Plant Disease Detection and Classification by Deep Learning—A Review

Li, Lili; Zhang, Shujuan

doi:10.1109/access.2021.3069646

Cited by 345 publications

(94 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It uses feed-forward artificial neural network architectures that contain many processing layers. Deep learning technology is best known technology for the recognition of image and voice [17]. In agriculture sector, Deep learning plays an important role for detection and classification.…”

Section: Deep Learningmentioning

confidence: 99%

Design of deep recursive CNN model for detecting and classifying peston plant

Kalambe¹,

Awachat²,

Raipure³

2022

ijhs

View full text Add to dashboard Cite

Automatic pest on plant detection in early stage is very essential for food quality control in the agriculture industry. However, the visual method to identify pest on every plant by human is a cumbersome process and cannot be well suited in the agriculture field, because it is time consuming, less accurate and labor intensive. Pest on Plant and plant leaf disease are the major factors responsible for reducing the quality and quantity of food production. Detection at the earlier stage of pest growth and its killing would result in reducing its effect on plant and enhance the quality of food production. Various existing ways have been used to identify and classify pest on plant, but issues have not been resolved, and there is still a scope for improvement. This paper proposes a Deep Recursive Convolutional Neural Networks (DR-CNN) to improve the average running time and achieve high accuracy. DR-CNN model is integrating the convolution, ReLU and Max pooling Layer into single unit and call recursively.

show abstract

Section: Deep Learningmentioning

confidence: 99%

Design of deep recursive CNN model for detecting and classifying peston plant

Kalambe¹,

Awachat²,

Raipure³

2022

ijhs

View full text Add to dashboard Cite

show abstract

“…Fuentes et al ( 2017 ) presented a DL tomato plant disease and pest detector. There are various recent excellent reviews of DL for plant image classification (Saleem et al, 2019 ; Hasan et al, 2020 ; Li et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Few-Shot Techniques for Fungal Plant Disease Classification and Evaluation of Clustering Capabilities Over Real Datasets

et al. 2022

View full text Add to dashboard Cite

Plant fungal diseases are one of the most important causes of crop yield losses. Therefore, plant disease identification algorithms have been seen as a useful tool to detect them at early stages to mitigate their effects. Although deep-learning based algorithms can achieve high detection accuracies, they require large and manually annotated image datasets that is not always accessible, specially for rare and new diseases. This study focuses on the development of a plant disease detection algorithm and strategy requiring few plant images (Few-shot learning algorithm). We extend previous work by using a novel challenging dataset containing more than 100,000 images. This dataset includes images of leaves, panicles and stems of five different crops (barley, corn, rape seed, rice, and wheat) for a total of 17 different diseases, where each disease is shown at different disease stages. In this study, we propose a deep metric learning based method to extract latent space representations from plant diseases with just few images by means of a Siamese network and triplet loss function. This enhances previous methods that require a support dataset containing a high number of annotated images to perform metric learning and few-shot classification. The proposed method was compared over a traditional network that was trained with the cross-entropy loss function. Exhaustive experiments have been performed for validating and measuring the benefits of metric learning techniques over classical methods. Results show that the features extracted by the metric learning based approach present better discriminative and clustering properties. Davis-Bouldin index and Silhouette score values have shown that triplet loss network improves the clustering properties with respect to the categorical-cross entropy loss. Overall, triplet loss approach improves the DB index value by 22.7% and Silhouette score value by 166.7% compared to the categorical cross-entropy loss model. Moreover, the F-score parameter obtained from the Siamese network with the triplet loss performs better than classical approaches when there are few images for training, obtaining a 6% improvement in the F-score mean value. Siamese networks with triplet loss have improved the ability to learn different plant diseases using few images of each class. These networks based on metric learning techniques improve clustering and classification results over traditional categorical cross-entropy loss networks for plant disease identification.

show abstract

“…However, they may lack the ability to extract the semantics and discriminate features in a changing environment and usually select appropriate features based on experience. Deep learning models solve the problem of manual feature extraction and are therefore widely used in various applications of crop disease measurement (Lawal, 2021 ; Li et al, 2021 ; Wani et al, 2022 ) Deep learning-based plant disease detection networks can be divided into the following networks: two-stage networks represented by Faster region-based convolutional neural network (Faster R-CNN) (Ren et al, 2017 ); one-stage networks represented by Single Shot Multibox Detector (SSD) (Liu et al, 2016 ), and You Only Look Once (YOLO) (Redmon and Farhadi, 2016 , 2018 ; Redmon et al, 2017 ; Bochkovskiy et al, 2020 ). The main difference between the two networks is that the two-stage network needs first to generate a candidate frame (Proposal) that may contain lesions before performing the target detection process.…”

Section: Introductionmentioning

confidence: 99%

An Industrial-Grade Solution for Crop Disease Image Detection Tasks

Dai

Fan

2022

Front. Plant Sci.

View full text Add to dashboard Cite

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.

show abstract

Plant Disease Detection and Classification by Deep Learning—A Review

Cited by 345 publications

References 85 publications

Design of deep recursive CNN model for detecting and classifying peston plant

Design of deep recursive CNN model for detecting and classifying peston plant

Analysis of Few-Shot Techniques for Fungal Plant Disease Classification and Evaluation of Clustering Capabilities Over Real Datasets

An Industrial-Grade Solution for Crop Disease Image Detection Tasks

Contact Info

Product

Resources

About