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Silver nanoparticles have gained considerable interest in recent decades due to their antimicrobial activity and are used in water disinfection, wound healing, food packaging, and plant protection. This study tested the potential of silver nanoparticles synthesized using the neem (Azadirachta indica) leaf extract against Alternaria solani causes early blight disease in tomato plants. The pathogen was isolated from infected tomato plants and identified using morphological and molecular features. The results showed significant variation among isolates. Isolates, Shk-1 and Ksr-1 were highly pathogenic, causing up to 80% disease incidence. The potential of silver nanoparticles against each isolate was determined using different concentrations of silver nanoparticles. During in vitro and in vivo experiments, the growth inhibition rate of the pathogen was 70–100% at 50 ppm. Lower concentrations of silver nanoparticles (5 and 10 ppm) increased phenolics, PO, PPO, and PAL production by more than 50% as compared to the untreated control. These defensive mechanisms clearly demonstrate the fungicidal potential of AgNPs and recommend their utilization in different crop protection programs. [ABSTRACT FROM AUTHOR]

This study investigated both spectrum and texture features for detecting early blight disease on eggplant leaves. Hyperspectral images for healthy and diseased samples were acquired covering the wavelengths from 380 to 1023 nm. Four gray images were identified according to the effective wavelengths (408, 535, 624 and 703 nm). Hyperspectral images were then converted into RGB, HSV and HLS images. Finally, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) were extracted from gray images, RGB, HSV and HLS images, respectively. The dependent variables for healthy and diseased samples were set as 0 and 1. K-Nearest Neighbor (KNN) and AdaBoost classification models were established for detecting healthy and infected samples. All models obtained good results with the classification rates (CRs) over 88.46% in the testing sets. The results demonstrated that spectrum and texture features were effective for early blight disease detection on eggplant leaves. [ABSTRACT FROM AUTHOR]

Tomato (Solanum lycopersicum L.) is among the most important vegetable crops worldwide. Early blight disease, caused by Alternaria solani, is a destructive foliar disease of tomato and other Solanaceae species. Herein, we investigated the in vitro antifungal properties of gallic acid and two of its derivatives (syringic and pyrogallic acids) against A. solani during 2019 and 2020 seasons. The physiological and biochemical effects of these compounds on infected tomato plants were also investigated using the whole plant bioassay. The in vitro investigation showed that all tested compounds showed fungistatic action and inhibited the mycelial radial growth of A. solani in a dose-dependent manner. In two separate pot-experiments, those compounds efficiently suppressed the development of the disease symptoms and area under disease progress curve (AUDPC), without any phytotoxic effects on the treated tomato plants. Additionally, all tested compounds positively enhanced the biochemical traits of treated plants including the chlorophyll content, the total soluble phenolics, the total soluble flavonoids, and the enzymatic activities of catalase, peroxidase, and polyphenol oxidase during 2019 and 2020 seasons. Moreover, the treatment with gallic acid and its derivatives significantly increased all yield components of A. solani-infected tomato plants such as the total number of flowers and fruits, and the fruit yield for each tomato plant in both experiments. Considering the fungitoxicity of phenolic acids against A. solani with no phytotoxicity on treated tomato plants, we believe that gallic acid and its derivatives might be a sustainable eco-friendly control strategy to reduce the usage of chemical fungicides partially or entirely against A. solani particularly, and fungal diseases in general. [ABSTRACT FROM AUTHOR]

This study investigated both spectrum and texture features for detecting early blight disease on eggplant leaves. Hyperspectral images for healthy and diseased samples were acquired covering the wavelengths from 380 to 1023 nm. Four gray images were identified according to the effective wavelengths (408, 535, 624 and 703 nm). Hyperspectral images were then converted into RGB, HSV and HLS images. Finally, eight texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation) based on gray level co-occurrence matrix (GLCM) were extracted from gray images, RGB, HSV and HLS images, respectively. The dependent variables for healthy and diseased samples were set as 0 and 1. K-Nearest Neighbor (KNN) and AdaBoost classification models were established for detecting healthy and infected samples. All models obtained good results with the classification rates (CRs) over 88.46% in the testing sets. The results demonstrated that spectrum and texture features were effective for early blight disease detection on eggplant leaves.