INTELLIGENT CUSTOMER SERVICE PLATFORM (ICSP) USING AI ALGORITHMS FOR AUTOMATED SUPPORT
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Abstract
Plant diseases have major implications on agricultural productivity and food security worldwide. It is even more important for plant disease detection methods to be early and highly specific with respect to effective crop management and yield improvement. Machine learning has evolved into this promising tool to automate identification of plant diseases with pattern recognition techniques based on leaf images and related data.
In this framework, classification algorithms like CNNs are trained on huge datasets of plant images to detect visual symptoms of a large number of diseases with high accuracy. The machine-learning-based approach distinguishes between healthy and infected plants and classifies different disease types, allowing timely intervention and limiting dependency on manual inspection. It goes without saying that such systems enjoy more use in actual field conditions if integrated into mobile applications and drones.
This study centers around this area of using ML in plant disease detection with emphasis on accuracy, datasets, and real-world implementation challenges. These are poised to act as smart plant health monitoring systems for sustainable agriculture.
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References
Member 1: Data Collection & Preprocessing
Role: Collect leaf images, preprocess (resize, normalize, augment) them for ML model training.
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using Deep Learning for Image-Based Plant Disease Detection. [arXiv:1604.03169](https://arxiv.org/abs/1604.03169)
Kaggle Dataset: [PlantVillage Dataset] ( https://www.kaggle.com/emmarex/plantdisease )
Member 2: Model Design & Training
Role: Build and train machine learning models (e.g., CNNs) using frameworks like TensorFlow or PyTorch.
Too, E. C., Yujian, L., Njuki, S., & Yingchun, L. (2019). A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture, 161, 272–279.
TensorFlow Tutorial: [Image Classification] ( https://www.tensorflow.org/tutorials/images/classification )
Member 3: Evaluation & Optimization
Role: Evaluate model performance using metrics like accuracy, precision, recall; tune hyperparameters.
Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318.
Scikit-learn Documentation: [Model Evaluation] ( https://scikit-learn.org/stable/modules/model_evaluation.html )