Predicting Crop Pests Using AI

TEACHING UNIT 1. PESTS IN PROTECTED AGRICULTURE 1. Most common pests in greenhouse crops 2. Life cycles and environmental conditions that favor their proliferation 3. Economic and productive impact of pests in intensive horticulture 4. Traditional techniques for pest monitoring and control 5. Limitations of conventional methods in early prediction TEACHING UNIT 2. ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN AGRICULTURE 1. AI, machine learning, and deep learning 2. Types of models used in agriculture 3. Current tools and platforms for implementing AI in agricultural settings 4. Successful applications of AI in the diagnosis and prevention of diseases and pests 5. Ethics, sustainability, and challenges in the use of AI in agribusiness LEARNING UNIT 3. DATA COLLECTION AND PROCESSING IN SMART GREENHOUSES 1. Agricultural sensors and IoT systems: types, functions, and deployment in greenhouses 2. Critical variables for pest prediction 3. Integration of multispectral, RGB, and thermal images into monitoring 4. Data storage, labeling, and cleaning for model training 5. Data quality and its impact on the predictive accuracy of algorithms COURSE UNIT 4. DEVELOPMENT AND TRAINING OF PREDICTIVE MODELS 1. Selection of relevant variables and design of datasets 2. Training, validation, and evaluation of predictive models 3. Model improvement techniques: hyperparameter tuning, regularization, and model ensembling 4. Use of computer vision for pest identification in images 5. Early detection and automated AI-based early warning systems