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In this paper, we addressed the challenges in sorting high-yield apple cultivars that traditionally relied on manual labor or system-based defect detection. Existing single-camera methods failed to uniformly capture the entire surface of apples, potentially leading to misclassification due to defects in unscanned areas. Various methods were proposed where apples were rotated using rollers on a conveyor. However, since the rotation was highly random, it was difficult to scan the apples uniformly for accurate classification. To overcome these limitations, we proposed a multi-camera-based apple sorting system with a rotation mechanism that ensured uniform and accurate surface imaging. The proposed system applied a rotation mechanism to individual apples while simultaneously utilizing three cameras to capture the entire surface of the apples. This method offered the advantage of quickly and uniformly acquiring the entire surface compared to single-camera and random rotation conveyor setups. The images captured by the system were analyzed using a CNN classifier deployed on embedded hardware. To maintain excellent CNN classifier performance while reducing its size and inference time, we employed knowledge distillation techniques. The CNN classifier demonstrated an inference speed of 0.069 s and an accuracy of 93.83% based on 300 apple samples. The integrated system, which included the proposed rotation mechanism and multi-camera setup, took a total of 2.84 s to sort one apple. Our proposed system provided an efficient and precise solution for detecting defects on the entire surface of apples, improving the sorting process with high reliability.

An imbalanced dataset is a significant challenge when training a deep neural network (DNN) model for deep learning problems, such as weeds classification. An imbalanced dataset may result in a model that behaves robustly on major classes and is overly sensitive to minor classes. This article proposes a yielding multi-fold training (YMufT) strategy to train a DNN model on an imbalanced dataset. This strategy reduces the bias in training through a min-class-max-bound procedure (MCMB), which divides samples in the training set into multiple folds. The model is consecutively trained on each one of these folds. In practice, we experiment with our proposed strategy on two small (PlantSeedlings, small PlantVillage) and two large (Chonnam National University (CNU), large PlantVillage) weeds datasets. With the same training configurations and approximate training steps used in conventional training methods, YMufT helps the DNN model to converge faster, thus requiring less training time. Despite a slight decrease in accuracy on the large dataset, YMufT increases the F1 score in the NASNet model to 0.9708 on the CNU dataset and 0.9928 when using the Mobilenet model training on the large PlantVillage dataset. YMufT shows outstanding performance in both accuracy and F1 score on small datasets, with values of (0.9981, 0.9970) using the Mobilenet model for training on small PlantVillage dataset and (0.9718, 0.9689) using Resnet to train on the PlantSeedlings dataset. Grad-CAM visualization shows that conventional training methods mainly concentrate on high-level features and may capture insignificant features. In contrast, YMufT guides the model to capture essential features on the leaf surface and properly localize the weeds targets.

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