Multi-Spectral Image Synthesis for Crop/Weed Segmentation in Precision Farming

Abstract

An effective perception system is a fundamental component for farming robots, as it enables them to properly perceive the surrounding environment and to carry out targeted operations. The most recent methods make use of state-of-the-art machine learning techniques to learn a valid model for the target task. However, those techniques need a large amount of labeled data for training. A recent approach to deal with this issue is data augmentation through Generative Adversarial Networks (GANs), where entire synthetic scenes are added to the training data, thus enlarging and diversifying their informative content. In this work, we propose an alternative solution with respect to the common data augmentation methods, applying it to the fundamental problem of crop/weed segmentation in precision farming. Starting from real images, we create semi-artificial samples by replacing the most relevant object classes (i.e., crop and weeds) with their synthesized counterparts. To do that, we employ a conditional GAN (cGAN), where the generative model is trained by conditioning the shape of the generated object. Moreover, in addition to RGB data, we take into account also near-infrared (NIR) information, generating four channel multi-spectral synthetic images. Quantitative experiments, carried out on three publicly available datasets, show that (i) our model is capable of generating realistic multi-spectral images of plants and (ii) the usage of such synthetic images in the training process improves the segmentation performance of state-of-the-art semantic segmentation convolutional networks.

References in corpus (7)

• Conditional Generative Adversarial Nets
• Fully Convolutional Networks for Semantic Segmentation
• Fully Convolutional Networks with Sequential Information for Robust Crop and Weed Detection in Precision Farming
• An empirical study on evaluation metrics of generative adversarial networks
• Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection
• Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution
• Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer

Cited by in corpus (5)

• PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain
• Shape and Style GAN-based Multispectral Data Augmentation for Crop/Weed Segmentation in Precision Farming
• PlantPlotGAN: A Physics-Informed Generative Adversarial Network for Plant Disease Prediction
• WasteGAN: Data Augmentation for Robotic Waste Sorting through Generative Adversarial Networks
• deepNIR: Datasets for generating synthetic NIR images and improved fruit detection system using deep learning techniques