Leaf-Movement-Based Growth Prediction Model Using Optical Flow Analysis and Machine Learning in Plant Factory

Nagano, Shogo; Moriyuki, Shogo; Wakamori, Kazumasa; Mineno, Hiroshi; Fukuda, Hirokazu

doi:10.3389/fpls.2019.00227

Cited by 34 publications

(26 citation statements)

References 33 publications

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“…Fieldscan from Phenospex) or aerial platforms ( Wang et al, 2019 ; Stevens et al, 2020 ). The output of camera systems can be used to estimate growth and motion-related traits ( Binder et al, 2006 ; Müller and Jiménez-Gómez, 2016 ; Nagano et al, 2019 ). However, plant movements and camera orientation are not always optimally aligned.…”

Section: Introductionmentioning

confidence: 99%

A digital sensor to measure real-time leaf movements and detect abiotic stress in plants

et al. 2021

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Plant and plant organ movements are the result of a complex integration of endogenous growth and developmental responses, partially controlled by the circadian clock, and external environmental cues. Monitoring of plant motion is typically done by image-based phenotyping techniques with the aid of computer vision algorithms. Here we present a method to measure leaf movements using a digital inertial measurement unit (IMU) sensor. The lightweight sensor is easily attachable to a leaf or plant organ and records angular traits in real-time for two dimensions (pitch and roll) with high resolution (measured sensor oscillations of 0.36° ± 0.53° for pitch and 0.50° ± 0.65° for roll). We were able to record simple movements such as petiole bending, as well as complex lamina motions, in several crops, ranging from tomato to banana. We also assessed growth responses in terms of lettuce rosette expansion and maize seedling stem movements. The IMU sensors are capable of detecting small changes of nutations (i.e., bending movements) in leaves of different ages and in different plant species. In addition, the sensor system can also monitor stress-induced leaf movements. We observed that unfavorable environmental conditions evoke certain leaf movements, such as drastic epinastic responses, as well as subtle fading of the amplitude of nutations. In summary, the presented digital sensor system enables continuous detection of a variety of leaf motions with high precision, and is a low-cost tool in the field of plant phenotyping, with potential applications in early stress detection.

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Section: Introductionmentioning

confidence: 99%

A digital sensor to measure real-time leaf movements and detect abiotic stress in plants

et al. 2021

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“…Though phenotype information, such as the leaf area index, has been used for plant status ( Wang et al, 2017 ) in CEA, the estimated FW provides better plant status information and serves as a good yield indicator ( Marondedze et al, 2018 ). In a plant factory setting, accurate yield prediction was performed with early time-series phenotyping data in lettuce ( Nagano et al, 2019 ). We tested a model plant in the CEA for growth forecast with a limited time window and it yielded an accurate result ( Figure 6B ).…”

Section: Discussionmentioning

confidence: 99%

Time-Series Growth Prediction Model Based on U-Net and Machine Learning in Arabidopsis

et al. 2021

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Yield prediction for crops is essential information for food security. A high-throughput phenotyping platform (HTPP) generates the data of the complete life cycle of a plant. However, the data are rarely used for yield prediction because of the lack of quality image analysis methods, yield data associated with HTPP, and the time-series analysis method for yield prediction. To overcome limitations, this study employed multiple deep learning (DL) networks to extract high-quality HTTP data, establish an association between HTTP data and the yield performance of crops, and select essential time intervals using machine learning (ML). The images of Arabidopsis were taken 12 times under environmentally controlled HTPP over 23 days after sowing (DAS). First, the features from images were extracted using DL network U-Net with SE-ResXt101 encoder and divided into early (15–21 DAS) and late (∼21–23 DAS) pre-flowering developmental stages using the physiological characteristics of the Arabidopsis plant. Second, the late pre-flowering stage at 23 DAS can be predicted using the ML algorithm XGBoost, based only on a portion of the early pre-flowering stage (17–21 DAS). This was confirmed using an additional biological experiment (P < 0.01). Finally, the projected area (PA) was estimated into fresh weight (FW), and the correlation coefficient between FW and predicted FW was calculated as 0.85. This was the first study that analyzed time-series data to predict the FW of related but different developmental stages and predict the PA. The results of this study were informative and enabled the understanding of the FW of Arabidopsis or yield of leafy plants and total biomass consumed in vertical farming. Moreover, this study highlighted the reduction of time-series data for examining interesting traits and future application of time-series analysis in various HTPPs.

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“…On the other hand, there are studies that measure the features or contours of individual leaves rather than the overall appearance of plants and utilize them for growth prediction or recognition [21][22]. In another aspect, a method of predicting growth has been studied by focusing on the prediction of indicators such as live weight rather than the appearance of plants [23].…”

Section: B Plant Image Predictionmentioning

confidence: 99%

Shape Based Deep Estimation of Future Plant Images

et al. 2022

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Plants exhibit dynamic changes as they grow. For example, a new leaf may appear suddenly, and rotate and fold over time. Therefore, it is difficult to predict the growth of plants. For accurate growth predictions, it is important to predict the shapes, colors and textures of the leaves. The conventional methods simply use RGB images to predict the next plant at once. In this paper, we propose a novel deep network which is divided into two subnets of shape estimation and color reconstruction. Four gray time-series images are first aligned to a future target using a spatial transformer network (STN) for shape estimation. They are then fused using U-Net with two LSTMs to generate a future shape image. The color reconstruction subnet fuses the predicted shape with a RGB plant image to restore the color information. In addition, we use gray images with texture information for shape estimation instead of binary images with only simple information and RGB images with too much information. The proposed deep network can robustly generate future plant images for plant growth prediction. It is evaluated using our proprietary dataset as well as two public datasets for different types of plants. The experimental results demonstrate that our proposed network predicts the leaf shape more accurately and restores RGB. As a result, our method can create accurate future plant images.

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Leaf-Movement-Based Growth Prediction Model Using Optical Flow Analysis and Machine Learning in Plant Factory

Cited by 34 publications

References 33 publications

A digital sensor to measure real-time leaf movements and detect abiotic stress in plants

A digital sensor to measure real-time leaf movements and detect abiotic stress in plants

Time-Series Growth Prediction Model Based on U-Net and Machine Learning in Arabidopsis

Shape Based Deep Estimation of Future Plant Images

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