A Deep Learning Approach to Classify Plant and Detect Disease (original) (raw)
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Review of Plant Disease Detection and Diagnosis Using Deep Learning Model
2020
Crop diseases are responsible for the significant economic losses in agricultural industry worldwide. Monitoring the health status of plants is difficult to control the spread of diseases and implement efficient management. There are various types of disease present on leaves such as bacterial, fungal, viral etc. In our project we are using concepts of deep learning. Deep learning provides an opportunity for detectors to recognize crop diseases in a timely and accurate manner, which will not only upgrade the accuracy of plant protection but also expand the scope of computer vision in the field of precision agriculture. Convolutional neural network (CNN) model is developed to perform plant disease detection and diagnosis using healthy and diseased plants leaves, through deep learning methods. It detects the plant disease from the picture of the plant leaf. All farmer has to capture the plant leaf image from app in his mobile. The app send this images to our designed AI system. Our AI...
Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification
Computational Intelligence and Neuroscience, 2016
The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, w...
Plants Leaf Diseases Detection Using Deep Learning
Iraqi Journal of Science, 2022
Agriculture improvement is a national economic issue that extremely depends on productivity. The explanation of disease detection in plants plays a significant role in the agriculture field. Accurate prediction of the plant disease can help treat the leaf as early as possible, which controls the economic loss. This paper aims to use the Image processing techniques with Convolutional Neural Network (CNN). It is one of the deep learning techniques to classify and detect plant leaf diseases. A publicly available Plant village dataset was used, which consists of 15 classes, including 12 diseases classes and 3 healthy classes. The data augmentation techniques have been used. In addition to dropout and weight regularization, which leads to good classification results by preventing the model from over fitting. The model was optimized with the Adam optimization technique. The obtained results in terms of performance were 98.08% in the testing stage and 99.24% in the training stage. Ne...
Application for Plant's Leaf Disease Detection using Deep Learning Techniques
IRJET, 2022
Agriculture/farming is a significant source of income for farmers in poor countries, and yield estimation is a significant challenge for them. This may be performed by agricultural monitoring of the plant or crop to predict the disease of a certain kind of plant, which can help to prevent hunger and support our Indian farmers before harvesting any plant. As a result, we're going to show you how to use deep learning algorithms to anticipate plant disease in a reliable and broad approach. First, we'll look at the diseases that affect that particular plant, as well as the yield estimates made by the remote sensing community, and then we'll provide a solution based on some of the most current representation learning technologies. We'll use a dataset of nation-level graph leaves and their related diseases to build a train model using convolutional neural networks and conditional random field approaches, which will be combined with image processing. This popular topic in our country suggests that our plan will implement some competing tactics.
Plant Disease Detection and Classification Using Deep Learning
International Journal for Research in Applied Science and Engineering Technology, 2023
Agriculture is the backbone of every country in the world. In India, most of the rural population still depends on agriculture. The agricultural sector provides major employment in rural areas. Furthermore, it contributes a significant amount to India's gross domestic product (GDP). Therefore, protecting and enhancing the agricultural sector helps in the development of India's economy. In this work, a real-time decision support system integrated with a camera sensor module was designed and developed for the identification of plant disease. This research proposes an intelligent method for plant disease classification using image processing techniques. The proposed method aims to assist farmers and experts in identifying and diagnosing plant diseases efficiently and accurately. The system first obtains images of the plant leaves from different perspectives and then preprocesses the images to enhance the quality and remove noise. The preprocessed images are then subjected to feature extraction using a deep convolutional neural network (CNN) model. The features extracted from the CNN are then fed into a classifier for the classification of plant diseases. The proposed method is evaluated using a dataset of plant images with three different types of diseases. The results obtained show that the proposed method achieves high accuracy in the classification of plant diseases, making it a useful tool for plant disease diagnosis and control. The proposed method can be integrated into a mobile application or web based platform for use by farmers and botanists.
Nanotechnology Perceptions, 2024
Plant diseases greatly affect agricultural productivity and quality. Effective illness management involves early detection and accurate diagnosis. However, hand identification is laborious and error-prone, which can cause losses. The biggest challenge is developing a reliable plant disease identification system. Plant pictures are complex, making it hard to identify disease symptoms from healthy structures. A high-level framework using powerful image processing to automate disease identification is needed. A reliable method to help farmers and other stakeholders detect and diagnose plant diseases are the goal. This research proposes a high-level deep learning system for plant disease detection, focusing on Kaggle repository classification of different plant illnesses. Advanced techniques including gradient-based Radial Basis Function (RBF) for segmentation and Deep Belief Network (DBN) for feature extraction were used to extract relevant features from plant photos using deep learning models. The classification phase used ResNet-50, known for its ability to understand complicated patterns and identify images reliably. Plants are photographed from several angles in real time under stable lighting. The ResNet-50 CNN method, known for extracting hierarchical features from images, classifies diseases. Independent test photos from the New Plant Diseases Dataset are used to validate algorithm performance. Plant disease identification performance improved by 6.7% to 30% over conventional approaches.
Classification of Plants Leaf Diseases using Convolutional Neural Network
Al-Nahrain journal of science, 2021
Agriculture is one of the most important professions in many countries, including Iraq, as the Iraqi financial system depends on agricultural production and great attention should be paid to concerns about agricultural production. Because plants are exposed to many diseases and monitoring plant diseases with the help of specialists in the agricultural region can be very expensive. There is a need for a system capable of automatically detecting diseases. The aim of the research proposed is to create a model that classifies and predicts leaf diseases in plants. This model is based on a convolution network, which is a kind of deep learning. The dataset used in this study called (Plant Village) was downloaded from the kaggle website. The dataset contains 34,934 RGB images, and the deep CNN model can efficiently classify 15 different classes of healthy and diseased plants using the leaf images. The model used techniques to augment data and dropout. The Softmax output layer was used with the categorical cross-entropy loss function to apply the CNN model proposed with the Adam optimization technique. The results obtained by the proposed model were 97.42% in the training phase and 96.18% in the testing phase.
Plant disease recognition using deep learning
INNOVATIONS IN COMPUTATIONAL AND COMPUTER TECHNIQUES: ICACCT-2021
Detection of disease in plants is a paramount issue in the agricultural sector of any country. Crop failure adversely affects the farmer and has an enormous impact on the economy. Modern technology has developed various tools using deep learning techniques to perform errorless predictions and to originate a rapid treatment. The purpose of this paper is to propose a deep learning based method to detect disease in plants using leaf images. Our main goal is to develop an efficient methodology to provide reliable predictions. We have considered CNN (Convolution Neural Network) algorithm as our principal detector. The proposed system can approach complex scenarios from the plant's body. The result achieved is very promising of 98.33 accuracy and 0.0047 loss on validation data.
Computer Vision-based Plant Leaf Disease Recognition using Deep Learning
International journal of innovative technology and exploring engineering, 2020
Computer vision-based applications play a vital role in the era of computer science and engineering. Now-a-days peoples are facing different problems in agricultural fields to improve their cultivation. So, a better approach is proposed for plant leaf disease recognition using deep learning techniques for agricultural improvement. This research is very much helpful for the farmers to identify the leaf diseases of a plant. This proposed system has three subsections. One is feature extraction, second is trained networking generation and the third one is classification. This system first takes an image as the input and extracts the features from the image using K-means clustering. Secondly, it generates a trained network using Convolutional Neural Networks (CNNs). Then compare the original leaf image with the generated trained database in the classification section and recognition of the disease of the plant. Different techniques are used in this system for properly recognized the diseases. After analyzed the 3000 trained images, three types of leaf diseases are properly recognized by this system, which are Cercospora Leaf Spot, Mosaic virus, and Alternaria Leaf Spot. The overall accuracy of this system is very good and which is up to 95.26%.
Plant Leaf Disease Detection Using Deep Learning
2021
Plant disease is an ongoing challenge for smallholder farmers, which threatens income and food security. The recent revolution in smartphone penetration and computer vision models has created an opportunity for image classification in agriculture. Convolutional Neural Networks (CNNs) are considered state-of-the-art in image recognition and offer the ability to provide a prompt and definite diagnosis. In this paper, the performance of a pre-trained ResNet34 model in detecting crop disease is investigated. The developed model is deployed as a web application and is capable of recognizing 7 plant diseases out of healthy leaf tissue. A dataset containing 8,685 leaf images; captured in a controlled environment, is established for training and validating the model. Validation results show that the proposed method can achieve an accuracy of 97.2% and an F1 score of greater than 96.5%. This demonstrates the technical feasibility of CNNs in classifying plant diseases and presents a path towa...