The agricultural sector has witnessed a lot of contributionswhen it comes to AI & and computer vision in areas like plant healthdetection and monitoring, harvesting, analysis of weather conditions and livestock farming.
Plant diseases are significant threat to agriculturalproductivity 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 andefficiently is a challenging task, especially considering the vast variety of plant species and diseases.
The first step in developing a deep learning model for plantdisease 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 learing model “DenseNet” is constructed using Keras. This model is composed of multiple layers ofconvolutional, pooling, and dense units and activation function for disease classification.
We have further improved our model’s accuracy and predictionusing data augmentation during training stage that involves generatingsynthetic data by applying transformations like rotation, scaling, and flippingto the original images.Once the model is trained, it is evaluated using a separate validation datasetto assess its performance.
Furthermore, the concept of smart agriculture can be appliedin livestock farming, crop management, precision farming and soil management.
مؤسسة عرين الأسد للتسويق الإلكتروني
.webp)
