scholarly journals Strawberry Fungal Leaf Scorch Disease Identification in Real-Time Strawberry Field Using Deep Learning Architectures

Plants ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 2643
Author(s):  
Irfan Abbas ◽  
Jizhan Liu ◽  
Muhammad Amin ◽  
Aqil Tariq ◽  
Mazhar Hussain Tunio
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Agricultural Development ◽  
Vision System ◽  
Severe Disease ◽  
Disease Diagnosis ◽  
Plant Diseases ◽  
Leaf Scorch ◽  
Disease Identification ◽  
Severe Stage

Plant health is the basis of agricultural development. Plant diseases are a major factor for crop losses in agriculture. Plant diseases are difficult to diagnose correctly, and the manual disease diagnosis process is time consuming. For this reason, it is highly desirable to automatically identify the diseases in strawberry plants to prevent loss of crop quality. Deep learning (DL) has recently gained popularity in image classification and identification due to its high accuracy and fast learning. In this research, deep learning models were used to identify the leaf scorch disease in strawberry plants. Four convolutional neural networks (SqueezeNet, EfficientNet-B3, VGG-16 and AlexNet) CNN models were trained and tested for the classification of healthy and leaf scorch disease infected plants. The performance accuracy of EfficientNet-B3 and VGG-16 was higher for the initial and severe stage of leaf scorch disease identification as compared to AlexNet and SqueezeNet. It was also observed that the severe disease (leaf scorch) stage was correctly classified more often than the initial stage of the disease. All the trained CNN models were integrated with a machine vision system for real-time image acquisition under two different lighting situations (natural and controlled) and identification of leaf scorch disease in strawberry plants. The field experiment results with controlled lightening arrangements, showed that the model EfficientNet-B3 achieved the highest classification accuracy, with 0.80 and 0.86 for initial and severe disease stages, respectively, in real-time. AlexNet achieved slightly lower validation accuracy (0.72, 0.79) in comparison with VGGNet and EfficientNet-B3. Experimental results stated that trained CNN models could be used in conjunction with variable rate agrochemical spraying systems, which will help farmers to reduce agrochemical use, crop input costs and environmental contamination.

Deep Learning-Based Mobile Application for Plant Disease Diagnosis

2019 ◽  
pp. 242-271 ◽  
Author(s):  
Shradha Verma ◽  
Anuradha Chug ◽  
Amit Prakash Singh ◽  
Shubham Sharma ◽  
Puranjay Rajvanshi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Mobile Application ◽  
Plant Disease ◽  
Prediction Models ◽  
Disease Diagnosis ◽  
Tomato Leaf ◽  
Plant Diseases ◽  
Learning Tools ◽  
Class Labels

With the increasing computational power, areas such as machine learning, image processing, deep learning, etc. have been extensively applied in agriculture. This chapter investigates the applications of the said areas and various prediction models in plant pathology for accurate classification, identification, and quantification of plant diseases. The authors aim to automate the plant disease identification process. To accomplish this objective, CNN has been utilized for image classification. Research shows that deep learning architectures outperform other machine learning tools significantly. To this effect, the authors have implemented and trained five CNN models, namely Inception ResNet v2, VGG16, VGG19, ResNet50, and Xception, on PlantVillage dataset for tomato leaf images. The authors analyzed 18,160 tomato leaf images spread across 10 class labels. After comparing their performance measures, ResNet50 proved to be the most accurate prediction tool. It was employed to create a mobile application to classify and identify tomato plant diseases successfully.

scholarly journals Meta-neural-network for real-time and passive deep-learning-based object recognition

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jingkai Weng ◽  
Yujiang Ding ◽  
Chengbo Hu ◽  
Xue-Feng Zhu ◽  
Bin Liang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Acoustic Scattering ◽  
Scattered Wave ◽  
Disease Diagnosis ◽  
Great Success ◽  
Destructive Testing ◽  
Unit Cells ◽  
Handwritten Digit

AbstractAnalyzing scattered wave to recognize object is of fundamental significance in wave physics. Recently-emerged deep learning technique achieved great success in interpreting wave field such as in ultrasound non-destructive testing and disease diagnosis, but conventionally need time-consuming computer postprocessing or bulky-sized diffractive elements. Here we theoretically propose and experimentally demonstrate a purely-passive and small-footprint meta-neural-network for real-time recognizing complicated objects by analyzing acoustic scattering. We prove meta-neural-network mimics a standard neural network despite its compactness, thanks to unique capability of its metamaterial unit-cells (dubbed meta-neurons) to produce deep-subwavelength phase shift as training parameters. The resulting device exhibits the “intelligence” to perform desired tasks with potential to overcome the current limitations, showcased by two distinctive examples of handwritten digit recognition and discerning misaligned orbital-angular-momentum vortices. Our mechanism opens the route to new metamaterial-based deep-learning paradigms and enable conceptual devices automatically analyzing signals, with far-reaching implications for acoustics and related fields.

scholarly journals A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition

Sensors ◽  
2017 ◽  
Vol 17 (9) ◽  
pp. 2022 ◽  
Author(s):  
Alvaro Fuentes ◽  
Sook Yoon ◽  
Sang Kim ◽  
Dong Park
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Tomato Plant ◽  
Plant Diseases

scholarly journals Improving Accuracy of Tomato Plant Disease Diagnosis Based on Deep Learning With Explicit Control of Hidden Classes

2021 ◽  
Vol 12 ◽  
Author(s):  
Alvaro Fuentes ◽  
Sook Yoon ◽  
Mun Haeng Lee ◽  
Dong Sun Park
Keyword(s):  
Deep Learning ◽  
Tomato Plant ◽  
Plant Disease ◽  
High Efficiency ◽  
Recognition Rate ◽  
Disease Diagnosis ◽  
Plant Diseases ◽  
Improve Model ◽  
Improving Accuracy ◽  
And Control

Recognizing plant diseases is a major challenge in agriculture, and recent works based on deep learning have shown high efficiency in addressing problems directly related to this area. Nonetheless, weak performance has been observed when a model trained on a particular dataset is evaluated in new greenhouse environments. Therefore, in this work, we take a step towards these issues and present a strategy to improve model accuracy by applying techniques that can help refine the model’s generalization capability to deal with complex changes in new greenhouse environments. We propose a paradigm called “control to target classes.” The core of our approach is to train and validate a deep learning-based detector using target and control classes on images collected in various greenhouses. Then, we apply the generated features for testing the inference of the system on data from new greenhouse conditions where the goal is to detect target classes exclusively. Therefore, by having explicit control over inter- and intra-class variations, our model can distinguish data variations that make the system more robust when applied to new scenarios. Experiments demonstrate the effectiveness and efficiency of the proposed approach on our extended tomato plant diseases dataset with 14 classes, from which 5 are target classes and the rest are control classes. Our detector achieves a recognition rate of target classes of 93.37% mean average precision on the inference dataset. Finally, we believe that our study offers valuable guidelines for researchers working in plant disease recognition with complex input data.

scholarly journals Research on Crop Leaf Disease Identification Method Based on LM-BP Neural Network

2019 ◽  
Vol 131 ◽  
pp. 01118
Author(s):  
Fan Tongke
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Bp Neural Network ◽  
Extraction Method ◽  
Large Scale ◽  
Disease Diagnosis ◽  
Learning Technology ◽  
Feature Extraction Method ◽  
Disease Identification

Aiming at the problem of disease diagnosis of large-scale crops, this paper combines machine vision and deep learning technology to propose an algorithm for constructing disease recognition by LM_BP neural network. The images of multiple crop leaves are collected, and the collected pictures are cut by image cutting technology, and the data are obtained by the color distance feature extraction method. The data are input into the disease recognition model, the feature weights are set, and the model is repeatedly trained to obtain accurate results. In this model, the research on corn disease shows that the model is simple and easy to implement, and the data are highly reliable.

scholarly journals How Convolutional Neural Networks Diagnose Plant Disease

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yosuke Toda ◽  
Fumio Okura
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Plant Disease ◽  
Disease Diagnosis ◽  
Black Box ◽  
Plant Diseases ◽  
Training Dataset ◽  
Great Success ◽  
Human Decision

Deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various plant diseases. However, a limited number of studies have elucidated the process of inference, leaving it as an untouchable black box. Revealing the CNN to extract the learned feature as an interpretable form not only ensures its reliability but also enables the validation of the model authenticity and the training dataset by human intervention. In this study, a variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available plant disease image dataset. We showed that neural networks can capture the colors and textures of lesions specific to respective diseases upon diagnosis, which resembles human decision-making. While several visualization methods were used as they are, others had to be optimized to target a specific layer that fully captures the features to generate consequential outputs. Moreover, by interpreting the generated attention maps, we identified several layers that were not contributing to inference and removed such layers inside the network, decreasing the number of parameters by 75% without affecting the classification accuracy. The results provide an impetus for the CNN black box users in the field of plant science to better understand the diagnosis process and lead to further efficient use of deep learning for plant disease diagnosis.

scholarly journals A Mobile-Based System for Detecting Plant Leaf Diseases Using Deep Learning

2021 ◽  
Vol 3 (3) ◽  
pp. 478-493
Author(s):  
Ahmed Abdelmoamen Ahmed ◽  
Gopireddy Harshavardhan Reddy
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Infected Plant ◽  
The United States ◽  
Disease Diagnosis ◽  
Mobile App ◽  
Plant Diseases ◽  
Plant Leaves ◽  
Plant Leaf ◽  
Crop Diseases

Plant diseases are one of the grand challenges that face the agriculture sector worldwide. In the United States, crop diseases cause losses of one-third of crop production annually. Despite the importance, crop disease diagnosis is challenging for limited-resources farmers if performed through optical observation of plant leaves’ symptoms. Therefore, there is an urgent need for markedly improved detection, monitoring, and prediction of crop diseases to reduce crop agriculture losses. Computer vision empowered with Machine Learning (ML) has tremendous promise for improving crop monitoring at scale in this context. This paper presents an ML-powered mobile-based system to automate the plant leaf disease diagnosis process. The developed system uses Convolutional Neural networks (CNN) as an underlying deep learning engine for classifying 38 disease categories. We collected an imagery dataset containing 96,206 images of plant leaves of healthy and infected plants for training, validating, and testing the CNN model. The user interface is developed as an Android mobile app, allowing farmers to capture a photo of the infected plant leaves. It then displays the disease category along with the confidence percentage. It is expected that this system would create a better opportunity for farmers to keep their crops healthy and eliminate the use of wrong fertilizers that could stress the plants. Finally, we evaluated our system using various performance metrics such as classification accuracy and processing time. We found that our model achieves an overall classification accuracy of 94% in recognizing the most common 38 disease classes in 14 crop species.

scholarly journals A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework

2021 ◽  
Vol 7 ◽  
pp. e432
Author(s):  
Bifta Sama Bari ◽  
Md Nahidul Islam ◽  
Mamunur Rashid ◽  
Md Jahid Hasan ◽  
Mohd Azraai Mohd Razman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Disease Diagnosis ◽  
Brown Spot ◽  
Real Field ◽  
Computer Assisted ◽  
Identification System ◽  
Rice Leaf ◽  
Leaf Disease ◽  
Leaf Infection

The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms’ edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time.

scholarly journals DISEASE IDENTIFICATION IN CROPS USING DEEP LEARNING MODELS

2021 ◽  
Vol 10 (6) ◽  
pp. 3860-3865
Author(s):  
Adya Trisal
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Food Source ◽  
Plant Diseases ◽  
Learning Models ◽  
Image Detection ◽  
The Past ◽  
Pest Infestation ◽  
Disease Identification ◽  
Visual Geometry

Food is one of the most fundamental necessities and is crucial for survival. Loss of the food source due to pest infestation attributes towards destroying one-fifth of the yearly worldwide crop yield. The past few decades have witnessed a burgeoning trend of using computerized methods for discerning various diseases found in crops. The main advantage of digitizing the detection process is that it eliminates the errors and miscalculations associated with manual detection. With the advent of Object Detection and Artificial Intelligence, malady detection has not only been rapid but has also maintained the expected level of accuracy. The concepts and models of deep learning have been efficaciously applied and used to identify as well as classify plant diseases. In the scope of this research paper, we present a comprehensive digitized approach to detect plant diseases by utilizing image detection, computer vision, and deep learning models like the Convolutional neural networks, Inception model, and the Visual Geometry Group (VGG16) model. In addition to this, the performance of the above-mentioned models has been evaluated by the virtue of metrics like f1 score, accuracy, precision, and recall.

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