Coffee wilt disease

In the early 20th century, coffee wilt disease (CWD) caused by the vascular wilt pathogen, Fusarium xylarioides, spread across Africa destroying coffee trees, reducing yields and significantly impacting producer livelihoods. Through systematic sanitation and establishment of breeding programmes in affected countries, CWD appeared to decline. However, the disease re-emerged and increased to epidemic proportions in the 1990s affecting robusta coffee in DRC, Uganda and Tanzania and arabica coffee in Ethiopia. In 1999, 14.5 million robusta coffee trees were estimated to have been destroyed in Uganda alone. This chapter discusses the history, impact, symptoms, cause and spread of CWD. A summary of the Regional Coffee Wilt Programme (RCWP) which examined many aspects of the disease and its management is also provided. . Future research trends include host specificity, underlying resistance mechanisms and the role of alternative hosts. Investigation of pathogen ecology is needed to allow greater focus on agroecological management practices.

Study on the Role of Phytohormones in Resistance to Watermelon Fusarium Wilt

Fusarium wilt disease is one of the major diseases causing a decline in watermelon yield and quality. Researches have informed that phytohormones play essential roles in regulating plants growth, development, and stress defendants. However, the molecular mechanism of salicylic acid (SA), jasmonic acid (JA), and abscisic acid (ABA) in resistance to watermelon Fusarium wilt remains unknown. In this experiment, we established the SA, JA, and ABA determination system in watermelon roots, and analyzed their roles in against watermelon Fusarium wilt compared to the resistant and susceptible varieties using transcriptome sequencing and RT-qPCR. Our results revealed that the up-regulated expression of Cla97C09G174770, Cla97C05G089520, Cla97C05G081210, Cla97C04G071000, and Cla97C10G198890 genes in resistant variety were key factors against (Fusarium oxysporum f. sp. Niveum) FON infection at 7 dpi. Additionally, there might be crosstalk between SA, JA, and ABA, caused by those differentially expressed (non-pathogen-related) NPRs, (Jasmonate-resistant) JAR, and (Pyrabactin resistance 1-like) PYLs genes, to trigger the plant immune system against FON infection. Overall, our results provide a theoretical basis for watermelon resistance breeding, in which phytohormones participate.

Systemic Acquired Resistance-Mediated Control of Pine Wilt Disease by Foliar Application With Methyl Salicylate

Pine wilt disease (PWD), caused by the pinewood nematode, is the most destructive disease in pine forest ecosystems worldwide. Extensive research has been done on PWD, but effective disease management is yet to be devised. Generally, plants can resist pathogen attack via a combination of constitutive and inducible defenses. Systemic acquired resistance (SAR) is an inducible defense that occurs by the localized infection of pathogens or treatment with elicitors. To manage PWD by SAR in pine trees, we tested previously known 12 SAR elicitors. Among them, methyl salicylate (MeSA) was found to induce resistance against PWD in Pinus densiflora seedlings. In addition, the foliar applications of the dispersible concentrate-type formulation of MeSA (MeSA 20 DC) and the emulsifiable concentrate-type formulation of MeSA (MeSA 20 EC) resulted in significantly reduced PWD in pine seedlings. In the field test using 10-year-old P. densiflora trees, MeSA 20 DC showed a 60% decrease in the development of PWD. Also, MeSA 20 EC gave the best results when applied at 0.1 mM concentration 2 and 1 weeks before pinewood nematode (PWN) inoculation in pine seedlings. qRT-PCR analysis confirmed that MeSA induced the expression of defense-related genes, indicating that MeSA can inhibit and delay the migration and reproduction of PWN in pine seedlings by modulating gene expression. These results suggest that foliar application of MeSA could reduce PWD incidence by inducing resistance and provide an economically feasible alternative to trunk-injection agents for PWD management.

Surveillance of pine wilt disease by high resolution satellite

Pine wilt disease caused by the pinewood nematode Bursaphelenchus xylophilus has led to the death of a large number of pine trees in China. This destructive disease has the characteristics of bring wide-spread, fast onset, and long incubation time. Most importantly, in China, the fatality rate in pines is as high as 100%. The key to reducing this mortality is how to quickly find the infected trees. We proposed a method of automatically identifying infected trees by a convolution neural network and bounding box tool. This method rapidly locates the infected area by classifying and recognizing remote sensing images obtained by high resolution earth observation Satellite. The recognition accuracy of the test data set was 99.4%, and the remote sensing image combined with convolution neural network algorithm can identify and determine the distribution of the infected trees. It can provide strong technical support for the prevention and control of pine wilt disease.

A Deep Learning-Based Generalized System for Detecting Pine Wilt Disease Using RGB-Based UAV Images

Pine wilt is a devastating disease that typically kills affected pine trees within a few months. In this paper, we confront the problem of detecting pine wilt disease. In the image samples that have been used for pine wilt disease detection, there is high ambiguity due to poor image resolution and the presence of “disease-like” objects. We therefore created a new dataset using large-sized orthophotographs collected from 32 cities, 167 regions, and 6121 pine wilt disease hotspots in South Korea. In our system, pine wilt disease was detected in two stages: n the first stage, the disease and hard negative samples were collected using a convolutional neural network. Because the diseased areas varied in size and color, and as the disease manifests differently from the early stage to the late stage, hard negative samples were further categorized into six different classes to simplify the complexity of the dataset. Then, in the second stage, we used an object detection model to localize the disease and “disease-like” hard negative samples. We used several image augmentation methods to boost system performance and avoid overfitting. The test process was divided into two phases: a patch-based test and a real-world test. During the patch-based test, we used the test-time augmentation method to obtain the average prediction of our system across multiple augmented samples of data, and the prediction results showed a mean average precision of 89.44% in five-fold cross validation, thus representing an increase of around 5% over the alternative system. In the real-world test, we collected 10 orthophotographs in various resolutions and areas, and our system successfully detected 711 out of 730 potential disease spots.