Machine Vision-Based Prediction of Lettuce Phytomorphological Descriptors using Deep Learning Networks

Lauguico, Sandy; Concepcion, Ronnie; Tobias, Rogelio Ruzcko; Alejandrino, Jonnel; Guia, Justin D. de; Guillermo, Marielet; Sybingco, Edwin; Dadios, Elmer P.

doi:10.1109/hnicem51456.2020.9400103

Cited by 13 publications

(2 citation statements)

References 22 publications

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“…In addition, there are some studies on other lettuce traits, which have similar names to the lettuce traits we studied. Lauguico et al (2020) adopted DarkNet-53 to predict modeling area and equivdiameter with R 2 values of 0.99 and 0.98. Since the modeling area and equivdiameter of this study had different concepts from the LA and D we evaluated, and the measurements used in this study were measured by image calculation rather than measured by destructive experiments, it was incompatible with our research.…”

Section: Discussionmentioning

confidence: 99%

TMSCNet: A three-stage multi-branch self-correcting trait estimation network for RGB and depth images of lettuce

Zhang

et al. 2022

Front. Plant Sci.

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Growth traits, such as fresh weight, diameter, and leaf area, are pivotal indicators of growth status and the basis for the quality evaluation of lettuce. The time-consuming, laborious and inefficient method of manually measuring the traits of lettuce is still the mainstream. In this study, a three-stage multi-branch self-correcting trait estimation network (TMSCNet) for RGB and depth images of lettuce was proposed. The TMSCNet consisted of five models, of which two master models were used to preliminarily estimate the fresh weight (FW), dry weight (DW), height (H), diameter (D), and leaf area (LA) of lettuce, and three auxiliary models realized the automatic correction of the preliminary estimation results. To compare the performance, typical convolutional neural networks (CNNs) widely adopted in botany research were used. The results showed that the estimated values of the TMSCNet fitted the measurements well, with coefficient of determination (R2) values of 0.9514, 0.9696, 0.9129, 0.8481, and 0.9495, normalized root mean square error (NRMSE) values of 15.63, 11.80, 11.40, 10.18, and 14.65% and normalized mean squared error (NMSE) value of 0.0826, which was superior to compared methods. Compared with previous studies on the estimation of lettuce traits, the performance of the TMSCNet was still better. The proposed method not only fully considered the correlation between different traits and designed a novel self-correcting structure based on this but also studied more lettuce traits than previous studies. The results indicated that the TMSCNet is an effective method to estimate the lettuce traits and will be extended to the high-throughput situation. Code is available at https://github.com/lxsfight/TMSCNet.git.

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Section: Discussionmentioning

confidence: 99%

TMSCNet: A three-stage multi-branch self-correcting trait estimation network for RGB and depth images of lettuce

Zhang

et al. 2022

Front. Plant Sci.

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“…Plant growth monitoring encompasses various areas such as length estimation at all crop growth stages as demonstrated in [ 76 , 77 ], and anomalies in plant growth in [ 78 , 82 ]. Other areas where plant growth monitoring is applied are in the prediction of Phyto-morphological descriptors as demonstrated in [ 79 ], seedling vigor rating in [ 80 ], leaf-shape estimation [ 83 ], and spike detection and segmentation in [ 81 ].…”

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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Smart approaches to Aquaponics 4.0 with focus on water quality − Comprehensive review

Chandramenon,

Aggoun,

Tchuenbou-Magaia

2024

Computers and Electronics in Agriculture

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Machine Vision-Based Prediction of Lettuce Phytomorphological Descriptors using Deep Learning Networks

Cited by 13 publications

References 22 publications

TMSCNet: A three-stage multi-branch self-correcting trait estimation network for RGB and depth images of lettuce

TMSCNet: A three-stage multi-branch self-correcting trait estimation network for RGB and depth images of lettuce

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

Smart approaches to Aquaponics 4.0 with focus on water quality − Comprehensive review

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