Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques

Wang, Qimei; Feng, Qinghua; Sun, Minghe; Qu, Jianhua; Xue, Jie

doi:10.1155/2019/9142753

Cited by 130 publications

(60 citation statements)

References 25 publications

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“…Also, some studies combine the Mask R-CNN framework with object detection networks for plant diseases and pests detection. Wang et al [ 83 ] used two different models, Faster R-CNN and ask R-CNN, in which Faster R-CNN was used to identify the class of tomato diseases and Mask R-CNN was used to detect and segment the location and shape of the infected area. The results showed that the proposed model can quickly and accurately identify 11 class of tomato diseases, and divide the location and shape of infected areas.…”

Section: Plant Diseases and Pests Detection Methods Based On Deep Leamentioning

confidence: 99%

Plant diseases and pests detection based on deep learning: a review

2021

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Plant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study plant diseases and pests identification has become a research issue of great concern to researchers. This review provides a definition of plant diseases and pests detection problem, puts forward a comparison with traditional plant diseases and pests detection methods. According to the difference of network structure, this study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized. Common datasets are introduced, and the performance of existing studies is compared. On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.

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Section: Plant Diseases and Pests Detection Methods Based On Deep Leamentioning

confidence: 99%

Plant diseases and pests detection based on deep learning: a review

2021

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“…In Table III, a comparison between these models and the proposed model based on average test accuracies is provided for the tomato dataset. For a fair comparison, other implementations in the literature using custom tomato datasets [12][11] [22] were not adopted for this exercise. It can be seen from Table III that the proposed model produced results that are comparable to the state-of-the-art models irrespective of the complexity of the input images.…”

Section: Comparison To Related Work In Literaturementioning

confidence: 99%

“…Wang et al [22] collected sick tomato leaf images from the internet and trained a region-based CNN (R-CNN) to detect disease types and areas of infection. Their networks were so deep that ResNet-101 obtained 23.25 hours of training time.…”

Section: Introductionmentioning

confidence: 99%

Gabor Capsule Network for Plant Disease Detection

Mensah¹,

Weyori²,

Ayidzoe³

2020

IJACSA

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Crop diseases contribute significantly to food insecurity, malnutrition, and poverty in Africa where the majority of the population is into Agriculture. Manual plant disease recognition methods are widespread but limited, ineffective, costly, and time-consuming making the need to search for automatic and efficient methods of recognition more crucial. Machine learning and Convolutional Neural Networks have been applied in other jurisdictions in an attempt to solve these problems. They have achieved impressive results in this domain but tend to be 'data-hungry', invariant, and vulnerable to attacks that can easily lead to misclassifications. Capsule Networks, on the other hand, avoids the weaknesses of CNNs and has not been widely used in this area. This article, therefore, proposes the use of Gabor and Capsule network to recognize blurred, deformed, and unseen tomato and citrus disease images. Experimental results show that the proposed model can achieve a 98.13% test accuracy which is comparable to the performance of state-of-theart CNN models in the literature. Also, the proposed model outperformed two state-of-the-art deep learning models (which were implemented as baseline models) in terms of robustness, flexibility, fast converges, and having fewer parameters. This work can be extended to other crops and may well serve as a useful tool for the recognition of unseen plant diseases under bad weather and bad illumination conditions.

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“…ANNs allow us to develop models based on the intrinsic relations among the variables, without prior knowledge of their functional relationships [9]. Soft computing for ANN techniques has been widely used to develop models to predict different crop indicators, such as growth, yield, and other biophysical processes, and also because of the commercial importance of tomato [10][11][12][13][14][15][16][17][18][19][20][21][22][23] and other vegetables, such as lettuce [24][25][26][27][28][29][30], pepper [31][32][33][34], cucumber [35][36][37][38], wheat [39][40][41][42][43][44][45], rice [46][47][48], oat [49], maize [50,51], corn [52][53][54], corn and soybean [55], soybean…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter

et al. 2020

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Non-linear systems, such as biological systems, can be simulated by artificial neural network (ANN) techniques. This research aims to use ANN to simulate the accumulated aerial dry matter (leaf, stem, and fruit) and fresh fruit yield of a tomato crop. Two feed-forward backpropagation ANNs, with three hidden layers, were trained and validated by the Levenberg–Marquardt algorithm for weights and bias adjusted. The input layer consisted of the leaf area, plant height, fruit number, dry matter of leaves, stems and fruits, and the growth degree-days at 136 days after transplanting (DAT); these were obtained from a tomato crop, a hybrid, EL CID F1, with indeterminate growth habits, grown with a mixture of peat moss and perlite 1:1 (v/v) (substrate) and calcareous soil (soil). Based on the experimentation of the ANNs with one, two and three hidden layers, with MSE values less than 1.55, 0.94 and 0.49, respectively, the ANN with three hidden layers was chosen. The 7-10-7-5-2 and 7-10-8-5-2 topologies showed the best performance for the substrate (R = 0.97, MSE = 0.107, error = 12.06%) and soil (R = 0.94, MSE = 0.049, error = 13.65%), respectively. These topologies correctly simulated the aerial dry matter and the fresh fruit yield of the studied tomato crop.

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Identification of Tomato Disease Types and Detection of Infected Areas Based on Deep Convolutional Neural Networks and Object Detection Techniques

Cited by 130 publications

References 25 publications

Plant diseases and pests detection based on deep learning: a review

Plant diseases and pests detection based on deep learning: a review

Gabor Capsule Network for Plant Disease Detection

Artificial Neural Network Modeling of Greenhouse Tomato Yield and Aerial Dry Matter

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