Monitoring Crop Carotenoids Concentration by Remote Sensing

Assessment of carotenoids (Car) content provides a valuable insight into clarifying the mechanisms of plant photoprotection and light-adaption and is critical for stress diagnoses in plants. Due to their small proportion in the overall total pigment content and to the overlapping of spectral absorption features with chlorophylls (Chl) in the blue region of the spectrum, accurate estimation of Car content in plants, from remotely sensed data, is challenging. Previous studies made progress in Car content estimation at both the leaf and canopy level with remote sensing techniques. However, established spectral indices and methods for Car estimation in most studies that generally rely on specific and limited measured data might lack predictive accuracy for Car estimation and lack sensitivity to low or high Car content in various species and at different growth stages. In this chapter, a new carotenoid index (CARI) was proposed for foliar Car assessment with abundant simulated leaf data and various measured leaf reflectances. Detailed analysis on the mechanism, formation and performance of the new spectral index on Car retrieval was presented. Analysis results suggested that accurate nondestructive estimation of foliar Car content with CARI could be achieved at the leaf scale, through remote sensing techniques

Application of UAV Remote Sensing in Monitoring Banana Fusarium Wilt

Fusarium wilt poses a current threat to worldwide banana plantation areas. To treat the Fusarium wilt disease and adjust banana planting methods accordingly, it is important to introduce timely monitoring processes. In this chapter, the multispectral images acquired by unmanned aerial vehicle (UAV) was used to establish a method to identify which banana regions were infected or uninfected with Fusarium wilt disease. The vegetation indices (VIs), including the normalised difference vegetation index (NDVI), normalised difference red edge index (NDRE), structural independent pigment index (SIPI), red-edge structural independent pigment index (SIPIRE), green chlorophyll index (CIgreen), red-edge chlorophyll index (CIRE), anthocyanin reflectance index (ARI), and carotenoid index (CARI), were selected for deciding the biophysical and biochemical characteristics of the banana plants. The relationships between the VIs and those plants infected or uninfected with Fusarium wilt were assessed using the binary logistic regression method. The results suggest that UAV-based multispectral imagery with a red-edge band is effective to identify banana Fusarium wilt disease, and that the CIRE had the best performance

Integrating Growth and Environmental Parameters to Discriminate Powdery Mildew and Aphid of Winter Wheat Using Bi-Temporal Landsat-8 Imagery

Monitoring and discriminating co-epidemic diseases and pests at regional scales are of practical importance in guiding differential treatment. A combination of vegetation and environmental parameters could improve the accuracy for discriminating crop diseases and pests. Different diseases and pests could cause similar stresses and symptoms during the same crop growth period, so combining growth period information can be useful for discerning different changes in crop diseases and pests. Additionally, problems associated with imbalanced data often have detrimental effects on the performance of image classification. In this study, we developed an approach for discriminating crop diseases and pests based on bi-temporal Landsat-8 satellite imagery integrating both crop growth and environmental parameters. As a case study, the approach was applied to data during a period of typical co-epidemic outbreak of winter wheat powdery mildew and aphids in the Shijiazhuang area of Hebei Province, China. Firstly, bi-temporal remotely sensed features characterizing growth indices and environmental factors were calculated based on two Landsat-8 images. The synthetic minority oversampling technique (SMOTE) algorithm was used to resample the imbalanced training data set before model construction. Then, a back propagation neural network (BPNN) based on a new training data set balanced by the SMOTE approach (SMOTE-BPNN) was developed to generate the regional wheat disease and pest distribution maps. The original training data set-based BPNN and support vector machine (SVM) methods were used for comparison and testing of the initial results. Our findings suggest that the proposed approach incorporating both growth and environmental parameters of different crop periods could distinguish wheat powdery mildew and aphids at the regional scale. The bi-temporal growth indices and environmental factors-based SMOTE-BPNN, BPNN, and SVM models all had an overall accuracy high than 80%. Meanwhile, the SMOTE-BPNN method had the highest G-means among the three methods. These results revealed that the combination of bi-temporal crop growth and environmental parameters is essential for improving the accuracy of the crop disease and pest discriminating models. The combination of SMOTE and BPNN could effectively improve the discrimination accuracy of the minor disease or pest

A deep learning-based approach for automated yellow rust disease detection from high resolution hyperspectral UAV images

Yellow rust in winter wheat is a widespread and serious fungal disease, resulting in significant yield losses globally. Effective monitoring and accurate detection of yellow rust are crucial to ensure stable and reliable wheat production and food security. The existing standard methods often rely on manual inspection of disease symptoms in a small crop area by agronomists or trained surveyors. This is costly, time consuming and prone to error due to the subjectivity of surveyors. Recent advances in Unmanned Aerial Vehicles (UAVs) mounted with hyperspectral image sensors have the potential to address these issues with low cost and high efficiency. This work proposed a new deep convolutional neural network (DCNN) based approach for automated crop disease detection using very high spatial resolution hyperspectral images captured with UAVs. The proposed model introduced multiple Inception-Resnet layers for feature extraction and was optimized to establish the most suitable depth and width of the network. Benefiting from the ability of convolution layers to handle three-dimensional data, the model used both spatial and spectral information for yellow rust detection. The model was calibrated with hyperspectral imagery collected by UAVs in five different dates across a whole crop cycle over a well-controlled field experiment with healthy and rust infected wheat plots. Its performance was compared across sampling dates and with random forest, a representative of traditional classification methods in which only spectral information was used. It was found that the method has high performance across all the growing cycle, particularly at late stages of the disease spread. The overall accuracy of the proposed model (0.85) was higher than that of the random forest classifier (0.77). These results showed that combining both spectral and spatial information is a suitable approach to improving the accuracy of crop disease detection with high resolution UAV hyperspectral images

The Suitability of PlanetScope Imagery for Mapping Rubber Plantations

Quickly and accurately understanding the spatial distribution of regional rubber resources is of great practical significance. Using the unique phenological characteristics of rubber trees derived from remotely sensed data is a common effective method for monitoring rubber trees. However, due to the lack of high-quality images available during the key phenological period, it is still very difficult to apply this method in practical applications. PlanetScope data with high temporal (daily) resolution have great advantages in acquiring high-quality images, but these images have not been previously used to monitor rubber plantations. In this paper, multitemporal PlanetScope images were used as data sources, and the spectral features, index features, first principal components, and textural features of the images were comprehensively utilized. Four classification methods, including a pixel-based random forest (RF) approach, pixel-based support vector machine (SVM) approach, object-oriented RF approach and object-oriented SVM approach, were utilized to discuss the feasibility of using PlanetScope data to monitor rubber forests. The results showed that the optimal time window for monitoring rubber forests in the study area spanned from the 49th day to the 65th day of 2019 according to the MODIS-NDVI analysis. The contribution rate of the difference in the modified simple ratio (dMSR) feature was largest among all considered features for all pixel-based and object-oriented methods. The object-oriented RF/SVM classification method achieved the best classification results with an overall accuracy of 93.87% and a Kappa index of agreement (KIA) of 0.92. The highest producer’s accuracy and user’s accuracy obtained with this method were 95.18% for rubber plantations. The results of this study show that it is feasible to use PlanetScope data to perform rubber monitoring, thus effectively solving the problem of missing images in the optimal rubber monitoring period; additionally, this method can be extended to other real-life applications

Study on Spatial Distribution Characters of Rubber Yield and Soil Nutrients in Guangba Farm of Hainai Province

International audienceStudying the spatial distribution characters of rubber yield and soil nutrients and the rule of spatial variability are important for suitable fertilization strategy in rubber plantation. This paper selected Hongquan Branch, Guangba Branch and Gongai Branch of Guangba Farm in Hainan province as study area and total of 327 samples were selected in the rubber plantation. The spatial distribution characters of rubber yield and five soil nutrients, including organic matter (OM), total nitrogen (TN), available phosphorus (AP), available potassium (AK), exchangeable calcium (Ga), were studied using traditional analysis method and geo-statistics analysis method. The results showed that: (1) The average value of rubber yield was 3.55 kg/hm2 with moderate spatial variability and the average values of OM, N, P, K and Ga were 11.65 g/kg, 0.07%, 16.23 mg/kg, 49.65 mg/kg and 84.44 mg/kg, respectively. Soil OM, TN, AK and Ga had moderate spatial variability but AP had strong spatial variability. (2) Rubber yield and soil total nitrogen (N) nutrient had strong spatial dependence; soil OM, AP, AK and Ga had moderate spatial dependence. (3) Based on the previous reports of normal range of soil nutrients, soil OM and TN nutrient content were very low in the studied rubber plantation of Guangba Farm. Therefore, more nitrogen fertilizer should be applied in the rubber plantation in future

Exploratory Analysis on the Spatial Distribution and Influencing Factors of Beitang Landscape in the Shangzhuang Basin

Beitang landscape is a production system and land use pattern that ancient people created to adapt to droughts and floods during a long traditional farming culture. It has a critical reference meaning for water resource use and water systems protection in modern cities. Taking the Shangzhuang Basin (China) as an example, this study used multi-source data, such as remote sensing images, Beitang vector dataset, land-use dataset, elevation, slope, river, road, and field survey, to investigate the spatial distribution and influencing factors Beitang landscape. Results showed that in a typical small watershed basin, an area of ponds accounted for 1.0%, about 12 ponds per square kilometer—the average area of ponds is 814 m2, of which the vast majority is less than 1000 m2. The study found that the spatial distribution of Beitang in the Shangzhuang Basin has cluster characteristics, influenced by elevation, slope, aspect, river, roads, villages, farmland, woodland, and other factors, all of which have closely related to the natural environment development and human activities. The upstream, middle, and downstream of three Beitang landscapes were coordinated to support the Beitang landscape system in the small watershed of the basin. Findings provided a model for protecting and utilizing natural water systems in rural areas during the construction of sponge cities

Exploratory Analysis on the Spatial Distribution and Influencing Factors of Beitang Landscape in the Shangzhuang Basin

Beitang landscape is a production system and land use pattern that ancient people created to adapt to droughts and floods during a long traditional farming culture. It has a critical reference meaning for water resource use and water systems protection in modern cities. Taking the Shangzhuang Basin (China) as an example, this study used multi-source data, such as remote sensing images, Beitang vector dataset, land-use dataset, elevation, slope, river, road, and field survey, to investigate the spatial distribution and influencing factors Beitang landscape. Results showed that in a typical small watershed basin, an area of ponds accounted for 1.0%, about 12 ponds per square kilometer—the average area of ponds is 814 m2, of which the vast majority is less than 1000 m2. The study found that the spatial distribution of Beitang in the Shangzhuang Basin has cluster characteristics, influenced by elevation, slope, aspect, river, roads, villages, farmland, woodland, and other factors, all of which have closely related to the natural environment development and human activities. The upstream, middle, and downstream of three Beitang landscapes were coordinated to support the Beitang landscape system in the small watershed of the basin. Findings provided a model for protecting and utilizing natural water systems in rural areas during the construction of sponge cities

A Trade-Off Algorithm for Solving p-Center Problems with a Graph Convolutional Network

The spatial optimization method between combinatorial optimization problems and GIS has many geographical applications. The p-center problem is a classic NP-hard location modeling problem, which has essential applications in many real-world scenarios, such as urban facility locations (ambulances, fire stations, pipelines maintenance centers, police stations, etc.). This study implements two methods to solve this problem: an exact algorithm and an approximate algorithm. Exact algorithms can get the optimal solution to the problem, but they are inefficient and time-consuming. The approximate algorithm can give the sub-optimal solution of the problem in polynomial time, which has high efficiency, but the accuracy of the solution is closely related to the initialization center point. We propose a new paradigm that combines a graph convolution network and greedy algorithm to solve the p-center problem through direct training and realize that the efficiency is faster than the exact algorithm. The accuracy is superior to the heuristic algorithm. We generate a large amount of p-center problems by the Erdos–Renyi graph, which can generate instances in many real problems. Experiments show that our method can compromise between time and accuracy and affect the solution of p-center problems