(download the dataset from https://www.kaggle.com/datasets/arjuntejaswi/plant-village )
The world's population is growing, and as such, the demand for food is increasing. Agriculture is one of the most critical sectors in ensuring food security. However, crop losses due to diseases pose a significant threat to global food production. Early identification of diseases can help farmers and plant pathologists take necessary measures to minimize crop losses.
The project "Leaf Disease Recognition " aims to develop a machine learning model to identify different diseases affecting plant leaves. The model utilizes the power of convolutional neural networks (CNN), a deep learning algorithm that can analyze and classify images with remarkable accuracy. TensorFlow, an open-source software library developed by Google, is used to build and train the CNN.
The first step in the project is data collection, where a dataset of plant images containing healthy and diseased leaves is assembled. The images are preprocessed and augmented to improve the quality of the data. The dataset is then divided into training and testing sets for model evaluation.
The CNN architecture is designed using TensorFlow's high-level API, Keras. The model is trained using the training set and evaluated using the testing set. The performance of the model is evaluated based on metrics such as accuracy, precision, and recall.
Once the model is trained and tested, it can be used to classify new images of plant leaves as healthy or diseased. The model's accuracy can be further improved by fine-tuning the hyperparameters or by using transfer learning to leverage pre-trained models.
The proposed model has the potential to revolutionize plant disease diagnosis and improve crop yields. By providing farmers and plant pathologists with an efficient and accurate tool to identify diseases at an early stage, the model could help minimize crop losses and increase food production. Furthermore, the model can be used to monitor crops regularly and alert farmers and pathologists when the disease is detected.
In conclusion, the "Leaf Disease Recognition" project aims to develop a machine learning model to classify and identify different diseases affecting plant leaves. The proposed model utilizes the power of CNN and TensorFlow to learn from a large dataset of plant images containing healthy and diseased leaves. By providing an efficient and accurate tool to identify diseases at an early stage, the model has the potential to revolutionize plant disease diagnosis and improve crop yields.