Identification of Apple Leaf Diseases Based on Improved Convolutional Neural Network

LI, Lili; Wang, Bin; Hu, Zhiwei

doi:10.35633/inmateh-67-54

Cited by 7 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wan, J. et al [33] used transfer learning and GoogLeNet to achieve fruit tree disease identification and severity grading. Bao, W. et al [34] introduced selective convolution in VGG16 to extract small disease features and used transfer learning to train the model, solving the problem of identifying small apple diseases.…”

Section: Related Workmentioning

confidence: 99%

Plant Diseased Lesion Image Segmentation and Recognition Based on Improved Multi-Scale Attention Net

Yang,

Wang,

Lian

2024

Applied Sciences

View full text Add to dashboard Cite

Fallen leaf disease can lead to a decrease in leaf area, a decrease in photosynthetic products, insufficient accumulation of fruit sugar, poor coloring and flavor, and a large number of fruits developing sunburn. To address the aforementioned issue, this article introduces a deep learning algorithm designed for the segmentation and recognition of agricultural disease images, particularly those involving leaf lesions. The essence of this algorithm lies in enhancing the Multi-scale Attention Net (MA-Net) encoder and attention mechanism to improve the model’s performance when processing agricultural disease images. Firstly, an analysis was conducted on MA-Net, and its limitations were identified. Compared to res-block, Mix Vision Transformer (MiT) consumes relatively less time during the training process, can better capture global and contextual information in images, and has better robustness and scalability. Then, the feature extraction parts of different networks were used as encoders to join the MA-Net network. Compared to a Position-wise Attention Block (PAB), which has higher computational complexity and requires a larger amount of computing resources, Effective Channel Attention net (ECANet) reduces the number of model parameters and computation by learning the correlation between channels, as well as having a better denoising ability. The experimental results show that the proposed solution has high accuracy and stability in agricultural disease image segmentation and recognition. The mean Intersection over Union (mIoU) is 98.1%, which is 0.2% higher than traditional MA-Net; Dice Loss is 0.9%, which is 0.1% lower than traditional MA-Net.

show abstract

Section: Related Workmentioning

confidence: 99%

Plant Diseased Lesion Image Segmentation and Recognition Based on Improved Multi-Scale Attention Net

Yang,

Wang,

Lian

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Therefore, it is necessary to develop variable selection methods that can meet the demand for AGB estimation under different forest cover conditions. To further improve the accuracy of AGB estimation and reduce the error transfer in the modeling process, we can select other machine learning methods that are suitable for processing multidimensional data, such as using K-nearest neighbors (KNN) [43] or deep learning [56] for further testing. In addition, we can try to use the L-M algorithm [57] for random forest hyper parameter tuning, which can reduce the number of iterations in the optimization process and make full use of the information from each test point [58].…”

Section: Effect Of Model Selection and Optimization On Estimation Acc...mentioning

confidence: 99%

Estimation of Quercus Biomass in Shangri-La Based on GEDI Spaceborne Lidar Data

Shu

et al. 2023

Forests

View full text Add to dashboard Cite

Accurately estimating forest biomass based on spaceborne lidar on a county scale is challenging due to the incomplete coverage of spaceborne lidar data. Therefore, this research aims to interpolate GEDI spots and explore the feasibility of approaches to improving Quercus forest biomass estimation accuracy in the alpine mountains of Yunnan Province, China. This paper uses GEDI data as the main information source and a typical mountainous area in Shangri-La, northwestern Yunnan Province, China, as the study area. Based on the pre-processing of light spots. A total of 38 parameters were extracted from the canopy and vertical profiles of 1307 light spots in the study area, and the polygon data of the whole study area were obtained from the light spot data through Kriging interpolation. Multiple linear regression, support vector regression, and random forest were used to establish biomass models. The results showed that the optimal model is selected using the semi-variance function for the Kriging interpolation of each parameter of GEDI spot, the optimal model of modis_nonvegetated is a linear model, and the optimal model for rv, sensitivity, and modis_treecover is the exponential model. Analysis of the correlation between 39 parameters extracted from GEDI L2B and three topographic factors with oak biomass showed that sensitivity had a highly significant positive correlation (p < 0.01) with Quercus biomass, followed by a significant negative correlation (p < 0.05) with aspect and modis_nonvegation. After variable selection, the estimation model of Quercus biomass established using random forest had R2 = 0.91, RMSE = 19.76 t/hm2, and the estimation accuracy was better than that of multiple linear regression and support vector regression. The estimated total biomass of Quercus in the study area was mainly distributed between 26.48 and 257.63 t/hm2, with an average value of 114.33 t/hm2 and a total biomass of about 1.26 × 107 t/hm2. This study obtained spatial consecutive information using Kriging interpolation. It provided a new research direction for estimating other forest structural parameters using GEDI data.

show abstract

“…We compared our method with the other conventional deep CNN architectures including ResNet-50 [38], DenseNet-121 [39], EffecientNet-b3 [40], ShuffleNet-v2 [41] and MobileNet-v3 [42]. Each of them was proved to be efficient in tomato disease classification.…”

Section: Accuracy Performancementioning

confidence: 99%

MLFAnet: A Tomato Disease Classification Method Focusing on OOD Generalization

Yin

Zhao

et al. 2023

Agriculture

View full text Add to dashboard Cite

Tomato disease classification based on images of leaves has received wide attention recently. As one of the best tomato disease classification methods, the convolutional neural network (CNN) has an immense impact due to its impressive performance. However, better performance is verified by independent identical distribution (IID) samples of tomato disease, which breaks down dramatically on out-of-distribution (OOD) classification tasks. In this paper, we investigated the corruption shifts, which was a vital component of OOD, and proposed a tomato disease classification method to improve the performance of corruption shift generalization. We first adopted discrete cosine transform (DCT) to obtain the low-frequency components. Then, the weight of the feature map was calculated by multiple low-frequency components, in order to reduce the influence of high-frequency variation caused by corrupted perturbation. The proposed method, termed as a multiple low-frequency attention network (MLFAnet), was verified by the benchmarking of ImageNet-C. The accuracy result and generalization performance confirmed the effectiveness of MLFAnet. The satisfactory generalization performance of our proposed classification method provides a reliable tool for the diagnosis of tomato disease.

show abstract

Identification of Apple Leaf Diseases Based on Improved Convolutional Neural Network

Cited by 7 publications

References 13 publications

Plant Diseased Lesion Image Segmentation and Recognition Based on Improved Multi-Scale Attention Net

Plant Diseased Lesion Image Segmentation and Recognition Based on Improved Multi-Scale Attention Net

Estimation of Quercus Biomass in Shangri-La Based on GEDI Spaceborne Lidar Data

MLFAnet: A Tomato Disease Classification Method Focusing on OOD Generalization

Contact Info

Product

Resources

About