Image Classification: From Fully Connected Networks to CNNs. A Weed Detection Approach.

The activity directly relates to the goals and objectives of the Smart Droplets project, particularly in the development and implementation of computer vision systems for anomaly detection and optimization of spraying operations. The activity focuses on studying the main differences between fully connected neural networks and convolutional neural networks (CNNs) and their suitability for image processing tasks. Fully connected neural networks: The activity highlights the drawbacks of using fully connected networks for image processing tasks. This is relevant to the Smart Droplets project as it involves analyzing visual data from RGB cameras for anomaly detection and dosage recommendation. By understanding the limitations of fully connected networks, the project can make informed decisions regarding the choice of neural network architectures for effective image analysis. CNNs: The activity emphasizes the importance of CNNs in handling image data due to their ability to capture local patterns and spatial hierarchies. CNNs are specifically designed for image processing tasks and excel at extracting features from images. This aligns with the Smart Droplets project’s objective of developing computer vision systems using AI techniques to analyze real- time image feeds and make accurate recommendations for optimizing spraying operations. Computational efficiency: The activity mentions that fully connected networks can become computationally expensive as the number of neurons increases. This is an important consideration for the Smart Droplets project, as the AI models developed for anomaly detection and dosage recommendation need to be efficient and operate in real-time on edge devices. Understanding the computational implications of different network architectures helps in selecting the most suitable approach for the project’s requirements.