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Peanut is a significant oilseed crop that is often affected by peanut southern blight, a disease that greatly reduces crop yield and quality. Therefore, accurate and timely monitoring of this disease is crucial to ensure crop safety and minimize the need for pesticides. Spectral features combined with texture features have been widely applied in plant disease monitoring. However, previous studies have mostly used original texture features, and its combination form has been rarely considered. This study presents a novel approach for monitoring peanut southern blight, integrating multiple spectral indices and textural indices (TIs). Firstly, a total of 20 vegetation indices (VIs) were extracted from the unmanned aerial vehicle multispectral images, while three TIs were constructed based on original textural features. Subsequently, Otsu-CIgreen algorithm was used to find the optimal threshold to eliminate the complex background of the image. Lastly, monitoring models for peanut southern blight were constructed using three machine learning models based on the screened VIs, VIs combined with TIs. Among these models, the K-nearest neighbor model using VIs combined with TIs demonstrates the best performance, with accuracy and F1 score on the test set reaching 91.89% and 91.39% respectively. The results indicate that the monitoring models utilizing VIs and TIs were more effective compared to models using only VIs. This approach provides valuable insights for non-destructive and accurate monitoring of peanut southern blight. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-based remote sensing has been widely considered recently in field scale crop yield estimation. In this research, the capability of 13 spectral indices in the form of 5 groups was studied under different irrigation water and N fertilizer managements in terms of corn biomass monitoring and estimation. Farm experiments were conducted at Urmia University, Iran. The research was done using a randomized complete block design at three levels of 60, 80, and 100% of irrigation water and nitrogen requirements during four replications. The aerial imagery operations were performed using a fixed-wing UAV equipped with a Sequoia sensor during three plant growth stages including stem elongation, flowering, and silking. The effect of different irrigation water and nitrogen levels on vegetation indices and crop biomass was examined using variance decomposition analysis. Then, the correlation of the vegetation indices with corn biomass was evaluated by fitting linear regression models. Based on the obtained results, the indices based on near infrared (NIR) and red-edge spectral bands showed a better performance. Thus, the MERIS terrestrial chlorophyll index (MTCI) indicated the highest accuracy at estimating corn biomass during the growing season with the R2 and RMSE values of 0.92 and 8.27 ton/ha, respectively. Finally, some Bayesian model averaging (BMA) models were proposed to estimate corn biomass based on the selected indices and different spectral bands. Results of the BMA models revealed that the accuracy of biomass estimation models could be improved using the capabilities and advantages of different vegetation indices. [ABSTRACT FROM AUTHOR]

Accurately identifying the quantity of maize seedlings is useful in improving maize varieties with high seedling emergence rates in a breeding program. The traditional method is to calculate the number of crops manually, which is labor-intensive and time-consuming. Recently, observation methods utilizing a UAV have been widely employed to monitor crop growth due to their low cost, intuitive nature and ability to collect data without contacting the crop. However, most investigations have lacked a systematic strategy for seedling identification. Additionally, estimating the quantity of maize seedlings is challenging due to the complexity of field crop growth environments. The purpose of this research was to rapidly and automatically count maize seedlings. Three models for estimating the quantity of maize seedlings in the field were developed: corner detection model (C), linear regression model (L) and deep learning model (D). The robustness of these maize seedling counting models was validated using RGB images taken at various dates and locations. The maize seedling recognition rate of the three models were 99.78% (C), 99.9% (L) and 98.45% (D) respectively. The L model can be well adapted to different data to identify the number of maize seedlings. The results indicated that the high-throughput and fast method of calculating the number of maize seedlings is a useful tool for maize phenotyping. [ABSTRACT FROM AUTHOR]

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In this paper, detection of objects in airborne systems of unmanned aerial vehicles using information about changes in the image of the terrain is considered. A proposed technique includes initial detection of objects by the level of local contrast over the field of the image misalignment function, calculation of the amplitude characteristics of objects, and their further filtering by a set of calculated values of characteristics. [ABSTRACT FROM AUTHOR]

Landslides are an important type of natural disaster that can cause catastrophic destruction of infrastructure and result in human casualties. An accurate digital surface model (DSM) is essential for analysis of landslide stability and associated risks. This work presents a rapid modeling method of landslide topography by combining high-resolution imagery from an unmanned aerial vehicle (UAV) with structure-from-motion (SfM) photogrammetry. Because of the relatively high efficiency and low costs, the micro-UAV system is suitable for emergency investigation of landslide disaster when compared to geodetic or other remote-sensing techniques, especially for inaccessible landslide area after earthquakes. Based on the micro-UAV images, the use of SfM combines well-established photogrammetric principles with modern computation to rapidly reconstruct a DSM. The relative error determined by comparison with measured data for the UAV-SfM technology is <1%. An unstable slope in Panyu District, Guangzhou City, China, was selected as a case study, in which a small UAV system was deployed for landslide investigation and generation of a highly efficient, cost-effective and user-friendly approach for 3D terrain modeling. [ABSTRACT FROM AUTHOR]