Recognition of Areca Leaf Yellow Disease Based on PlanetScope Satellite Imagery

Areca yellow leaf disease is a major attacker of the planting and production of arecanut. The continuous expansion of arecanut (Areca catechu L.) planting areas in Hainan has placed a great need to strengthen the monitoring of this disease. At present, there is little research on the monitoring of areca yellow leaf disease. PlanetScope imagery can achieve daily global coverage at a high spatial resolution (3 m) and is thus suitable for the high-precision monitoring of plant pest and disease. In this paper, PlanetScope images were employed to extract spectral features commonly used in disease, pest and vegetation growth monitoring for primary models. In this paper, 13 spectral features commonly used in vegetation growth and pest monitoring were selected to form the initial feature space, followed by the implementation of the Correlation Analysis (CA) and independent t-testing to optimize the feature space. Then, the Random Forest (RF), Backward Propagation Neural Network (BPNN) and AdaBoost algorithms based on feature space optimization to construct double-classification (healthy, diseased) monitoring models for the areca yellow leaf disease. The results indicated that the green, blue and red bands, and plant senescence reflectance index (PSRI) and enhanced vegetation index (EVI) exhibited highly significant differences and strong correlations with healthy and diseased samples. The RF model exhibits the highest overall recognition accuracy for areca yellow leaf disease (88.24%), 2.95% and 20.59% higher than the BPNN and AdaBoost models, respectively. The commission and omission errors were lowest with the RF model for both healthy and diseased samples. This model also exhibited the highest Kappa coefficient at 0.765. Our results exhibit the feasible application of PlanetScope imagery for the regional large-scale monitoring of areca yellow leaf disease, with the RF method identified as the most suitable for this task. Our study provides a reference for the monitoring, a rapid assessment of the area affected and the management planning of the disease in the agricultural and forestry industries.

Remote Sensing Detecting of Yellow Leaf Disease of Arecanut Based on UAV Multisource Sensors

Unmanned aerial vehicle (UAV) remote sensing technology can be used for fast and efficient monitoring of plant diseases and pests, but these techniques are qualitative expressions of plant diseases. However, the yellow leaf disease of arecanut in Hainan Province is similar to a plague, with an incidence rate of up to 90% in severely affected areas, and a qualitative expression is not conducive to the assessment of its severity and yield. Additionally, there exists a clear correlation between the damage caused by plant diseases and pests and the change in the living vegetation volume (LVV). However, the correlation between the severity of the yellow leaf disease of arecanut and LVV must be demonstrated through research. Therefore, this study aims to apply the multispectral data obtained by the UAV along with the high-resolution UAV remote sensing images to obtain five vegetation indexes such as the normalized difference vegetation index (NDVI), optimized soil adjusted vegetation index (OSAVI), leaf chlorophyll index (LCI), green normalized difference vegetation index (GNDVI), and normalized difference red edge (NDRE) index, and establish five algorithm models such as the back-propagation neural network (BPNN), decision tree, naïve Bayes, support vector machine (SVM), and k-nearest-neighbor classification to determine the severity of the yellow leaf disease of arecanut, which is expressed by the proportion of the yellowing area of a single areca crown (in percentage). The traditional qualitative expression of this disease is transformed into the quantitative expression of the yellow leaf disease of arecanut per plant. The results demonstrate that the classification accuracy of the test set of the BPNN algorithm and SVM algorithm is the highest, at 86.57% and 86.30%, respectively. Additionally, the UAV structure from motion technology is used to measure the LVV of a single areca tree and establish a model of the correlation between the LVV and the severity of the yellow leaf disease of arecanut. The results show that the relative root mean square error is between 34.763% and 39.324%. This study presents the novel quantitative expression of the severity of the yellow leaf disease of arecanut, along with the correlation between the LVV of areca and the severity of the yellow leaf disease of arecanut. Significant development is expected in the degree of integration of multispectral software and hardware, observation accuracy, and ease of use of UAVs owing to the rapid progress of spectral sensing technology and the image processing and analysis algorithms.

Survey of Sugarcane Yellow Leaf Virus in the Canal Point Breeding and Cultivar Development Program

Sugarcane yellow leaf virus (SCYLV), a Polerovirus in the family Luteoviridea, causes yellow leaf disease (YLD). Yield losses from YLD have been reported from several countries in both symptomatic and asymptomatic sugarcane cultivars. The breeding nursery at Canal Point (CP) in 2016 and primary and secondary seed increases in the CP cultivar development program at grower’s farm from 2015 to 2019 were surveyed for SCYLV infection by the tissue-blot immunoassay using polyclonal antibodies raised against SCYLV. More than 32% of varieties in the CP breeding nursery were infected with SCYLV in 2016. The SCYLV data of primary and secondary seedcane increases from 2015 to 2019 showed that out of 54 varieties screened at different locations, 12 had no SCYLV-positive plants, 24 had less than 5%, 5 had 6% to 12%, and 13 had 20% to 75% of the plants infected with SCYLV. The SCYLV screenings in varieties in the primary and secondary seed increase plantings provide growers an opportunity to acquire virus-free clean seedcane by apical meristem propagation to minimize the spread of the SCYLV and avoid yield losses.

Transmission of areca palm velarivirus 1 (APV1) by mealybugs causes yellow leaf disease (YLD) in betel palm (Areca catechu)

Yellow leaf disease (YLD) is the most destructive disease of betel palm (Areca catechu). A strong association between YLD and areca palm velarivirus 1 (APV1) has been observed. However, the causal relationship between APV1 and disease, and the transmission mode, require further investigation. This work showed that APV1 was transmitted by both Ferrisia virgata and Pseudococcus cryptus mealybugs, and caused YLD symptoms in betel palm seedlings; therefore, we demonstrate that APV1 is a causal agent of YLD. APV1 was detected in the stylets, foreguts, midguts, and hindguts of the vectors via both immunocapture RT-PCR and immunofluorescence assays. APV1 was not transmitted transovarially from viruliferous female F. virgata to their progeny. In summary, the transmission of APV1 by F. virgata may occur in a non-circulative, semi-persistent manner. This study fills important gaps in our knowledge of velarivirus transmission, which is critical for developing YLD management practices.

Develop an efficient inoculation technique for Fusarium solani isolate “TJP-2178-10” pathogeny assessment in Phalaenopsis orchids

Background Phalaenopsis is one of the important ornamental plants worldwide. It plays the most significant role in flower exportation in Taiwan. However, the yellow leaf disease caused by Fusarium spp. has reduced the orchid flower yield 10–50 % yearly. Varieties resistant to yellow leaf disease associated with Fusarium is urgently needed for orchid growers and breeders, and is the ultimate solution for the long-term goal. To achieve this, phenotyping is the first step and the most necessary information for further studies, such as resistance gene identification, quantitative trait loci identification, and genome-wide association study. Results The inoculation of Fusarium was performed in either abbreviated stem or detached leaf, and the pros and cons were compared. The former is the general method of phenotyping for estimating the tolerance to yellow leaf disease of Phalaenopsis, but it is time-consuming and spacy, and thus not suitable for the assessment of large numbers of samples. In contrast, the latter not only showed a similar trend of disease severity with time reduced to only one fourth of the former one but also less space needed. Conclusions This solution allows a better phenotyping approach for the fast detection of yellow leaf disease associated with Fusarium in a large number of Phalaenopsis samples.

Comparative genomics reveals insights into genetic variability and molecular evolution among sugarcane yellow leaf virus populations

Yellow leaf disease caused by sugarcane yellow leaf virus (SCYLV) is one of the most prevalent diseases worldwide. In this study, six near-complete genome sequences of SCYLV were determined to be 5775–5881 bp in length. Phylogenetic analysis revealed that the two SCYLV isolates from Réunion Island, France, and four from China were clustered into REU and CUB genotypes, respectively, based on 50 genomic sequences (this study = 6, GenBank = 44). Meanwhile, all 50 isolates were clustered into three phylogroups (G1–G3). Twelve significant recombinant events occurred in intra- and inter-phylogroups between geographical origins and host crops. Most recombinant hotspots were distributed in coat protein read-through protein (RTD), followed by ORF0 (P0) and ORF1 (P1). High genetic divergences of 12.4% for genomic sequences and 6.0–24.9% for individual genes were determined at nucleotide levels. The highest nucleotide diversity (π) was found in P0, followed by P1 and RdRP. In addition, purifying selection was a main factor restricting variability in SCYLV populations. Infrequent gene flow between Africa and the two subpopulations (Asia and America) were found, whereas frequent gene flow between Asia and America subpopulations was observed. Taken together, our findings facilitate understanding of genetic diversity and evolutionary dynamics of SCYLV.

Nematicidal activity of cinnamon bark extracts and chitosan against Meloidogyne incognita and Pratylenchus coffeae

Summary In this study, we aimed to evaluate the nematicidal activity of cinnamon bark extracts (CE) and chitosan (Cs) against Meloidogyne incognita and Pratylenchus coffeae under pot and field conditions. In the pot experiments, CE mixed with Cs effectively inhibited M. incognita and P. coffeae infection on robusta coffee plants. The formulations applied, CE:Cs = 8 mg:30 mg, CE:Cs = 16 mg:60 mg and CE:Cs = 16 mg:60 mg per pot, significantly reduced the gall index and nematode number in 5 g of root and 100 g of soil. In addition, the application of CE:Cs = 48 mg:180 mg CE:Cs = 80 mg:300 mg and CE:Cs = 112 mg:420 mg plant−1 effectively reduced root gall formation and nematode density in roots and soil compared with the non-treated control under field conditions. Nematode density in the roots was positively correlated with the rate of yellow leaf disease. These results suggest that cinnamon mixed with chitosan may be used as an effective eco-friendly pesticide against plant-parasitic nematodes.