VGNet: A Lightweight Intelligent Learning Method for Corn Diseases Recognition

Abstract

The automatic recognition of crop diseases based on visual perception algorithms is one of the important research directions in the current prevention and control of crop diseases. However, there are two issues to be addressed in corn disease identification: (1) A lack of multicategory corn disease image datasets that can be used for disease recognition model training. (2) The existing methods for identifying corn diseases have difficulty satisfying the dual requirements of disease recognition speed and accuracy in actual corn planting scenarios. Therefore, a corn diseases recognition system based on pretrained VGG16 is investigated and devised, termed as VGNet, which consists of batch normalization (BN), global average pooling (GAP) and L2 normalization. The performance of the proposed method is improved by using transfer learning for the task of corn disease classification. Experiment results show that the Adam optimizer is more suitable for crop disease recognition than the stochastic gradient descent (SGD) algorithm. When the learning rate is 0.001, the model performance reaches a highest accuracy of 98.3% and a lowest loss of 0.035. After data augmentation, the precision of nine corn diseases is between 98.1% and 100%, and the recall value ranges from 98.6% to 100%. What is more, the designed lightweight VGNet only occupies 79.5 MB of space, and the testing time for 230 images is 75.21 s, which demonstrates better transferability and accuracy in crop disease image recognition.

A robust and light-weight transfer learning-based architecture for accurate detection of leaf diseases across multiple plants using less amount of images

“…In experiments with citrus, cucumber, grapes, and tomato, the suggested model outperformed all other models with an accuracy of 93.56%, 92.34%, 99.50%, and 96.56%, respectively, under difficult background conditions. Fan and Guan (2023) developed VGNet, a corn disease recognition system based on pre-trained VGG16, with batch normalization (BN), global average pooling (GAP), and L2 normalization. Transfer learning for corn disease categorization improves the proposed strategy.…”

“…The application of deep learning approaches ( Mustak Un Nobi et al., 2023 ) to identify plant diseases has emerged as a pivotal component in the observation and evaluation of the production of distinct plant species. The rapid advancement in high-performance computing and image processing components has enabled the effective utilization of deep learning techniques in diverse domains ( Fan and Guan, 2023 ). These methods ( Bajpai et al., 2023 ) have proven highly proficient in uncovering complex structures within high-dimensional data, making them applicable to a wide array of fields, including science, engineering, industry, bioinformatics, and agriculture ( Pal and Kumar, 2023 ).…”

Enhanced Sea Horse Optimization Algorithm for Hyperparameter Optimization of Agricultural Image Recognition

“…In corn disease recognition, the accuracy of AT-AlexNet was significantly higher than other models. Fan et al studied and designed a corn disease recognition system VGNet based on pretrained VGG16 [2]. The experimental results show that the performance of the proposed model is significantly better than other models.…”

Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring

“…The machine learning methods SVM, DT, and KNN were tested and compared. Fan et al [11] developed a VGNet with the backbone set as VGG16, with the ability to improve the recognition of corn with poor health in fields. In particular, there was a 3.5% enhancement in the accuracy of the proposed VGNet compared to its predecessor VGG16.…”