BIO VISION
The Problem
In India, we lose over $11 Billion worth of crop every year due to diseases
Parameters Required for Health Monitoring
Deep – Learning Model
Model Objective:
Detect and classify plant diseases using deep learning to aid farmers and gardeners in early detection and management.
Model Architecture:
Detect and classify plant diseases using deep learning to aid farmers and gardeners in early detection and management.
- Convolutional Layers: Extract features from input images.
- Max-Pooling Layers: Reduce the spatial dimensions of the feature maps.
- Flatten Layer: Convert 2D matrices to a 1D vector.
- Dense Layers: Perform classification based on extracted features.
- ReLU for intermediate layers to introduce non-linearity.
- Softmax for the output layer to provide probability distributions across classes.
Deep – Learning Model Output
The confusion matrix is a performance measurement tool for the classification model. It provides a comprehensive overview of how well the model is performing in distinguishing between different classes.
Input
Output
Feasibility
- Farmers can use the system with minimal technical knowledge, as the interface is designed to be user-friendly.
- Although the initial cost of the system is high, the system in the long term pays for itself through improved productivity and reduced losses within a few growing seasons.
- The solution can be scaled to farms of various sizes and types, making it versatile and adaptable to different agricultural environments in terms of scalability.
- The system promotes sustainable farming practices by reducing the reliance on chemical treatments in the sustainability aspect.
INCREASE THE DATASET SIZE
Increases the amount of data for the model to use would increase the accuracy of each prediction as it simply learns more with each datapoint.
IMPROVE THE PROTOTYPE DESIGN TO BE PRODUCT-MARKET FIT
Making the design more friendly to the needs of the use case is imperative, especially functionality such as being able to be used in different kinds of terrains.
TO MAKE IT FULLY AUTONOMOUS
Making it fully autonomous is very important in its mission and complete functionality.
