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.

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.

Deep Learning Models for the Detection of Plant Leaf Diseases: A Systematic Review

2019 ◽  
Author(s):  
Malusi Sibiya ◽  
Mbuyu Sumbwanyambe
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Systems ◽  
Learning Models ◽  
Plant Leaves ◽  
Plant Leaf ◽  
Learning Framework ◽  
Software Frameworks ◽  
Software Packages ◽  
Learning Frameworks

Machine learning systems use different algorithms to detect the diseases affecting the plant leaves. Nevertheless, selecting a suitable machine learning framework differs from study to study, depending on the features and complexity of the software packages. This paper introduces a taxonomic inspection of the literature in deep learning frameworks for the detection of plant leaf diseases. The objective of this study is to identify the dominating software frameworks in the literature for modelling machine learning plant leaf disease detecting systems.

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.

scholarly journals Plant leaf Damage Detection using Segmentation

2019 ◽  
Vol 9 (2S3) ◽  
pp. 238-243
Keyword(s):  
Agricultural Production ◽  
Primary Source ◽  
Plant Diseases ◽  
Leaf Damage ◽  
Agricultural System ◽  
Technical Knowledge ◽  
Plant Leaves ◽  
Employment Opportunities ◽  
Plant Leaf ◽  
Advanced Technologies

A primary source of livelihood is agriculture. In developing country like India, wide-ranging employment opportunities are provided by Agriculture for the villagers. Various crops are included in the agricultural system of India and 70% of the population depends upon agriculture as reported by survey. Because of lagging in technical knowledge, manual cultivation is adopted by majority of the Indian farmers. The kind of crops that grows well on their land is unaware by the farmers. The agriculture production is affected by the heterogeneous diseases that affect the plant leaves and result in the productive loss. Moreover, the quality as well as quantity of the agricultural production is reduced by it. A key role is played by the leaves in the rapid growth of the plants and production of crops. The identification of diseases related to plant leaf is a difficult task for the farmers and for the researchers. At present, various pesticides were sprayed on the plants that directly or indirectly affect the human health and the economy. Various methods must be adopted for detecting these kinds of plant diseases. This paper presents a review of various plant diseases and several advanced technologies in detecting the diseases.

scholarly journals Implementation of CNN for Plant Leaf Classification

2021 ◽  
Vol 2 (2) ◽  
pp. 1
Author(s):  
Mohammad Diqi ◽  
Sri Hasta Mulyani
Keyword(s):  
Deep Learning ◽  
Medicinal Plants ◽  
Programming Language ◽  
Classification Model ◽  
Plant Leaves ◽  
Plant Leaf ◽  
The Public ◽  
Java Programming ◽  
Python Programming ◽  
Deep Learning Model

Many deep learning-based approaches for plant leaf stress identification have been proposed in the literature, but there are only a few partial efforts to summarize various contributions. This study aims to build a classification model to enable people or traditional medicine experts to detect medicinal plants by using a scanning camera. This Android-based application implements the Java programming language and labels using the Python programming language to build deep learning applications. The study aims to construct a deep learning model for image classification for plant leaves that can help people determine the types of medicinal plants based on android. This research can help the public recognize five types of medicinal plants, including spinach Duri, Javanese ginseng, Dadap Serep, and Moringa. In this study, the accuracy is 0.86, precision 0.22, f-1 score 0.23, while recall is 0.2375.

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 A Chaotic Salp Swarm Feature Selection Algorithm for Apple and Tomato Plant Leaf Disease Detection

2021 ◽  
Vol 10 (5) ◽  
pp. 3037-3045
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Detection System ◽  
Plant Diseases ◽  
Healthy Plant ◽  
Disease Detection ◽  
Test Accuracy ◽  
Plant Leaves ◽  
Leaf Disease ◽  
Deep Learning Network

Plants are seen as vital because they provide mankind with energy. Plant diseases can harm the leaf at any time between planting and harvesting, resulting in enormous losses in crop output and market value. A leaf disease detection system acts asignificant role in agricultural production. A large amount of labour is required for this process as well as an in-depth understanding of plant diseases. Determining the presence of illnesses in plant leaves requires the use of deep learning and machine learning methods, which classify the data based on a specified set. In this paper, apple and tomato leaves disease detection process is carried out by Chaotic Salp Swarm algorithm (CSSA) followed by Bi-directional Long Short Term Memory (Bi-LSTM) technique for classification. We've used the Bi-LSTM architecture to sense disease in tomato and apple leaves in studies. In order to determine the type of leaves, we trained a deep learning network using the PlantVillage dataset of damaged and healthy plant leaves. It is estimated that the trained model achieves a test accuracy of 96%.

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 First Report of Powdery Mildew Caused by an Oidium sp. on Torenia fournieri

Plant Disease ◽  
1999 ◽  
Vol 83 (9) ◽  
pp. 878-878 ◽  
Author(s):  
G. E. Holcomb
Keyword(s):  
Powdery Mildew ◽  
Infected Plant ◽  
Hyphal Growth ◽  
The United States ◽  
Plant Leaves ◽  
Torenia Fournieri ◽  
Bedding Plant ◽  
Leaf Surfaces ◽  
Powdery Mildew Fungi ◽  
Leaf Distortion

Torenia fournieri Lind. ex Fourn. (wishbone flower, bluewings) is a popular summer bedding plant in Louisiana. Clown Mixture cultivars are available in garden centers in March and April. Transplants of cultivar Clown Rose were purchased, transplanted to larger pots, and maintained in a greenhouse. A powdery mildew was observed on these plants in March and all plants (six) were severely diseased by May. Symptoms included leaf distortion and yellowing. Powdery mildew was not present on transplants and none was found in later checks of garden centers. An Oidium sp. was observed sporulating on both leaf surfaces of infected plants. Conidia were ellipsoid, produced in chains, lacked fibrosin bodies, and averaged 41 × 22 μm in dimensions. No sexual stage was observed. Healthy plants of Clown Mixture cultivars were obtained and inoculated by brushing conidia from infected plant leaves to leaves of healthy plants. Plants were maintained in a greenhouse where temperatures ranged from 16 to 26°C. Hyphal growth appeared on inoculated plants after 5 days and the reproductive structures formed later appeared the same as those on originally infected plants. Uninoculated plants remained healthy. No previous reports of powdery mildew diseases of T. fournieri in the United States were found. Other powdery mildew pathogens reported on T. fournieri are Sphaerotheca fuliginea (Schlechtend.:Fr.) Pollacci in Finland and Japan and an Erysiphe sp. in Japan (1). Reference: (1) K. Amano. Host Range and Geographical Distribution of the Powdery Mildew Fungi. Japan Scientific Press, Tokyo, 1986.

scholarly journals Plant Diseases Detection and Pesticide Suggestion Implementation

2021 ◽  
Vol 9 (VI) ◽  
pp. 1806-1812
Author(s):  
Onkar Kunjir
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Transfer Learning ◽  
Plant Disease ◽  
Plant Diseases ◽  
Disease Detection ◽  
Plant Leaf

Plant diseases affect the life of not only farmers but also businesses which are dependent on it. Plant disease detection is a computer vision problem which tries to identify the disease splat is infected using an image of a plant leaf. Different kinds of models have been proposed to tackle this problem. This paper focuses on generating small, lightweight and accurate models with the help of deep learning and transfer learning.

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