Paddy Crop Disease Detection Using Machine Learning

In our paper we detect diseases in paddy (rice) crops by utilizing machine learning will aid farmers to make informed decisions, to make sure that the timely involvements and decreases crop losses. By using the latest techniques we develop our project according to scholars interests along with a brief explanation. This task mainly includes image classification on the basis of leaf or plant images.

 Here we had given a step-by-step guidance to execute a paddy crop disease detection project:

  1. Objective Definition

            State the goal: “To design machine learning method that will accurately find and classify disease in paddy crops by utilizing images”.

  1. Data Collection
  • Custom Dataset: Gather images of healthy paddy crops and those include some common diseases like Rice Blast, Bacterial Leaf Blight, Sheath Blight, etc. To make sure that varied lighting conditions, phases of disease and camera angles.
  • Public Datasets: We search for pre-existing datasets on agricultural platforms or academic resources.
  1. Data Preprocessing
  • Image Resizing: Our work regulates the size of all images.
  • Image Augmentation: We produce different versions of images (rotated, zoomed, flipped) to improve data diversity and enhance generalization.
  • Normalization: In our work we normalize the pixel values to fall surrounded by a standard range, typically [0, 1].
  1. Exploratory Data Analysis (EDA)
  • Our work visualizes samples from each disease group.
  • We analyze the distribution of classes to check for imbalances. Some of the methods like oversampling, undersampling or utilizing the SMOTE method addresses class imbalance.
  1. Feature Extraction
  • Transfer Learning: Our paper uses pretrained methods (e.g., VGG16, ResNet, MobileNet) to retrieve characters from images.
  • Deep Learning from Scratch: In our work we utilize CNNs to study characters directly from our data.
  1. Model Selection & Training
  • Deep Learning Models: CNNs are powerful for image-based challenges. We acknowledge designs like ResNet, EfficientNet or a custom CNN.
  • Transfer Learning: We initiate with models pretrained on big datasets (like ImageNet) and fine-tune them on our paddy disease dataset.
  1. Model Evaluation
  • Accuracy: Our work measures the whole classification success.
  • Confusion matrix: Gain understandings into particular misclassifications.
  • Other Metrics: Think about the metrics precision, recall, F1-score, especially if there is class imbalance.
  1. Optimization & Hyperparameter Tuning
  • We utilize the methods like dropout and batch normalization to enhance convergence and counteract overfitting.
  • Our work uses grid search or random search to optimize hyperparameters such as learning rate, batch size, number of layers, etc.
  1. Deployment
  • Change the model into a mobile or web app utilizing platforms like TensorFlow Lite, Flask or Streamlit. This tools are utilized by farmers to analyze paddy diseases in real-time.
  1. Feedback Loop & Continuous learning
  • Our work permits users to offer feedback on predictions.
  • We gather misclassified cases and periodically retrain the model.
  1. Conclusions & Future Enhancements
  • Document the tasks, results, and fields for enhancements.
  • Our Future work could involve:
  • Multispectral Images: We utilize multispectral or hyperspectral images to detect diseases even previously.
  • Location-based Advice: Combine with GIS (Geographical Information System) to offer location-specific advice.
  • Integration with pest Detection: In our work we expand the model to detect pest infestations.


  • Data Diversity: To make sure that the representation of various disease stages and severities.
  • Annotations: If the model offers bounding boxes around disease spots, to make sure accurate annotations utilize tools like Labelbox or VGG Image Annotator.
  • Local Conditions: Think about the variations in diseases and work together with agronomists or local agricultural specialists for accurate data labeling.

Our work uses a system when it effectively executed and deployed will be a boon for farmers, helps in early detection, and allowing timely interventions to project their crops.

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Paddy Crop Disease Detection using Machine Learning Ideas

Paddy Crop Disease Detection Using Machine Learning Thesis Topics

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  1. Disease Detection in Paddy Crop using Machine Learning Techniques


Transfer learning, Convolutional Neural Network, Image Processing, Machine Learning, Paddy crop disease

            Our paper proposes a ML method to detect and identify the paddy plants disease by getting effective and best solution. We can detect the paddy plant disease by utilizing CNN method with MobileNetV2 and transfer learning method to classify the disease with photos. Our model overcomes the challenge of overfitting by concentrate on training more data and data augmentation. Our model train the overall model to get a increased accuracy. 

  1. Smart Agriculture Framework for Automated Detection of Leaf Blast Disease in Paddy Crop Using Colour Slicing and GLCM Features based Random Forest Approach


Colour slicing, Grey level co-occurrence matrices, Internet of things, Paddy, Smart agriculture

           Our study proposes an automated framework for detecting blast disease affected leaves of paddy plants with Image processing and ML techniques. We also utilize colour slicing and grey level co-occurrence matrices (GLCM) method for processing and texture- related feature extraction of leaf image of paddy crop. We use the ML classifiers like RF, KNN, DT, XGBoost, AdaBoost and histogram based gradient boosting method. Our colour slicing and GLCM feature based RF classification method is the correct selection for detection. 

  1. An optimized machine learning framework for crop disease detection


Crop Disease Detection, Random Forest, Feature Extraction, Krill Herd Optimization, Classify Crop Disease

            Our work proposes a Krill Herd based Random Forest (KHbRF) to detect the crop disease accurately and improve the performance of detection accuracy by utilizing an optimized fitness function. To update the classification layer for effective crop disease detection we utilized krill herd fitness function. Also the development processes like preprocessing, segmentation, feature extraction and classification. Finally the classification layer detects the crop disease in the dataset by utilizing the fitness of krill herd.   

  1. Deep learning system for paddy plant disease detection and classification


Computer vision, Deep learning, Support vector machine, Image segmentation

            To detect and classify the disease accurately from a provided photo an automated approach can be used in our paper. The detection of rice plant disease accept a computer vision- based approach that employs the method of image processing, ML and DL method to decrease the support on conventional methods to safe paddy crop from diseases. We also used image pre-processing and image segmentation to find the diseased part. SVM classifier and CNN are utilized to classify and identify the particular kind of paddy plant disease. 

  1. K-Means Clustering Algorithm for Crop Leaf Disease Detection



Training, Model, Histogram, Decision Making

            Our paper proposes an Image processing and ML methods to improve the scalability and early crop detection. To get the high accuracy our paper calculates some images to find various crop diseases. In implementation process we utilized three processes like image preprocessing, feature extraction and train the ML model. Then at last we give the new image that was in in our trained dataset to predict the best accuracy.  

  1. Design of a Crop Disease Detection Model using Multi-parametric Bio-inspired Feature Representation and Ensemble Classification


Crop, Disease, KNN, MLP, LR, Accuracy, Precision, Recall, AUC

            Our paper proposes a disease detection and yield prediction model through multi-parametric bio inspired feature representation. We first utilize a cross-specific adaptive threshold method that proposes an efficient segmentation for variety of crops. The segmented image can be processed through multiple feature extraction units and that features can be processed through GA based feature selection, then the selected feature set can be classified using the classification model like SVM, MLP, LR, DT and NB. 

  1. Recent Advances in Crop Disease Detection Using UAV and Deep Learning Techniques


UAV; drone; remote sensing; detection; classification; segmentation

            Our goal is to analyse the actual progress in crop disease detection with an importance of ML and DL techniques by utilizing UAV based remote sensing. To detect the crop disease with UAV imagery initially we increase the importance of various sensors and image processing methods and Second, we propose taxonomy to collect and classify the previous work. Third we analyse and summarize the different ML and DL method for crop disease detection. At last we underscore the UAV based disease detection. 

  1. Automation of Crop Disease Detection through Conventional Machine Learning and Deep Transfer Learning Approaches


Traditional machine learning; classification accuracy; deep learning optimizers; activation functions

            We conduct a comparative study between traditional ML (SVM, LDA, KNN, CART, RF and NF) and deep transfer learning (VGG16, VGG19, InceptionV3, ResNet50 and CNN) methods in terms of metrics precision, accuracy, f1score and recall on a dataset. We also apply several activation function and DL optimizers to improve the CNN performance. InceptionV3 gives the good classification accuracy.

  1. Rice Crop Disease Detection Using Machine Learning Algorithms


Disease detection, Rice disease

            Our paper uses ML methods to predict the result. We can select the classification method is difficult as the outcome can vary based on input data. KNN, PNN, Genetic Algorithm, SVM, PCA, ANN and fuzzy logic are some different classification methods. In real life naked eye observation is the classic method for plant disease detection and identification. 

  1. A Two-Step Machine Learning Approach for Crop Disease Detection Using GAN and UAV Technology


Automated plant disease detection; data augmentation, generative adversarial networks

            We propose a two-step ML technique to analyse low and high-fidelity data that are characteristic of UAV images. Two data-generators were also utilized in our paper to reduce class imbalance in high fidelity dataset and low fidelity data that give the representative of UAV images. A ML identifier finds whether the plant is diseased or not and labels them.  


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