Novel CropdocNet Model for Automated Potato Late Blight Disease Detection from Unmanned Aerial Vehicle-Based Hyperspectral Imagery

The accurate and automated diagnosis of potato late blight disease, one of the most destructive potato diseases, is critical for precision agricultural control and management. Recent advances in remote sensing and deep learning offer the opportunity to address this challenge. This study proposes a novel end-to-end deep learning model (CropdocNet) for accurate and automated late blight disease diagnosis from UAV-based hyperspectral imagery. The proposed method considers the potential disease-specific reflectance radiation variance caused by the canopy’s structural diversity and introduces multiple capsule layers to model the part-to-whole relationship between spectral–spatial features and the target classes to represent the rotation invariance of the target classes in the feature space. We evaluate the proposed method with real UAV-based HSI data under controlled and natural field conditions. The effectiveness of the hierarchical features is quantitatively assessed and compared with the existing representative machine learning/deep learning methods on both testing and independent datasets. The experimental results show that the proposed model significantly improves accuracy when considering the hierarchical structure of spectral–spatial features, with average accuracies of 98.09% for the testing dataset and 95.75% for the independent dataset, respectively.

In Tuber Biocontrol of Potato Late Blight by a Collection of Phenazine-1-Carboxylic Acid-Producing Pseudomonas spp.

Phenazine-1-carboxylic acid (PCA) produced by plant-beneficial Pseudomonas spp. is an antibiotic with antagonistic activities against Phytophthora infestans, the causal agent of potato late blight. In this study, a collection of 23 different PCA-producing Pseudomonas spp. was confronted with P. infestans in potato tuber bioassays to further understand the interaction existing between biocontrol activity and PCA production. Overall, the 23 strains exhibited different levels of biocontrol activity. In general, P. orientalis and P. yamanorum strains showed strong disease reduction, while P. synxantha strains could not effectively inhibit the pathogen’s growth. No correlation was found between the quantities of PCA produced and biocontrol activity, suggesting that PCA cannot alone explain P. infestans’ growth inhibition by phenazine-producing pseudomonads. Other genetic determinants potentially involved in the biocontrol of P. infestans were identified through genome mining in strains displaying strong biocontrol activity, including siderophores, cyclic lipopeptides and non-ribosomal peptide synthase and polyketide synthase hybrid clusters. This study represents a step forward towards better understanding the biocontrol mechanisms of phenazine-producing Pseudomonas spp. against potato late blight.

Control Efficacy of Bacillus velezensis AFB2-2 against Potato Late Blight Caused by Phytophthora infestans in Organic Potato Cultivation

Although late blight is an important disease in eco-friendly potato cultivation in Korea, it is highly dependent on the use of eco-friendly agricultural materials and the development of biological control technology is low. It is a necessary to develop an effective biocontrol agent to inactivate late blight in the field. AFB2-2 strain is a gram-positive with peritrichous flagella. It can utilize 20 types of carbon sources, like L-arabinose, and D-trehalose at 35°C. The optimal growth temperature of the strain is 37°C. It can survive at 20-50°C in tryptic soy broth. The maximum salt concentration tolerated by AFB2-2 strain is 7.5% NaCl. AFB2-2 strain inhibited the mycelial growth of seven plant pathogens by an average inhibitory zone of 10.2 mm or more. Among the concentrations of AFB2-2, 107 cfu/ml showed the highest control value of 85.7% in the greenhouse. Among the three concentrations of AFB2-2, the disease incidence and severity of potato late blight at 107 cfu/ml was lowest at 0.07 and 6.7, respectively. The nucleotide sequences of AFB2-2 strain were searched in the NCBI GenBank; Bacillus siamensis strain KCTC 13613, Bacillus velezensis strain CR-502, and Bacillus amyloliquefaciens strain DSM7 were found to have a genetic similarity of 99.7%, 99.7%, and 99.5%, respectively. The AFB2-2 strain was found to harbor the biosynthetic genes for bacillomycin D, iturin, and surfactin. Obtained data recommended that the B. velezensis AFB2-2 strain could be considered as a promising biocontrol agent for P. infestans in the field.

Potato late blight disease prediction using meteorological parameters in Northern Himalayas of India

Weather parameters play an important role in the spread of potato late blight of caused by Phytophthora infestans (Mont.) de Bary has historically been serious disease of potatoes through worldwide, including India. Due to spatial variation in prevailing weather conditions, its severity varies from region to region. Disease development process and the weather parameters are well understood and have been utilized for disease developing forecasting models and decision support system. Therefore, an experiment was conducted for two consecutive cropping seasons (2017 & 2018) to develop a forecasting model against late blight of potato using stepwise regression analysis for Northern Himalayas in India. Maximum and minimum temperature, relative humidity, rainfall and wind speed appeared to be most significant factors in the potato late blight disease development. The meteorological conditions conducive for the development of potato late blight disease were characterized. Maximum and minimum temperatures in the range of 15.0 – 28.0°C and 2.0 – 12.0°C were found favorable for potato blight disease. Similarly, relative humidity, rainfall and wind speed in the range of 85 - 95 per cent, 15.5 - 20.75 mm and 1.0 - 5.5 Km h-1, respectively, were conducive for potato late blight disease which are helpful in disease development.