Flexible and high quality plant growth prediction with limited data

Abstract

Predicting plant growth is a fundamental challenge that can be employed to analyze plants and further make decisions to have healthy plants with high yields. Deep learning has recently been showing its potential to address this challenge in recent years, however, there are still two issues. First, image-based plant growth prediction is currently taken either from time series or image generation viewpoints, resulting in a flexible learning framework and clear predictions, respectively. Second, deep learning-based algorithms are notorious to require a large-scale dataset to obtain a competing performance but collecting enough data is time-consuming and expensive. To address the issues, we consider the plant growth prediction from both viewpoints with two new time-series data augmentation algorithms. To be more specific, we raise a new framework with a length-changeable time-series processing unit to generate images flexibly. A generative adversarial loss is utilized to optimize our model to obtain high-quality images. Furthermore, we first recognize three key points to perform time-series data augmentation and then put forward T-Mixup and T-Copy-Paste. T-Mixup fuses images from a different time pixel-wise while T-Copy-Paste makes new time-series images with a different background by reusing individual leaves extracted from the existing dataset. We perform our method in a public dataset and achieve superior results, such as the generated RGB images and instance masks securing an average PSNR of 27.53 and 27.62, respectively, compared to the previously best 26.55 and 26.92.

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

“…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].…”

“…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.)…”

“…This is because our study does not focus on specific machine learning techniques, making the comparison of loss functions less effective. The most commonly used loss functions in the papers reviewed in our study are L1 loss in 8 papers [31,[34][35][36][37]47,59,78], MSE in 5 papers [29,39,40,53,66], and Dice loss in 4 papers [32,33,37,74]. We also reviewed learning algorithms that do not use loss functions, such as PLS and PLSR [68,69], as well as those where the loss function is not mathematically defined but only verbally explained, as in Y-H Chang et al [42].…”

Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests

“…However, many hybrid approaches have been presented and verified by researchers to tackle this challenge. Meng et al (2022) proposed a time series data generation method that utilized good quality of plant leaf images. It recognizes three viewpoints for the growth prediction of plants and proposes two new time series data generation algorithms (T-copy-paste and Tmixup).…”

“…However, many hybrid approaches have been presented and verified by researchers to tackle this challenge. Meng et al. (2022) proposed a time series data generation method that utilized good quality of plant leaf images.…”