Deep Learning for Plant Diseases: Detection and Saliency Map Visualisation

Brahimi, Méziane; Arsenović, Marko; Laraba, Sohaïb; Sladojević, Srdjan; Boukhalfa, Kamel; Moussaoui, Abdelouahab

doi:10.1007/978-3-319-90403-0_6

Cited by 187 publications

(129 citation statements)

References 25 publications

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“…Diversity of applications and the remarkable results of studies regarding DL have encouraged researchers to also apply it in the agricultural field. Thereafter, many papers on applying DL techniques for plant disease detection have been published, and some of these [7,8,[31][32][33][34][35][36][37][38][39][40][41][42][43] are presented below.…”

Section: Related Workmentioning

confidence: 99%

“…If there is an insufficient amount of data, transfer learning may be useful [13][14][15]25,[36][37][38][39]45]. Authors of [36] proved that this method could be useful by training a modified LeNet on the PlantVillage dataset and then on the dataset they collected.…”

Section: Related Workmentioning

confidence: 99%

“…The authors suggested that results could be improved by database enrichment. Transfer learning was part of the experiment described in [38], where it was applied after preprocessing and augmentation of the images from the PlantVillage dataset. Here, six state-of-the-art architectures (AlexNet, DenseNet-169, Inception v3, ResNet-34, SqueezeNet-1.1, and VGG13) and three different training strategies were compared.…”

Section: Related Workmentioning

confidence: 99%

“…Extensive research has shown no evidence of a paper where all of these issues were addressed. Based on our previous work [37,38] and motivated by the current limitations of the available plant disease detection models, a novel two-stage network algorithm is proposed, which combines advanced techniques in an effort to accurately identify plant diseases in complex backgrounds and in various conditions. In the following section, the dataset, model, and the experiments are explained in detail.…”

Section: Related Workmentioning

confidence: 99%

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Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection

et al. 2019

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Plant diseases cause great damage in agriculture, resulting in significant yield losses. The recent expansion of deep learning methods has found its application in plant disease detection, offering a robust tool with highly accurate results. The current limitations and shortcomings of existing plant disease detection models are presented and discussed in this paper. Furthermore, a new dataset containing 79,265 images was introduced with the aim to become the largest dataset containing leaf images. Images were taken in various weather conditions, at different angles, and daylight hours with an inconsistent background mimicking practical situations. Two approaches were used to augment the number of images in the dataset: traditional augmentation methods and state-of-the-art style generative adversarial networks. Several experiments were conducted to test the impact of training in a controlled environment and usage in real-life situations to accurately identify plant diseases in a complex background and in various conditions including the detection of multiple diseases in a single leaf. Finally, a novel two-stage architecture of a neural network was proposed for plant disease classification focused on a real environment. The trained model achieved an accuracy of 93.67%.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection

et al. 2019

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“…There are many deep learning based methods used to identify plant diseases [1][2][3][4]. Some of them report about a high detection level, exceeding 90 %.…”

Section: Previous Studiesmentioning

confidence: 99%

Deep Siamese Networks for Plant Disease Detection

et al. 2020

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Crop losses are a major threat to the wellbeing of rural families, to the economy and governments, and to food security worldwide. The goal of our research is to develop a multi-functional platform to help the farming community to tilt against plant diseases. In our previous works, we reported about the creation of a special database of healthy and diseased plants’ leaves consisting of five sets of grapes images and proposed a special classification model based on a deep siamese network followed by k-nearest neighbors (KNN) classifier. Then we extended our database to five sets of images for grape, corn, and wheat – 611 images in total. Since after this extension the classification accuracy decreased to 86 %, we propose in this paper a novel architecture with a deep siamese network as feature extractor and a single-layer perceptron as a classifier that results in a significant gain of accuracy, up to 96 %.

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Enhancing practicality of deep learning for crop disease identification under field conditions: insights from model evaluation and crop‐specific approaches

Tian,

Zhao,

Yan

et al. 2024

Pest Management Science

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BACKGROUNDCrop diseases can lead to significant yield losses and food shortages if not promptly identified and managed by farmers. With the advancements in convolutional neural networks (CNN) and the widespread availability of smartphones, automated and accurate identification of crop diseases has become feasible. However, although previous studies have achieved high accuracy (>95%) under laboratory conditions (Lab) using mixed data sets of multiple crops, these models often falter when deployed under field conditions (Field). In this study, we aimed to evaluate disease identification accuracy under Lab, Field, and Mixed (Lab and Field) conditions using an assembled data set encompassing 14 diseases of apple (Malus × domestica Borkh.), potato (Solanum tuberosum L.), and tomato (Solanum lycopersicum L.). In addition, we investigated the impact of model architectures, parameter sizes, and crop‐specific models (CSMs) on accuracy, using DenseNets, ResNets, MobileNetV3, EfficientNet, and VGG Nets.RESULTSOur results revealed a decrease in accuracy across all models from Lab (98.22%) to Mixed (91.76%) to Field (71.55%) conditions. Interestingly, disease classification accuracy showed minimal variation across model architectures and parameter sizes: Lab (97.61–98.76%), Mixed (90.76–92.31%), and Field (68.56–73.81%). Although CSMs were found to reduce inter‐crop disease misclassifications, they also led to a slight increase in intra‐crop misclassifications.CONCLUSIONOur findings underscore the importance of enriching data representation and volumes over employing new model architectures. Furthermore, the need for more field‐specific images was highlighted. Ultimately, these insights contribute to the advancement of crop disease identification applications, facilitating their practical implementation in farmer's fields. © 2024 Society of Chemical Industry.

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Deep Learning for Plant Diseases: Detection and Saliency Map Visualisation

Cited by 187 publications

References 25 publications

Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection

Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection

Deep Siamese Networks for Plant Disease Detection

Enhancing practicality of deep learning for crop disease identification under field conditions: insights from model evaluation and crop‐specific approaches

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