Query: plant leaf disease prediction

title: Deep Learner Based Earlier Plant Leaf Disease Prediction and Classification Using Machine Learning Algorithms
abstract: Machine Learning makes changes in the world in recent days. Machine Learning is now well developing and reduces a human effort. Agriculture is the art and science of growing plant and other crops for rising economic gain. However, the diseases that affect the Plant leaf have make an impact of agriculture production. The Plant leaf usually gets infected by pathogens such as bacteria, fungus and virus. In order to address the above issue a novel prediction system for earlier detection of plant leaf diseases. In that case of situation, Machine Learning plays a major role to provide an effective solution through monitoring the whether the leaf is healthy or not. Deep Learning algorithm are used here to acquire the data from various plant health parameters and these data's are stored into the cloud. Stored data can be viewed through website from anywhere. In this paper our proposed a deep learning algorithms automatically to detect and classify the plant leaf disease using image datasets. Plant leaf disease detection and identification includes the stages like image acquisition, image pre processing, image segmentation, feature extraction and classification. This paper discusses techniques for image pre processing, image segmentation algorithm used for automatic recognition and research on various plant leaf disease classification algorithms that may be used for leaves disease classification. The outcome of the proposed project is represented through graphical visualization, thereby providing better prediction system projecting the high similarity symptoms.

title: Plant Leaf Disease prediction using Machine Learning
abstract: Agriculture is critical to India's economic growth. Farmers struggle to choose the right fruit and vegetable crop. Disease management by hand is a difficult job. The majority of diseases affect the plant's leaves or stems. As a result, agriculturists must develop effective techniques. To predict the disease, we combined two techniques in this paper. The proposed work's aim is to diagnose disease using image processing and clustering techniques on a photo of diseased plant leaves. Preprocessing begins with the input image. The RGB to L*a*b conversion of the input image of leaves is then performed. Following that, hierarchical clustering is used to segment leaf disease. The predominantly green color pixels are masked after segmentation depending on specific threshold values. Finally, the proposed method's precision was compared to that of other approaches. The suggested procedure was shown to be 92% accurate.

title: Covariance Kalman Geometric Graph Based Feature Extraction And Bernoulli Kernel Classifier For Plant Leaf Disease Prediction
abstract: As far as the agricultural domain is concerned, one of the most hot research areas of analysis is accurate prediction of leaf disease from the leaf images of a plant. The prediction of agricultural plant diseases bymeans of the image processing techniques will hence reduce the dependence on the farmers to safeguard their agricultural land and also their products. However, with the presence of noise, the leaf disease prediction is said to be hindered. To address this issue, in this paper, Covariance Kalman Geometric Graph basedBernoulliClassifier (CKGG BC) for Plant leaf disease prediction is proposed. The CKGG BC method is split into three parts. To start with the plant leaf image provided as input, the Covariance Kalman Filtered Preprocessing modelintroduced for the image enhancement. Second, Geometric Graph based Segmented Cooccurrence Feature Extraction model is applied to the preprocessed image to accurately segment the infected leaf areas and followed by which extracting the accurate infected leaf areas. Finally, Bernoulli Online Multiple Kernel Learning Classifier is applied for accurate plant leaf disease prediction with minimum classification error. The proposed method provides a significant refinement with respect to state of the art methods. Even under complex background conditions, i.e. in the presence of noise, the averageaccuracy of the proposed method is said to be improved and hence paves mechanism for prediction of plant leaf disease in a significant manner. Experimentalresults exhibit the effectiveness of the proposed method in terms of computational overhead, accuracy, true positive rate and classification error respectively.

title: Deep Learning Based Automated Feature Engineering for Rice Leaf Disease Prediction
abstract: Though the financial impact of agriculture is gradually decreasing with the extensive economic growth of the country, still agriculture is one of the most wide economic sectors that play an important role in the global socio economic structure of India. There are various diseases that affect food crops and cause huge damages to crops, frightening the livings of helpless farmers and the food and nutrition security of masses. So, identification of diseases that affect the plants and quick recovery from the diseases are the major concerns of the researchers. Here, we have developed a deep learning based automated feature engineering for rice leaf disease prediction. The objective of this work is to enhance the crop production by early prediction of leaf diseases and taking precautions to remove or at least not to spread the diseases over the surroundings of the infected areas. Initially, the diseased portion of the leaves of the rice plant images is identified and separated from the leaves. These diseased images are feed into the convolution neural network (CNN) model, a modern family of deep learning model. The architecture of the CNN model consists of four convolution layers, two fully connected layers, and at the last softmax output layer. The main motivation for using CNN is that it has the power of automated feature engineering to create infinite number of features without any human bias. Also, it has the capability of capturing all possible complex nonlinear interactions among the features. Next, a dimension reduction method is applied to remove the redundant features and finally, the diseases are classified using various classifiers. The method is experimented using 10,500 infected leaves. The experimental results and comparative study based on the performance evaluation show the effectiveness of the proposed method and help to select the appropriate classifier for rice leaf disease prediction.

title: Prediction of plant leaf disease using image pre processing and filter based optimal feature selection for KNN classifier
abstract: In plants disease detection is a significant task related to agricultural production of a country. The major economy of a country is linked with the agricultural production which is very important for development of a nation. Any kind of infection on the leaves of the plants leads to loss in crop production and puts down the effort of farmers which in turn hits the economy and livelihood of the country. In this paper we propose an image processing and filter based feature selection method which distinguishes the disease of the plants leaf and classifies them with using KNN classifier. Using feature selection process, the unwanted redundancy and irrelevant data is filtered which helps the classifier to learn and classify the data more precisely.  Plant Leaf, Disease, image processing, machine Learning,

title: An Analysis of Various Techniques for Leaf Disease Prediction
abstract: The detection of plant diseases at early stage can be the best precaution taken by any farmer to avoid great loss. If that work of detecting the plant is made automatic, then it would be the essential topic for discovery. Mainly, plant diseases are caused by fungi, bacteria, and virus. All three affect the plant in different way which can be identified. This feature helps in detecting the particular disease. When it comes to fungi, they can be classified with their morphology that is based on their reproductive structures. Bacteria have the unique property of increasing their number in short duration, the single cell dividing into two and hence multiplying in number, but compared to fungi, they have simple life cycle. Viruses are the smallest particle found. They are made up of proteins and genetic materials. The method for detecting involves five stages, in the first stage, the image is selected through the inputs, the second stage involves processing of image, the third involves dividing the image, the fourth involves finding unique attributes, and final step involves analysis.

title: PEST DETECTION BASED LEAF DISEASE PREDICTION USING DEEP LEARNING TECHNIQUES
abstract: Agricultural productivity is something on which economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. Plant diseases are extremely significant, as that can adversely affect both quality and quantity of crops in agriculture production. Plant disease diagnosis is very essential in earlier stage in order to cure and control them. Plant diseases can increase the cost of agricultural production and may extend to total economic disaster of a producer if not cured appropriately at early stages. Inspired by the deep learning breakthrough in image based plant disease recognition, this work proposes deep learning models for image based automatic diagnosis of plant disease severity. Deep learning algorithm in image can offer an alternative solution in plant monitoring and such an approach may anyway be controlled by a professional to offer his services with lower cost. It includes image segmentation which includes active contour method and image classification approach which includes neural network algorithm to predict various types of diseases. Back Propagation Neural Network (BPNN) is a multilayer neural network consisting of an input layer, minimum one hidden layer and an output layer. As the name of the algorithm implies, the errors (hence, learning) propagate backwards from the output layer to the input layer. Therefore, the process of back propagation is used to estimate weights which minimize the errors in reference with the network's modifiable weights. And also extend the approach to recommend the fertilizers based on severity analysis with measurements. Finally provide alert about fertilizer to farmers in the form of SMS alert.

title: A Multiclass Plant Leaf Disease Detection using Image Processing and Machine Learning Techniques
abstract: In Indian economy, Agriculture is considered as a one of the strongest pillars. Agriculture sector contributes significantly in GDP of the country and it provides employability to many people live in rural area. Plant disease interrupts normal growth of plant and it is one of major reason behind less production that turns to economic losses. Timely detection of disease helps to provide the remedies that can control the spreading of disease in plants. Leaf examination is deliberated as a one of the best technique in plant disease diagnosis. Computer vision and machine learning become evolving fields that makes computers able to recognize and understand information from digital images. The paper proposed a prediction model for plant leaf disease detection and classification using computer vision and machine learning methods. The raw image of a leaf is pre processed, segmented and features like shape, color, texture, vein etc. are extracted. Different machine learning classifiers are applied to classify the leaf image. The experimental results are evaluated and compared with Random Forest, Support Vector Machine, KNeighrest Neighbor and Artificial Neural Network. The proposed prediction model performs well with random forest as compare to other classifiers.

title: PLANT LEAF DISEASE DETECTION USING CNN AND RASPBERRY Pi
abstract: For identifying diseases for plants we use naked eye. But it is not accurate so SVM is used for prediction which is less accurate 70 even if dataset is good.so, this paper we use Raspberry pi for identification of the diseases. Here we use the convolutional neural networks for processing of images. CNN uses various focal points to identify the diseases in the plants. In research purposes it is very useful because by this we can identify diseases of leaf correctly using the CNN algorithm. Disease refers to type of the plant effected. Here we use the best algorithm to identify and predict the plant lead disease. Easily identification of diseases will be very helpful because it will increase the productivity and reduce chemicals on the plants. It will increase agriculture yield with minimum cost which is very helpful now a days and we can get accuracy of 90% if dataset is good.  CNN, Raspberry Pi, Python

title: A Hybrid of Plant Leaf Disease and Soil Moisture Prediction in Agriculture Using Data Mining Techniques
abstract: This paper focuses on developing novel technologies for monitoring the plant health, evaluating the water content level for different plants by analyzing the plant and soil images respectively. Initially, plants and soil images are captured through digital camera with a required resolution. The innerdistance shape context based descriptor and geometrical descriptors are used for extracting the shape and geometric features from the plant images. In addition, texture and color features are extracted from the soil images. By using the contour features of the plant images, the plant type is identified through the botanical plant species dictionary. The leaf diseases are predicted by Transductive Support Vector Machine classification. The causes for the specific plant disease are identified based on the Latent Dirichlet Allocation and Artificial Neural Network classification technique through the features of soil images and the diseased plant images. The obtained results are broadcasted to the cultivators through mobile phones by means of text messages on a daily and seasonal basis with possible suggestions of preventive measures.