Estimating leaf area index of maize using UAV-based digital imagery and machine learning methods

Du, Lianfeng; Yang, Huan; Song, Xuan; Wei, Ning; Chen, Yu; Wang, Weitong; Zhao, Yun

doi:10.1038/s41598-022-20299-0

Cited by 26 publications

(19 citation statements)

References 44 publications

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“…The closer the spacing, the higher the leaf density and the lower the exposure to sunlight reaching the lower leaf layers so LAI value increases (Du et al, 2022). The inhibition of leaf expansion will have an impact on decreasing the capacity of the leaves to absorb light, so that it will minimize the performance of the rate of photosynthesis in plants.…”

Section: Resultsmentioning

confidence: 99%

Physiological Responses of Indigenous Vegetable of Sintrong (Crassocephalum crepidioides) due to Exposure to High Temperature

Utama

Kurniawati²,

Faridah³

2023

Caraka Tani J. Sustain. Agric.

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Sintrong is an Indonesian indigenous vegetable with leaves used for vegetables, digestive disorders, and burns. Changes in the environment due to an increase in temperature affect the growth and quality of sitrong, and its existence in the nature is threatened. This study aims to obtain information about the effect of exposure to high temperatures on the physiological character and flavonoid content of indigenous sintrong vegetables and obtain accession of sintrong, which can be developed as a functional vegetable. The Nested randomized group design was applied with two factors, temperature differences as the main plot and accession as a second plot. Four replications were conducted for each accession in the Cikabayan experimental garden of IPB. The results showed that exposure to high temperatures up to 32 °C increased the speed of flowering age, which was 4.76% and 7.14% faster and showed a high wilting rate of 36.66%, but decreased leaf area index up to 30.30% and 42.42% at the conditions above ambient temperature exposure (control). Flavonoid content did not show any effect due to exposure to high temperatures. The flavonoid content reached 1695.38 and 1834.83 mg QE 100 g<sup>-1</sup>. Bogor 1 accession showed the best performance so that the plants can be developed for functional vegetables. Based on the research findings, sintrong should be harvested earlier before flowering to obtain high leaf production and good-quality vegetables.

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Section: Resultsmentioning

confidence: 99%

Physiological Responses of Indigenous Vegetable of Sintrong (Crassocephalum crepidioides) due to Exposure to High Temperature

Utama

Kurniawati²,

Faridah³

2023

Caraka Tani J. Sustain. Agric.

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“…Additionally, using handheld sensors (SPAD(Japan), Greenseeker(America), Li-cor(America) et al) can indirectly evaluate crop N status at the small scale level, which cannot comprehensively represent crop canopy in the field or at the large scale level [4][5][6]. Remote sensing technology based on an unmanned aerial vehicle (UAV) has been widely used for image collection to evaluate crop growth conditions [7][8][9][10][11][12]. Most studies used RGB, near-infrared, multispectral, hyperspectral, and thermal infrared cameras or sensors based on UAVs for crop monitoring at a large scale [8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Remote sensing technology based on an unmanned aerial vehicle (UAV) has been widely used for image collection to evaluate crop growth conditions [7][8][9][10][11][12]. Most studies used RGB, near-infrared, multispectral, hyperspectral, and thermal infrared cameras or sensors based on UAVs for crop monitoring at a large scale [8][9][10][11][12][13][14]. Due to its affordability, flexibility, and high efficiency, as well as its band alignment, the RGB camera is mostly used in crop monitoring compared to multispectral and hyperspectral sensors, which indicates that the RGB camera based on UAVs has great potential for crop monitoring [15,16].…”

Section: Introductionmentioning

confidence: 99%

Accuracy Comparison of Estimation on Cotton Leaf and Plant Nitrogen Content Based on UAV Digital Image under Different Nutrition Treatments

et al. 2023

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The rapid, accurate estimation of leaf nitrogen content (LNC) and plant nitrogen content (PNC) in cotton in a non-destructive way is of great significance to the nutrient management of cotton fields. The RGB images of cotton fields in Shihezi (China) were obtained by using a low-cost unmanned aerial vehicle (UAV) with a visible-light digital camera. Combined with the data of LNC and PNC in different growth stages, the correlation between N content and visible light vegetation indices (VIs) was analyzed, and then the Random Forest (RF), Support Vector Machine (SVM), Back Propagation Neural Network (BP), and stepwise multiple linear regression (SMLR) were used to develop N content estimation models at different growth stages. The accuracy of the estimation model was assessed by coefficient of determination (R2), root mean squared error (RMSE), and relative root mean square error (rRMSE), so as to determine the optimal estimated growth stage and the best model. The results showed that the correlation between VIs and LNC was stronger than that between PNC, and the estimation accuracy of different models decreased continuously with the development of growth stages, with higher estimation accuracy in the peak squaring stage. Among the four algorithms, the best accuracy (R2 = 0.9001, RMSE = 1.2309, rRMSE = 2.46% for model establishment, and R2 = 0.8782, RMSE = 1.3877, rRMSE = 2.82% for model validation) was obtained when applying RF at the peak squaring stage. The LNC model for whole growth stages could be used in the later growth stage due to its higher accuracy. The results of this study showed that there is a potential for using an affordable and non-destructive UAV-based digital system to produce predicted LNC content maps that are representative of the current field nitrogen status.

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“…In recent years, advances in unmanned aerial vehicle (UAV) technology have provided a means to quickly and efficiently acquire high-throughput plant image data, enabling intelligent monitoring of massive plant growth processes [8]. Researchers use UAV-carrying sensors to collect data on plant height [9][10], leaf number [11][12], leaf area [13][14], etc., enabling real-time monitoring of plant growth and development as well as predicting yield. [15] showed an accuracy rate of 94.4% when using a camera-carrying UAV to count the number of pineapple crowns.…”

Section: Introductionmentioning

confidence: 99%

YOSBG: UAV image data-driven high-throughput field tobacco leaf counting method

Xiao,

Li,

Liang

et al. 2023

Preprint

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Background: Estimating tobacco leaf yield is a crucial task. The number of leaves is directly related to yield. Therefore, it is important to achieve intelligent and rapid high-throughput statistical counting of field tobacco leaves. Unfortunately, the current method of counting the number of tobacco leaves is expensive, imprecise, and inefficient. It heavily relies on manual labor and also faces challenges of mutual shading among the field tobacco plants during their growth and maturity stage, as well as complex environmental background information. This study proposes an efficient method for counting the number of tobacco leaves in a large field based on unmanned aerial vehicle (UAV) image data. First, a UAV is used to obtain high-throughput vertical orthoimages of field tobacco plants to count the leaves of the tobacco plants. The tobacco plant recognition model is then used for plant detection and segmentation to create a dataset of images of individual tobacco plants. Finally, the improved algorithm YOLOv8 with Squeeze-and-Excitation (SE) and bidirectional feature pyramid network (BiFPN) and GhostNet (YOSBG) algorithm is used to detect and count tobacco leaves on individual tobacco plants. Results: Experimental results show YOSBG achieved an average precision (AP) value of 93.6% for the individual tobacco plant dataset with a model parameter (Param) size of only 2.5 million (M). Compared to the YOLOv8n algorithm, the F1 (F1-score) of the improved algorithm increased by 1.7% and the AP value increased by 2%, while the model Param size was reduced by 16.7%. In practical application discovery, the occurrence of false detections and missed detections is almost minimal. In addition, the effectiveness and superiority of this method compared to other popular object detection algorithms have been confirmed. Conclusions: This article presents a novel method for high-throughput counting of tobacco leaves based on UAV image data for the first time, which has a significant reference value. It solves the problem of missing data in individual tobacco datasets, significantly reduces labor costs, and has a great impact on the advancement of modern smart tobacco agriculture.

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Estimating leaf area index of maize using UAV-based digital imagery and machine learning methods

Cited by 26 publications

References 44 publications

Physiological Responses of Indigenous Vegetable of Sintrong (Crassocephalum crepidioides) due to Exposure to High Temperature

Physiological Responses of Indigenous Vegetable of Sintrong (Crassocephalum crepidioides) due to Exposure to High Temperature

Accuracy Comparison of Estimation on Cotton Leaf and Plant Nitrogen Content Based on UAV Digital Image under Different Nutrition Treatments

YOSBG: UAV image data-driven high-throughput field tobacco leaf counting method

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