Plant Growth Prediction through Intelligent Embedded Sensing

Nesteruk, Sergey; Shadrin, Dmitrii; Kovalenko, Vladislav; Rodríguez-Sánchez, Antonio Jose; Somov, Andrey

doi:10.1109/isie45063.2020.9152399

Cited by 11 publications

(6 citation statements)

References 33 publications

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“…Table (b) represents the evaluation of studies focusing on bone, as cited in the following references [43,66]. Table (c) represents the evaluation of studies focusing on plant, as cited in the following references [34][35][36]40,41,47,59].…”

Section: Discussionmentioning

confidence: 99%

“…Due in part to the recent development of open software in machine learning libraries, the use of ADAM optimizer was used in 7 cases [29][30][31][32][33][34][35], Pytorch in 5 cases [29,33,[35][36][37], Numpy in 2 cases [38,39], TensorFlow in 2 cases [30,40], and OpenCV in 2 cases [41,42]. (By considering the mean square and mean of the gradient as first and second-order moments, weights can be updated on an appropriate scale for each parameter.)…”

Section: Softwarementioning

confidence: 99%

“…, where µ is the mean, σ is the standard deviation, C is a predefined constant MAPE (mean absolute percentage error) 3 [31,41,68] 1 LSTM was the most frequently selected technology, with 5, GAN and U-net with 4, and STN with 3. LSTM networks are a type of recurrent neural network called RNNs.…”

Section: Rq4: Which Mathematical Models Can Be Used To Explain the Ma...mentioning

confidence: 99%

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Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

2023

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Growth prediction technology is not only a practical application but also a crucial approach that strengthens the safety of image processing techniques. By supplementing the growth images obtained from the original images, especially in insufficient data sets, we can increase the robustness of machine learning. Therefore, predicting the growth of living organisms is an important technology that increases the safety of existing applications that target living organisms and can extend to areas not yet realized. This paper is a systematic literature review (SLR) investigating biological growth prediction based on the PRISMA 2020 guidelines. We systematically survey existing studies from 2017 to 2022 to provide other researchers with current trends. We searched four digital libraries—IEEE Xplore, ACM Digital Library, Science Direct, and Web of Science—and finally analyzed 47 articles. We summarize the methods used, year, features, accuracy, and dataset of each paper. In particular, we explained LSTM, GAN, and STN, the most frequently used methods among the 20 papers related to machine learning (40% of all papers).

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Section: Discussionmentioning

confidence: 99%

Section: Softwarementioning

confidence: 99%

Section: Rq4: Which Mathematical Models Can Be Used To Explain the Ma...mentioning

confidence: 99%

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Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

2023

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“…To improve productivity in plant cultivation, we can control the environment factors adaptively, depending on plant growth status. Sensors and monitoring systems have been used in a number of agricultural technologies to increase plant production [2,[28][29][30][31]38]. However, they require expensive equipment to measure plant growth, and the measurement also takes much time and is destructive.…”

Section: Introductionmentioning

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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“…It includes intelligent plant condition sensing, measuring responses to external impacts, treatment optimization, yield prediction, spoiling detection, etc. The main limitation of this domain is the insufficient amount of well-annotated data [23]. To overcome this issue, we present the XtremeAugment framework.…”

Section: Introductionmentioning

confidence: 99%

XtremeAugment: Getting More From Your Data Through Combination of Image Collection and Image Augmentation

et al. 2022

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Deep convolutional neural networks, being very efficient for computer vision tasks using much training data, still struggle with small training datasets. Therefore, we need a training pipeline that handles rare object types and an overall lack of training data to build well-performing models that provide stable predictions. This paper presents a comprehensive framework XtremeAugment that provides an easy, reliable, and scalable way to collect image datasets and efficiently label and augment collected data. The presented framework consists of two augmentation techniques that can be used independently and complement each other when used together. They are, namely, Hardware Dataset Augmentation (HDA) and Object-Based Augmentation (OBA). HDA is a technique that allows collecting more data and spending less time on manual data labeling. OBA significantly increases training data variability and remains the distribution of the augmented images close to the original dataset. We check the performance of the proposed approach and its independent parts on the apple spoil segmentation problem. Our results show a substantial increase in model accuracy, reaching 0.91 F1-score and outperforming the baseline model on up to 0.62 F1-score for a few-shot learning case in the wild data. The highest benefit from the XtremeAugment raises on the cases where we collect images in the controlled indoor environment but have to use the model in the wild.

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Plant Growth Prediction through Intelligent Embedded Sensing

Cited by 11 publications

References 33 publications

Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

Computer Vision Techniques for Growth Prediction: A Prisma-Based Systematic Literature Review

Shape Based Deep Estimation of Future Plant Images

XtremeAugment: Getting More From Your Data Through Combination of Image Collection and Image Augmentation

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