Assessing the effectiveness of oxathiapiprolin towards Phytophthora agathidicida, the causal agent of kauri dieback disease

Phytophthora species cause disease and devastation of plants in ecological and horticultural settings worldwide. A recently identified species, P. agathidicida, infects and ultimately kills the treasured kauri trees (Agathis australis) that are endemic to New Zealand. Currently there are few options for managing kauri dieback disease. In this study, we sought to assess the toxicity of the oomycide oxathiapiprolin against several life cycle stages of two geographically distinct P. agathidicida isolates. The effective concentration to inhibit 50% of mycelial growth (EC50) was determined to be approximately 0.1 ng/ml, indicating that P. agathidicida mycelia are more sensitive to oxathiapiprolin than those from most other Phytophthora species that have been studied. Oxathiapiprolin was also highly effective at inhibiting the germination of zoospores (EC50 = 2–9 ng/ml for the two isolates) and oospores (complete inhibition at 100 ng/ml). In addition, oxathiapiprolin delayed the onset of detached kauri leaf infection in a dose-dependent manner. Collectively, the results presented here highlight the significant potential of oxathiapiprolin as a tool to aid in the control of kauri dieback disease.

Detection and Classification of Plant Diseases by Image Processing

The point of this undertaking is to configuration, carry out and assess a picture handling programming based answer for programmed recognition and grouping of plant leaf infection. Anyway contemplates show that depending on unadulterated unaided eye perception of specialists to recognize and group infections can be tedious and costly, particularly in country regions and agricultural nations. So we present quick, programmed, modest and precise picture preparing based arrangement. Arrangement is made out of four primary stages; in the main stage we make a shading change structure for the RGB leaf picture and afterward, we apply shading space change for the shading change structure. Then, in the subsequent stage, the pictures are sectioned utilizing the K-implies bunching strategy. In the third stage, we figure the surface components for the portioned contaminated items. At long last, in the fourth stage the separated provisions are gone through a pre-prepared neural organization.

A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework

The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms’ edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time.

Assessing the effectiveness of oxathiapiprolin towards Phytophthora agathidicida, the causal agent of kauri dieback disease

Phytophthora species cause disease and devastation of plants in ecological and horticultural settings worldwide. A recently identified species, P. agathidicida, infects and ultimately kills the treasured kauri trees that are endemic to New Zealand. Currently there are few options for controlling or treating P. agathidicida. In this study, we sought to assess the toxicity of the oomycide oxathiapiprolin against several lifecycle stages of two geographically distinct P. agathidicida isolates. Half maximal effective concentration (EC50) values were determined to be approximately 0.1 ng/ml for inhibiting mycelial growth, indicating that P. agathidicida mycelia are more sensitive to oxathiapiprolin than those from most other Phytophthora species that have been studied. Oxathiapiprolin was also highly effective at inhibiting the germination of zoospores (EC50 = 2-9 ng/ml for the two isolates) and oospores (complete inhibition at 100 ng/ml). In addition, oxathiapiprolin delayed the onset of detached kauri leaf infection in a dose-dependent manner. Collectively, the results presented here highlight the significant potential of oxathiapiprolin as a tool to aid in the control of kauri dieback disease.

Mutation induction to improve quinoa (Chenopodium quinoa) resistance to downy mildew (Peronospora variabilis).

Quinoa is an important crop due to its nutritional characteristics (better than cereals) and its tolerance to abiotic stresses. However, various factors such as high susceptibility to diseases, especially downy mildew caused by Peronospora variabilis, limit its agricultural performance. Genetic improvement of quinoa could reduce the need to use fungicides for this crop and maintain the organic quality of Peruvian production in small-scale farms. Seeds of var. 'Amarilla Marangani', irradiated with 150 and 250 Gy of gamma-rays (60Co), were evaluated in two experimental locations in Peru: coastland at La Molina and highland at Huancayo. Resistance to downy mildew and other agricultural traits in the M3 and M4 generations was studied. In both locations, downy mildew was observed in susceptible plants under natural infection, from the seedling stage to plant maturity. At the coastland site, six mutants with 30% leaf infection were obtained in the progeny of plants exposed to 150 Gy. Five additional mutants with 40% leaf infection were found in the progeny of plants exposed to 250 Gy. In the highland trial, only seven lines were identified with 30% severity (foliar area with symptoms) among the plants from the 150 Gy experiment. The parent materials showed 70-80% disease severity. Mutant lines with quantitative resistance and tolerance to downy mildew, high yield potential, reduced duration, shorter plant height, altered inflorescence shape and grain colour mutations were selected from both doses. This study showed that quantitative resistance and tolerance to downy mildew could be obtained in quinoa and this resulted in increased grain yields.

Detection of strains of endophytic bacteria of the genus Bacillus with the most pronounced growth-stimulating and protective properties

Treatment of wheat with endophytic bacteria increases plant growth and reduces the area of leaf infection, but the degree of manifestation of these properties depends on the bacterial strain.

Effects of seed infection and hydration on the buildup of common bacterial blight and its impact on the yield of dry beans

Common bacterial blight (CBB), caused by Xanthomonas axonopodis pv. phaseoli (Xap), is a serious foliar disease of dry bean (Phaseolus vulgaris). A four-year field study examined the effects of different sources of infection and seed hydration on CBB development, yield components and yield in seven resistant or susceptible dry bean lines and cultivars. The five agronomic treatments examined included clean seed, diseased seed, hydrated diseased seed, clean seed with a Xap spray and diseased seed with a Xap spray. Disease development, the yield components and yield were strongly influenced by weather conditions. In comparison with the diseased-seed treatment, the use of clean (disease-free) seed reduced the incidence of CBB leaf infection in the susceptible dry bean cultivars, but no similar benefit was observed in the resistant lines and cultivars. During the three dry growing seasons, the seed hydration treatment increased the incidence of CBB leaf infection compared with the diseased-seed treatment for the susceptible cultivars, but not for the resistant lines and cultivars. In the wet growing season, no significant difference in the incidence of leaf infection was observed between the hydrated seed and diseased-seed treatments in any of the cultivars, possibly because the wet soil conditions promoted pathogen development within the bean plants that year. Seed hydration did not improve seed yield in the dry years, but sometimes decreased it under wet conditions. Therefore, seed hydration cannot be recommended for use in the production of dry beans.