Year: 2026
Type: dissertation
Abstract: Agricultural production across regions faces several challenges in cultivation, including climate variability, pests, diseases, and weeds. Deep neural network models have proven efficient in addressing some of these challenges, for example, in identifying and classifying weeds. A practical way for a farmer to monitor the health of their crop is to take a picture of a plant using their mobile device’s camera and run an application on the device that analyzes the image using a neural network. If it identifies the invasive plant, the application can determine the weed type in the image. The problem with developing this type of application is that many deep learning models require significant computational power, making them unsuitable for mobile devices. To mitigate this problem, we developed a pipeline for evaluating deep learning models suitable for weed classification. Our solution uses a set of metrics to assess both computational efficiency and model performance. Furthermore, we use our pipeline to generate an optimized model that performs inference on mobile devices with performance comparable to that of traditional deep learning models.
Author: Sanches, Leonardo Goshi
Language: Portuguese
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W7167994211