Leaf Disease Detection and Prevention using Ensemble Deep Learning

Описание: Leaf Disease Detection and Prevention using Ensemble Deep Learning using Python..
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Abstract:
Leaf diseases pose a significant threat to plant health and agricultural productivity, leading to reduced crop yields and economic losses. Early detection and accurate diagnosis of such diseases are essential for minimizing damage and applying timely treatments This project proposes an advanced approach utilizing ensemble deep learning models to predict and classify leaf diseases. Upon detecting a disease, the system provides relevant treatment methods tailored to the identified condition.

Objective:
To design and implement an ensemble deep learning model combining VGG16, InceptionResNet, and MobileNet architectures to accurately predict leaf diseases from images. To create a system capable of determining whether a leaf is affected by disease or is healthy, and to accurately classify the specific disease affecting the plant. To provide automated and personalized treatment suggestions based on the identified disease, including preventive measures and recommendations

Existing System:
Currently, there are several systems and methods employed for plant disease detection, most of which involve manual inspection. Some existing systems utilize basic ML model such as SVM to classify plant diseases based on leaf images. It focus on extracting features from leaf images, such as color patterns, texture, or shape. Once these features are extracted, algorithms are used to identify the presence of disease. These models typically require large datasets for training and can be quite time-consuming when handling large amounts of image data.

Proposed System:
Proposed system aims to revolutionize leaf disease detection and treatment recommendations by leveraging the power of deep learning and ensemble models. By combining the strengths VGG16, Inception ResNet, and MobileNet to achieve highly accurate leaf disease detection. The system incorporates data augmentation techniques, to artificially expand the dataset. The system operates by analyzing leaf images and classifying into two categories: diseased and healthy. For diseased leaves, the model further predicts the specific disease and provides personalized treatment recommendations to mitigate the condition.

Advantages :
More accurate and reliable predictions for leaf diseases
Reduce the chances of false positives and negatives
Cost-efficient
Reducing crop loss and increasing yield

Modules
Leaf Disease Dataset
Data Splitting and Data Augmentation
Ensemble Training Loop
Voting Method
Leaf Identification
Disease Identification
Treatment Recommendations