# Yielding Multi-Fold Training Strategy for Image Classification of Imbalanced Weeds  
[PERSON]  
1 Department of ICT Convergence System Engineering, Chonnam National University, Gwangju 61186, Korea; [EMAIL_ADDRESS] (V.H.T. ); [EMAIL_ADDRESS] (Y.G.H.) [PERSON]  
1 Department of ICT Convergence System Engineering, Chonnam National University, Gwangju 61186, Korea; [EMAIL_ADDRESS] (V.H.T. ); [EMAIL_ADDRESS] (Y.G.H.) [PERSON]  
1 Department of ICT Convergence System Engineering, Chonnam National University, Gwangju 61186, Korea; [EMAIL_ADDRESS] (V.H.T. ); [EMAIL_ADDRESS] (Y.G.H.) [PERSON]  
2 Faculty of Information Technology, Saigon University, Ho Chi Minh City 72710, Vietnam 22 Faculty of Information Technology, Saigon University, Ho Chi Minh City  
as those using machine learning techniques [17; 18], DNNs [19; 20], generative adversarial networks (GANs) [21], or reinforcement learning (RL) [22]. Many approaches using conventional training of DNN models to solve the weeds classification problem using imbalanced weeds datasets, such as those of [PERSON] et al. [23], who aggregated multiple DNN models and used the CNU Weeds dataset. The authors of [24; 25; 26; 27; 28; 29; 30; 31; 32] developed and proposed novel models and techniques and applied them to the PlantSeedlings and PlantVillage datasets. In reality, weeds image datasets may have non-uniform distributions due to the relative prevalence of various species, some of which are common and diverse, while others are rare and identical. Hence, training a DNN model by selecting a batch of random samples from an entire training set makes the model robust to majority species, but sensitive to minority species, as [PERSON] and [PERSON] pointed out in their survey [33]. To reduce this bias, we proposed the yielding multi-fold training (YMufT) strategy, which trains the DNN model by arbitrarily dividing the training data into multiple folds and then trains the model on each of these folds consecutively. Furthermore, samples of minority species are trained more often than those of majority species so that the model pays more attention to minority species. The MCMB procedure assures this specification to determine the number of samples of each species selected and passed on each consecutive fold. In addition, we propose a formula to determine the number of training loops and training periods in YMufT. This formula aims to ensure that the number of loading training images in the YMufT strategy is approximately the same as, or less than, that in conventional training methods. We explore our strategy on four weeds datasets, containing two large datasets (CNU and large PlantVillage) and two small datasets (PlantSeedlings and small PlantVillage). Both types of datasets have plain and in-the-wild weeds datasets. Experiments are done using three DNN models (Mobilenet, Resnet, and NASNet mobile) and the results are compared to those of the conventional training methods. We find that YMufT runs faster during the training period and is quicker to converge when training on the validation set, even though both approaches have the same training configurations and approximate training steps. The evaluation shows that training the DNN model using the YMufT strategy results in comparable performance to conventional training strategies on large datasets and higher performance on small datasets. Specifically, YMufT increases the F1 score in the NASNet model to 0.9708 on the CNU dataset and 0.9928 by using Mobilenet model training on large PlantVillage dataset. On the small PlantVillage dataset, Mobilenet achieves the highest performance of 0.9981 in accuracy and 0.9970 in F1 score. On the PlantSeedlings dataset, Resnet shows the highest accuracy (0.9718) and F1 score (0.9689). Grad-CAM visualization shows that YMufT guides the model to capture essential features of the leaf surface and correctly localize weeds targets. Conventional training methods mainly concentrate on high-level features and may capture insignificant features.