YOLO-VOLO-LS: A Novel Method for Variety Identification of Early Lettuce Seedlings

Zhang, Pan; Li, Bingbing

doi:10.3389/fpls.2022.806878

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…Other applications of SSD include detecting oyster mushrooms in [ 39 ] and sweet pepper in [ 49 ]. Another DL model called You Only Look Once (YOLO) with different modifications has been utilized in some of the reviewed papers for crop yield estimation as demonstrated in [ 36 , 41 , 46 , 47 , 51 , 52 , 53 ]. As described in [ 40 , 42 , 45 , 48 , 50 , 61 ], R-CNN models such as Mask-RCNN and Faster-RCNN, two of the most widely used DL models, are used in crop yield prediction applications, especially for tomato and strawberry.…”

Section: Deep Learning In Ceamentioning

confidence: 99%

Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

Ojo

Zahid

2022

Sensors

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Controlled environment agriculture (CEA) is an unconventional production system that is resource efficient, uses less space, and produces higher yields. Deep learning (DL) has recently been introduced in CEA for different applications including crop monitoring, detecting biotic and abiotic stresses, irrigation, microclimate prediction, energy efficient controls, and crop growth prediction. However, no review study assess DL’s state of the art to solve diverse problems in CEA. To fill this gap, we systematically reviewed DL methods applied to CEA. The review framework was established by following a series of inclusion and exclusion criteria. After extensive screening, we reviewed a total of 72 studies to extract the useful information. The key contributions of this article are the following: an overview of DL applications in different CEA facilities, including greenhouse, plant factory, and vertical farm, is presented. We found that majority of the studies are focused on DL applications in greenhouses (82%), with the primary application as yield estimation (31%) and growth monitoring (21%). We also analyzed commonly used DL models, evaluation parameters, and optimizers in CEA production. From the analysis, we found that convolutional neural network (CNN) is the most widely used DL model (79%), Adaptive Moment Estimation (Adam) is the widely used optimizer (53%), and accuracy is the widely used evaluation parameter (21%). Interestingly, all studies focused on DL for the microclimate of CEA used RMSE as a model evaluation parameter. In the end, we also discussed the current challenges and future research directions in this domain.

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Section: Deep Learning In Ceamentioning

confidence: 99%

Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

Ojo

Zahid

2022

Sensors

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WS-YOLO: An Agronomical and Computer Vision-Based Framework to Detect Drought Stress in Lettuce Seedlings Using IR Imaging and YOLOv8

Wolter-Salas,

Canessa,

Campos-Vargas

et al. 2023

Communications in Computer and Information Science

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The Selection of Lettuce Seedlings for Transplanting in a Plant Factory by a Non-Destructive Estimation of Leaf Area and Fresh Weight

Jeong,

Ha,

Kwack

2024

Horticulturae

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Selecting uniform and healthy seedlings is important to ensure that a certain level of production can be reliably achieved in a plant factory. The objectives of this study were to investigate the potential of non-destructive image analysis for predicting the leaf area and shoot fresh weight of lettuce and to determine the feasibility of using a simple image analysis to select robust seedlings that can produce a uniform and dependable yield of lettuce in a plant factory. To vary the range of the leaf area and shoot fresh weight of lettuce seedlings, we applied two- and three-day irrigation intervals during the period of seedling production and calculated the projected canopy size (PCS) from the top-view images of the lettuce seedlings, although there were no significant growth differences between the irrigation regimes. A high correlation was identified between the PCS and shoot fresh weight for the lettuce seedlings during the period of seedling production, with a coefficient of determination exceeding 0.8. Therefore, the lettuce seedlings were classified into four grades (A–D) based on their PCS values calculated at transplanting. In the early stages of cultivation after transplanting, there were differences in the lettuce growth among the four grades; however, at the harvest (28 days after transplanting), there was no significant difference in the lettuce yield between grades A–C, with the exception of grade D. The lettuce seedlings in grades A–C exhibited the anticipated yield (150 g/plant) at the harvest time. In the correlation between the PCS and leaf area or the shoot fresh weight of lettuce during the cultivation period after transplanting and the entire cultivation period, the R2 values were higher than 0.9, confirming that PCS can be used to predict lettuce growth with greater accuracy. In conclusion, we demonstrated that the PCS calculation from the top-view images, a straightforward image analysis technique, can be employed to non-destructively and accurately predict lettuce leaf area and shoot fresh weight, and the seedlings with the potential to yield above a certain level after transplanting can be objectively and accurately selected based on PCS.

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YOLO-VOLO-LS: A Novel Method for Variety Identification of Early Lettuce Seedlings

Cited by 3 publications

References 40 publications

Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects

WS-YOLO: An Agronomical and Computer Vision-Based Framework to Detect Drought Stress in Lettuce Seedlings Using IR Imaging and YOLOv8

The Selection of Lettuce Seedlings for Transplanting in a Plant Factory by a Non-Destructive Estimation of Leaf Area and Fresh Weight

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