Neural Network Algorithms for Real Time Plant Diseases Detection Using UAVs

Abstract

Precision agriculture aims to optimize investments and yields taking into account environmental and conditions variability between different agronomic substrates. It has influenced every aspect of agriculture such as tillage, seeding, fertilization, irrigation and pesticide spraying. The crop management optimization is the main goal of precision agriculture and it could be achieved from a triple point of view: (i) Agronomic: improvement of inputs/yields efficiency such as the choice of varieties more adapted to agricultural context; (ii) Environmental: reducing the risks to human health and environment minimizing the use and release of nitrates, phosphates and pesticides; (iii) Economic: reducing energy consumption and chemical inputs while increasing yields. In recent years, Unmanned Aerial Vehicles (UAVs) have been used in agriculture as part of photogrammetric and remote sensing tasks, but new opportunities arise also in real time pathogen detection and pesticide distribution. To this purpose, high resolution images are required, introducing a technical complexity linked to data transfer and storage. One possibility is to store only images requiring eventually a post processing operation, dropping all images proposing a standard healthy crop. The use of Artificial Intelligence (AI), and Deep Learning (DL) in particular, allows larger learning capabilities and thus higher performance and precision of real-time classification and detection. The aim of this work is to develop and test a DL model in order to detect in real-time plants diseases. The neural network proposed in this work has been trained using a dataset of RGB images, then tested on a test set. The system adopted a Convolutional Neural Network (CNN) as feature extractors from the input images and TensorFlow as framework, showing good results in disease detection.