Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module

Zhao, Shengyi; Peng, Yun; Liu, Jizhan; Wu, Shuo

doi:10.3390/agriculture11070651

Cited by 111 publications

(70 citation statements)

References 25 publications

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“…Even the accuracy of the YOLO V5 model used by Mathew and Mahesh (2022) was 4.8% lower than our study. By comparing the model accuracy of the different number of disease categories, the model accuracy of Zhang et al (2020), Gao et al (2021), Sharma et al (2021), Zhao et al (2021), and Al-Wesabi et al ( 2022) is higher than our results, which is due to the smaller number of disease categories (up to 10 categories). Our study required the identification of up to 59 plant disease categories, which exceeded at least 85% of the disease categories in other studies and reduced the accuracy by up to 4.5% relative to other studies.…”

Section: Evaluation Of Model Trainingcontrasting

confidence: 87%

An Industrial-Grade Solution for Crop Disease Image Detection Tasks

Dai

Fan

2022

Front. Plant Sci.

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Crop leaf diseases can reflect the current health status of the crop, and the rapid and automatic detection of field diseases has become one of the difficulties in the process of industrialization of agriculture. In the widespread application of various machine learning techniques, recognition time consumption and accuracy remain the main challenges in moving agriculture toward industrialization. This article proposes a novel network architecture called YOLO V5-CAcT to identify crop diseases. The fast and efficient lightweight YOLO V5 is chosen as the base network. Repeated Augmentation, FocalLoss, and SmoothBCE strategies improve the model robustness and combat the positive and negative sample ratio imbalance problem. Early Stopping is used to improve the convergence of the model. We use two technical routes of model pruning, knowledge distillation and memory activation parameter compression ActNN for model training and identification under different hardware conditions. Finally, we use simplified operators with INT8 quantization for further optimization and deployment in the deep learning inference platform NCNN to form an industrial-grade solution. In addition, some samples from the Plant Village and AI Challenger datasets were applied to build our dataset. The average recognition accuracy of 94.24% was achieved in images of 59 crop disease categories for 10 crop species, with an average inference time of 1.563 ms per sample and model size of only 2 MB, reducing the model size by 88% and the inference time by 72% compared with the original model, with significant performance advantages. Therefore, this study can provide a solid theoretical basis for solving the common problems in current agricultural disease image detection. At the same time, the advantages in terms of accuracy and computational cost can meet the needs of agricultural industrialization.

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Section: Evaluation Of Model Trainingcontrasting

confidence: 87%

An Industrial-Grade Solution for Crop Disease Image Detection Tasks

Dai

Fan

2022

Front. Plant Sci.

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“…The attention mechanism not only greatly improves the efficiency and accuracy of perceptual information processing, but also provides the interpretability for the model generation process ( Niu, Zhong & Yu, 2021 ). At present, the application of AM to the field of agriculture is gradually becoming popular ( Zhao et al, 2021 ; Zhao, Huang & Nie, 2021 ). However, it has no relevant application to cultivated land quality.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of cultivated land quality using attention mechanism-back propagation neural network

Liu

et al. 2022

PeerJ Computer Science

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Cultivated land quality is related to the quality and safety of agricultural products and to ecological safety. Therefore, reasonably evaluating the quality of land, which is helpful in identifying its benefits, is crucial. However, most studies have used traditional methods to estimate cultivated land quality, and there is little research on using deep learning for this purpose. Using Ya’an cultivated land as the research object, this study constructs an evaluation system for cultivated land quality based on seven aspects, including soil organic matter and soil texture. An attention mechanism (AM) is introduced into a back propagation (BP) neural network model. Therefore, an AM-BP neural network that is suitable for Ya’an cultivated land is designed. The sample is divided into training and test sets by a ratio of 7:3. We can output the evaluation results of cultivated land quality through experiments. Furthermore, they can be visualized through a pie chart. The experimental results indicate that the model effect of the AM-BP neural network is better than that of the BP neural network. That is, the mean square error is reduced by approximately 0.0019 and the determination coefficient is increased by approximately 0.005. In addition, this study obtains better results via the ensemble model. The quality of cultivated land in Yucheng District is generally good, i.e.,mostly third and fourth grades. It conforms to the normal distribution. Lastly, the method has certain to evaluate cultivated land quality, providing a reference for future cultivated land quality evaluation.

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“…These use highly accurate methods for identifying plant disease in tomato leaves. In addition, researchers have proposed many deep learning-based solutions in disease detection and classification, as discussed below in [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ].…”

Section: Related Workmentioning

confidence: 99%

“…As a result, it can detect a broad spectrum of diseases. The model also forecasts 99.24% accuracy in tests, network complexities, and real-time adaptability [ 48 ].…”

Section: Related Workmentioning

confidence: 99%

Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network

Trivedi

Gautam

Anand

et al. 2021

Sensors

216

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Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease’s effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate.

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Tomato Leaf Disease Diagnosis Based on Improved Convolution Neural Network by Attention Module

Cited by 111 publications

References 25 publications

An Industrial-Grade Solution for Crop Disease Image Detection Tasks

An Industrial-Grade Solution for Crop Disease Image Detection Tasks

Evaluation of cultivated land quality using attention mechanism-back propagation neural network

Early Detection and Classification of Tomato Leaf Disease Using High-Performance Deep Neural Network

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