Fusarium Wilt of Radish Detection Using RGB and Near Infrared Images from Unmanned Aerial Vehicles

Abstract

The radish is a delicious, healthy vegetable and an important ingredient to many side dishes and main recipes. However, climate change, pollinator decline, and especially Fusarium wilt cause a significant reduction in the cultivation area and the quality of the radish yield. Previous studies on plant disease identification have relied heavily on extracting features manually from images, which is time-consuming and inefficient. In addition to Red-Green-Blue (RGB) images, the development of near-infrared (NIR) sensors has enabled a more effective way to monitor the diseases and evaluate plant health based on multispectral imagery. Thus, this study compares two distinct approaches in detecting radish wilt using RGB images and NIR images taken by unmanned aerial vehicles (UAV). The main research contributions include (1) a high-resolution RGB and NIR radish field dataset captured by drone from low to high altitudes, which can serve several research purposes; (2) implementation of a superpixel segmentation method to segment captured radish field images into separated segments; (3) a customized deep learning-based radish identification framework for the extracted segmented images, which achieved remarkable performance in terms of accuracy and robustness with the highest accuracy of 96%; (4) the proposal for a disease severity analysis that can detect different stages of the wilt disease; (5) showing that the approach based on NIR images is more straightforward and effective in detecting wilt disease than the learning approach based on the RGB dataset.

Monitoring Wheat Leaf Rust and Stripe Rust in Winter Wheat Using High-Resolution UAV-Based Red-Green-Blue Imagery

“…In the former site particularly, where two cultivars were available (Table 1), WLR and WSR severities were statistically different between cultivars (p < 0.001). WLR and WSR have been recorded, sometimes at substantial levels (30 to 65%), in Luxembourg over these past years [5,18,42,43]. With weather patterns projected to change in the coming years in the GDL [44], it is expected that Luxembourgish wheat-growing areas may experience increased occurrences of WSR and WLR, which will require more efficient integrated disease management (e.g., informed decisions based on support tools or systems, use of resistant cultivars, etc.).…”

“…With the continuing advances in automated phenotyping systems through sophisticated sensors, high-throughput phenotyping platforms (e.g. unmanned aerial vehicle (UAV) technology, tractor-mounted equipment, field robots), and advanced data analytics, the automatic identification, classification (healthy versus diseased) and quantification of symptoms of plant diseases have the potential to address such hurdles [11,[14][15][16][17][18][19]. However, such image-based disease identification and quantification is not always straightforward because of the potential changes in spectral response of diseased leaves.…”

Daily and seasonal heat usage patterns analysis in heat networks

“…By taking full advantage of the advancement of artificial intelligence (AI) in recent years 12 , 13 , the data-based heat usage prediction models show promising results 7 . Moreover, deep learning, which is a specialized area of Machine Learning (ML) that allow computers to learn from and make predictions about data automatically, has progressively been a default choice in various domains, such as Computer Vision (CV) 14 and natural language processing (NLP) 15 . Commonly used ML algorithms in heat usage patterns predictions include moving average 16 , multiple linear regression 17 , regression tree 18 , support vector regression (SVR) 19 , extreme gradient boosting (XGBoost) 20 , and deep learning 15 .…”

A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images

“…Applying MLP, they were able to identify TS and BS diseases from UAV imagery with an accuracy of 97% and 98%, respectively. Dang et al [ 25 ], proposed RadRGB model to classify Fusarium Wilt Disease in radish crops. Compared to VGG-16 and Inception-V3, the proposed architecture provides the best results in terms of accuracy and testing time of 96.4% and 0.043 s/image, respectively, while VGG-16 and Inception-V3 achieved slightly lower accuracies of 93.1% and 95.7%, respectively, and longer testing time of 0.1 (VGG-16) and 0.22 (Inception-V3) s/image (Table 3 ).…”

“…For example, the authors in Tetila et al [ 75 ] adopted four state-of-the-art CNN architectures to classify soybean diseases, which are Inception-V3, ResNet-50, VGG-19, and Xception. Other researchers developed their own custom CNN architectures to classify diseases, including the study of Dang et al [ 25 ].…”