AFD-Net: Apple Foliar Disease Multi Classification using Deep Learning on Plant Pathology Dataset

Plant diseases significantly affect the crop, so their identification is very important. Correct identification of these diseases is crucial for establishing a good disease control strategy to avoid time and financial losses. In general, machines can greatly reduce the possibility of human error. In particular, computer vision techniques developed through deep learning have paved a way to detect and diagnose these plant diseases on the leaf. In this work, the model AFD-Net was developed to detect and identify various leaf diseases in apple trees. The dataset is from Kaggle 2020 and 2021 and was financially supported by the Cornell Initiative for Digital Agriculture. A AFD-Net was proposed for leaf disease classification in apple trees and the results of the efficiency of the model are compared with other state-of-the-art deep learning approaches. The results of the experiments in the validation dataset show that the proposed AFD-Net model achieves the highest values compared to other deep learning models in the original and extended datasets with 98.7% accuracy for Plant Pathology 2020 and 92.6% for Plant Pathology 2021.

Antagonistic screening of Trichoderma spp. isolated from patchouli rhizosphere

The objective of this research was to determine the antagonistic activity of Trichoderma spp. isolated from patchouli rhizosphere (Pogostemon cablin Benth.). Another objective was to perform antagonistic screening of these fungi to inhibit the growth of the wilted pathogen Fusarium spp. In vitro research was conducted in the Laboratory of Plant Pathology, Universitas Syiah Kuala, from January to June 2020. The study used a completely randomised design with five treatments and three replications. The antagonistic screening was carried out by using the dual culture method of Trichoderma spp. against Fusarium spp. with the medium of Potato Dextrose Agar (PDA). The result showed that five isolates of Trichoderma have different antagonistic percentages in inhibiting the Fusarium. The highest antagonistic activity was found from isolate 2 and the lowest value was shown by isolate 3.

Phytopathology teaching teaching during COVID-19: UNA Costa Rica case

<p>The pandemic caused by SARS-CoV-2 has been a challenge when it comes to teaching at a university level, and particularly when teaching plant pathology. As preventive strategies, lockdown and social distancing have been generalized in all countries. In Costa Rica, universities suspended in-person classes beginning in March 2020, and implemented virtual teaching-learning processes for bachelor’s and postgraduate students. However, every course/professor has designed the virtual teaching strategies and models according to the socio-educational realities of the group. The teaching of plant pathology in Agronomy bases its teaching methods in laboratory practices and commercial productive units. This article presents strategies and regulations implemented by the National University of Costa Rica and applied to university teaching and reflects upon the achievements reached and the challenges in the plant pathology education in its virtual form.</p>

Tan spot disease under the lenses of plant pathologists

Necrotrophic plant pathogens pose an important threat to crop production, and many fungi in the Pleosporales have caused the sudden emergence of major epidemics on cereal crops. Tan spot of wheat, caused by Pyrenophora tritici-repentis, is one example, and since its emergence in the 1970s, scientists have explored its virulence and interactions with the host. In this chapter, our aim is to provide a comprehensive review of the most significant landmarks in tan spot research over the past 50 years from a plant pathology perspective.

Global patterns of cereal diseases and the impacts of breeding for host plant resistance

Plant breeding for host plant resistance to pathogens brings together different disciplinary domains, especially genetics and plant pathology. The strategies underpinning plant breeding have therefore, logically, been influenced by a number of paradigms that have dominated the field of disease management. This chapter provides a brief overview of these paradigms, where the authors link the implications for plant breeding. In doing so, the authors try to follow an approximate (1) linear and (2) chronological order. Yet the authors are aware that, depending on the considered crop and region, (1) there might have been occasional jumps and step-backs leading to non-linear paths, and (2) the chronology of events may have much varied.

Bibliometric analysis of strawberry (Fragaria × ananassa Duch.) research from Plant Sciences category based on Web of Science

Strawberry (Fragaria × ananassa Duch.) is one of the most significant horticultural cash crops in the world. The study aimed to identify and analyse the 2,930 articles and review type papers of strawberry research from the Plant Sciences category based on the Web of Science. Papers mainly written in English (2,894, 98.771%), were from a total of 8,838 authors, 96 countries/territories, 1,845 organisations and published in 197 journals and book series. The top seven core journals are ranked as Plant Disease (252, 8.601%), Phytopathology (229, 7.816%), Plant Pathology (93, 3.174%), Frontiers in Plant Science (89, 3.308%), Canadian Journal of Plant Science (86, 2.935%), European Journal of Plant Pathology (86, 2.935%) and Journal of Experimental Botany (86, 2.935%), and these journals each published >86 papers. The top five countries and regions were the USA, People's Republic of China, Spain, Canada and England. The top five organisations were the University of Florida, USDA ARS, University of Malaga, University of California Davis, Agriculture and Agri-Food Canada. The top five authors are Peres, Natalia A. (Peres, Natalia; Peres, N.A.); Madden, LV; Munoz-Blanco, Juan (Munoz-Blanco, J); Schwab, Wilfried (Schwab, W) and Ellis, MA, each published >25 papers. All keywords of the strawberry research from the Plant Sciences category were separated into 11 clusters for different research topics. Visualisations offer exploratory information on the current state in a scientific field or discipline as well as indicate possible developments in the future. The review could provide a valuable guide for designing future studies.

Reproducibility in plant pathology: where do we stand and a way forward.

Open research practices have been highlighted extensively during the last ten years in many fields of scientific study as essential standards needed to promote transparency and reproducibility of scientific results. Scientific claims can only be evaluated based on how protocols, materials, equipment and methods were described; data were collected and prepared; and, analyses were conducted. Openly sharing protocols, data and computational code is central for current scholarly dissemination and communication, but in many fields, including plant pathology, adoption of these practices has been slow. We randomly selected 300 articles published from 2012 to 2018 across 21 journals representative of the plant pathology discipline and assigned them scores reflecting their openness and reproducibility. We found that most of the articles were not following protocols for open science, and were failing to share data or code in a reproducible way. We also propose that use of open-source tools facilitates reproducible work and analyses benefitting not just readers, but the authors as well. Finally, we also provide ideas and tools to promote open, reproducible research practices among plant pathologists.

Evaluation of Yield Loss Assessment Caused due to Curvularia Leaf Spot (Curvularia lunata) in Maize

An experiment was conducted during Kahrif 2017 and Kharif 2018 at department of plant pathology, Rajasthan collage of Agriculture, Udaipur, Raj. to find out the average yield loss assessment caused due to Curvularia leaf spot of maize. During Kharif 2017 estimated average yield loss was 21.69% with 18.56 % PDI and 1032.33 kg/plot yield in protected plot similarly in Kharif 2018 yield loss was 22.49% and 992.73 kg /plot yield with 18.07% PDI. On average yield loss caused due to CLS in maize in both years was estimated 22.06%.