The agricultural sector has witnessed a lot of contributions when it comes to AI and computer vision in areas like plant health detection and monitoring, harvesting, analysis of weather conditions, and livestock farming.
Plant diseases are a significant threat to agricultural productivity, leading to losses, economic instability, and food insecurity. Early detection and timely intervention are crucial to prevent the spread of diseases and minimize crop damage. However, identifying diseases accurately and efficiently is a challenging task, especially considering the vast variety of plant species and diseases.
The first step in developing a deep learning model for plant disease detection is to collect a diverse dataset containing images of healthy and diseased plants. These images are then preprocessed to standardize their size, color, and orientation, ensuring consistency and compatibility with the model architecture.
Once the dataset is prepared, one of the deep learning models, DenseNet, is constructed using Keras. This model is composed of multiple layers of convolutional, pooling, and dense units, and an activation function for disease classification.
We have further improved our model’s accuracy and prediction using data augmentation during the training stage. This involves generating synthetic data by applying transformations like rotation, scaling, and flipping to the original images. Once the model is trained, it is evaluated using a separate validation dataset to assess its performance.
Furthermore, the concept of smart agriculture can be applied in:
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