Fine-grained image classification methods often suffer from the challenge that the subordinate categories within an entry-level category can only be distinguished by subtle differences. Crop disease classification is affected by various visual interferences, including uneven illumination, dew, and equipment jitter. It demands an effective algorithm to accurately discriminate one category from the others. Thus, the representational ability of algorithm needs to be strengthened to learn a robust domain-specific discrimination through an effective way. To address this challenge, a unified convolutional neural network (CNN) denoting the matrix-based convolutional neural network (M-bCNN) was proposed. Its hallmark is the convolutional kernel matrix, whose convolutional layers are arranged parallelly in the form of a matrix, and integrated with DropConnect, exponential linear unit, local response normalization, and so on to defeat over-fitting and vanishing gradient. With a tolerable addition of parameters, it can effectively increase the data streams, neurons, and link channels of the model compared with the commonly used plain networks. Therefore, it will create more non-linear mappings and will enhance the representational ability with a tolerable growth of parameters. The images of winter wheat leaf diseases were utilized as experimental samples for their strong similarities among sub-categories. A total of 16652 images containing eight categories were collected from Shandong Province, China, and were augmented into 83260 images. The M-bCNN delivered significant improvements and achieved an average validation accuracy of 96.5% and a testing accuracy of 90.1%; this outperformed AlexNet and VGG-16. The M-bCNN demonstrated accuracy gains with a convolutional kernel matrix in fine-grained image classification.

Maturity level based classification system plays an essential role in the design of tomato harvesting robot. Traditional knowledge based systems are unable to meet the current production management requirements of precision picking, because they are time-consuming and have low accuracy. Our research proposes an improved deep learning based classification method that improves accuracy and scalability of tomato ripeness with a small amount of training data. This study was on the relationship of different dataset augmentation methods and prediction results of final classification task. We implemented classification systems based on convolutional neural network (CNN), by training and validating the model on different augmented datasets and tried to choose an optimal augmentation method for datasets. The experimental results showed an average accuracy of 91.9% with a less than 0.01 second prediction time. Compared to existing methods, our solution achieved better prediction results both in terms of accuracy and time consumption. Moreover, this is a versatile method and can be extended to other related fields.

Effective and efficient fruit detection is considered crucial for designing automated robot (AuRo) for yield estimation, disease control, harvesting, sorting, and grading. Several fruit detection schemes for designing AuRo have been developed during the last decades. However, conventional fruit detection methods are deficient in the real-time response, accuracy, and extensibility. This paper proposes an improved multi-task cascaded convolutional network-based intelligent fruit detection method. This method has the capability to make the AuRo work in real time with high accuracy. Moreover, based on the relationship between the diversity samples of the dataset and the parameters of neural networks' evolution, this paper presents an improved augmented method, a procedure that is based on image fusion to improve the detector performance. The experiment results demonstrated that the proposed detector performed immaculately both in terms of accuracy and time-cost. Furthermore, the extensive experiment also demonstrated that the proposed technique has the capacity and good portability to work with other akin objects conveniently.

Deep learning architecture has achieved amazing success in many areas with the recent advancements in convolutional neural networks (CNNs). However, real-time applications of CNNs are seriously hindered by the significant storage and computational costs. Structured pruning is a promising method to compress and accelerate CNNs and does not need special hardware or software for an auxiliary calculation. Here a simple strategy of structured pruning approach is proposed to crop unimportant filters or neurons automatically during the training stage. The proposed method introduces a mask for all filters or neurons to evaluate their importance. Thus the filters or neurons with zero mask are removed. To achieve this, the proposed method adopted L1 regularization to zero filters or neurons of CNNs. Experiments were conducted to assess the validity of this technique. The experiments showed that the proposed approach could crop 90.4%, 95.6% and 34.04% parameters on LeNet-5, VGG-16, and ResNet-32respectively, with a negligible loss of accuracy.