Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

Abstract

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

Automated Classification of Blood Loss from Transurethral Resection of the Prostate Surgery Videos Using Deep Learning Technique

“…Mean Residual Coefficient (EPOCH = 50 LR = 0.00001) Res-Unet model 0.28 Autoencoder model [31] 0.63 Segnet model [32] 0.70 Table 11. The comparison of removing the misjudgment area.…”

“…Mean Residual Coefficient (EPOCH = 50 LR = 0.00001) Res-Unet model 0.28 Autoencoder model [31] 0.63 Segnet model [32] 0.70 The proposed Res-Unet algorithm, which integrated the Resnet-50 and U-Net models, was used to perform the segmentation of the misjudgment of prostate surgery images. To evaluate the performance of the proposed algorithm, it is compared with two efficient algorithms, Autoencoder and Segnet.…”

“…Image semantic segmentation methods have been used in the precision agriculture. F. P. Jorge et al [31] utilized a deep convolutional network with an encoder-decoder in fig plant segmentation, which classified each pixel as a crop or non-crop of the raw images, and the experimental results showed a mean accuracy of 93.85%. M. Andres et al [32] designed the network architecture of combining the Segnet and Enet to separate sugar beet plants, weeds, and background images.…”

Lettuce Canopy Area Measurement Using Static Supervised Neural Networks Based on Numerical Image Textural Feature Analysis of Haralick and Gray Level Co-Occurrence Matrixs

“…Character points on leaf shape were recorded and used as basis for leaf area measurement in developing a leaf growth framework (Kierzkowski et al, 2019). On the other hand, a convolutional neural network (CNN) with modeled encoder-decoder architecture was developed to segment fig crop from its complicated background (Fuentes-Pacheco et al, 2019). Ten color vegetation indices were derived from RGB components of the image and assessed using SegNet-Basic deep learning model and the developed architecture.…”

Deep Learning for Soil and Crop Segmentation from Remotely Sensed Data

“…For example, an important but typical case is the application of variable rate nitrogen fertilizers, as discussed in [3]. Vineyards and fruit plants are also especially good examples of complex exercises in both crop detection and study for agricultural image segmentation [4] problems such as weed detection [5], nitrogen application at hot-spots and selective harvesting.…”