Combined UAV‐ and tractor‐based stripe rust monitoring in winter wheat under field conditions

Abstract

Unmanned aerial vehicles (UAVs) have the potential to monitor the health status of several crop fields of a farmer in full coverage. For a complete and fast monitoring, however, high flight altitudes are usually needed, especially if large areas should be observed in short time intervals. In this case, the resolution on the ground becomes insufficient to detect specific symptoms of crop diseases because a ground resolution in the submillimeter scale is needed. This study pursued the idea of performing remote UAV imaging to detect discoloration in combination with near‐surface tractor imaging to detect the uredospore layers as characteristic signs of stripe (yellow) rust (caused by Puccinia striiformis Westend. f. sp. tritici) in winter wheat (Triticum aestivum L.). To simulate healthy and diseased field parts, the 3‐yr experimental design included controlled infected plots and those sprayed with fungicides as healthy controls. Imaging, disease severity, and crop development rating were performed along a time series. Significant differences between infected and control plots occurred in the UAV imagery using the normalized green–red difference index from a median (upper three leaves) infested leaf area of 3% and for the tractor images using the maximally stable extreme regions detector from 3 and 5%, respectively. In the future, it is conceivable that farmers will combine UAV (aerial monitoring of crop damage of complete fields) and tractor (ground monitoring to determine the cause) imaging for automatic scanning of the health status.

Using Ground and UAV Vegetation Indexes for the Selection of Fungal-Resistant Bread Wheat Varieties

“…A moderate temperature with humidity has been found to facilitate the penetration of Fusarium pseudograminearum and the infection of wheat in Australia at around 15 • C and reduces the wheat resistance to the disease [50]. The same was demonstrated in other studies, where they also indicated that the most pathogenic conditions for cereals were in warm temperatures and under high humidity [49,51] RGB and NDVI vegetation indexes and their changes over the growing season can be used as tools to learn more about the state of a plant and, in this case, whether the fungicide treatment was beneficial towards the crop status for different varieties. Almost all of the different vegetation indexes differentiated fungicide-treated plants compared to untreated ones for at least one site or visit time, but TGIveg, GA, and CSI were the most consistent from the ground level.…”

“…However, Ejea de los Caballeros exhibited one of the greatest differences between treated and untreated for the two visits during the month of May (see GA and CSI in Figures 6 and 7), possibly related to the heavy rains that it experienced in late April (Figure 2e), which may have done more to intensify the fungal infection for the remainder of the season. Thus, we suggest that the severity of fungal attack on wheat crops was determined by the combination of the environment, timing of the infection with the wheat cycle, and geographic location [49].…”

“…Additionally, fungal attacks act as a form of stress on plants by causing a drop in chlorophyll content and crop vigor. According to Dammer et al [49], NGRDI can be a useful index for identifying plant stress in general. This index has a strong correlation with grain yield, which decreases under stressful conditions.…”

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

“…Sensors represent one of the fundamental building blocs of any UAV to perform different tasks autonomously with little or no human intervention through intelligent algorithms, including navigation, detect and geolocating potential diseases in crops from visual data, and providing a map of crops status that could be helpful to the farmers or other machines working in coordination with the UAV Dammer et al [ 24 ], Lytridis et al [ 51 ]. Remote sensing systems that are based on camera sensors mounted on UAV platforms can be classified according to two main factors, which are the UAV type and the camera sensor type.…”

Remote sensing monitoring of wheat leaf rust based on UAV multispectral imagery and the BPNN method

“…Besides, machine learning regression techniques had been used to investigate leaf rust disease detection using hyperspectral measurement. The above studies provide essential guidance for early monitoring of crop diseases at the field scale (Ashourloo et al, 2016; Karl‐Heinz et al, 2021; Nduku et al, 2023).…”