis of three imaging trials conducted to detect drought stress in maize plants using an existing, custom-developed, low-cost, high-throughput phenotyping platform. A pipeline is proposed for early detection of water stress in maize plants using a Vision Transformer classifier and analysis of distributions of near-infrared (NIR) reflectance from the plants. A classification accuracy of 85% was achieved in one of our trials, using hold-out trials for testing. Suitable regions on the plant that are more sensitive to drought stress were explored, and it was shown that the region surrounding the youngest expanding leaf (YEL) and the stem can be used as a more consistent alternative to analysis involving just the YEL. Experiments in search of an ideal window size showed that small bounding boxes surrounding the YEL and the stem area of the plant perform better in separating drought-stressed and well-watered plants than larger window sizes enclosing most of the plant. The results presented in this work show good separation between well-watered and drought-stressed categories for two out of the three imaging trials, both in terms of classification accuracy from data-driven features as well as through analysis of histograms of NIR reflectance.

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A deep learning approach for early detection of drought stress in maize using proximal scale digital images

Cited by 7 publications

References 41 publications

Early Drought Detection in Maize Using UAV Images and YOLOv8+

Early Drought Detection in Maize Using UAV Images and YOLOv8+

Quantifying Visual Differences in Drought Stressed Maize through Reflectance and Data-Driven Analysis

Quantifying Visual Differences in Drought-Stressed Maize through Reflectance and Data-Driven Analysis

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