Identification and Severity Monitoring of Maize Dwarf Mosaic Virus Infection Based on Hyperspectral Measurements

Ganoderma lucidum: Insights on host range, diagnosis, and management strategies

Forest ecosystems play an important role in upholding life on our planet. However, the onslaught of fungal pathogens like Ganoderma lucidum, poses a threat by decimating numerous tree species. G. lucidum identified as a root pathogen, causing root rot in numerous tree species of horticulture and forestry importance. The fungus initiates infection through basidiospores, which germinate and penetrate within roots and start to degrade lignocellulosic components of plant cells. Early-stage detection of G. lucidum, is challenging, while in advance stages, the wood undergoes softening and a loss of tensile strength, rendering the disease incurable. Hence, effective management of G. lucidum necessitates a pivotal role of disease diagnostic techniques, which are currently underutilized or inadequately accessible. Subsequent implementation of suitable control measures becomes imperative to thwart disease occurrence and mitigate its impact in early stages, thus preserving the vitality of forest ecosystems. This study provides comprehensive overview of G. lucidum, covering taxonomy, pathogenicity, disease cycle, diagnosis and effective control measures, which will be helpful in formulating effective diagnostic techniques for early management of root rot disease.

SCANet: Implementation of Selective Context Adaptation Network in Smart Farming Applications

In the last decade, deep learning has enjoyed its spotlight as the game-changing addition to smart farming and precision agriculture. Such development has been predominantly observed in developed countries, while on the other hand, in developing countries most farmers especially ones with smallholder farms have not enjoyed such wide and deep adoption of this new technologies. In this paper we attempt to improve the image classification part of smart farming and precision agriculture. Agricultural commodities tend to possess certain textural details on their surfaces which we attempt to exploit. In this work, we propose a deep learning based approach called Selective Context Adaptation Network (SCANet). SCANet performs feature enhancement strategy by leveraging level-wise information and employing context selection mechanism. In exploiting contextual correlation feature of the crop images our proposed approach demonstrates the effectiveness of the context selection mechanism. Our proposed scheme achieves 88.72% accuracy and outperforms the existing approaches. Our model is evaluated on the cocoa bean dataset constructed from the real cocoa bean industry scene in Indonesia.

Classification of Southern Corn Rust Severity Based on Leaf-Level Hyperspectral Data Collected under Solar Illumination

Maize is one of the most important crops in China, and it is under a serious, ever-increasing threat from southern corn rust (SCR). The identification of wheat rust based on hyperspectral data has been proved effective, but little research on detecting maize rust has been reported. In this study, full-range hyperspectral data (350~2500 nm) were collected under solar illumination, and spectra collected under solar illumination (SCUSI) were separated into several groups according to the disease severity, measuring height and leaf curvature (the smoothness of the leaf surface). Ten indices were selected as candidate indicators for SCR classification, and their sensitivities to the disease severity, measuring height and leaf curvature, were subjected to analysis of variance (ANOVA). The better-performing indices according to the ANOVA test were applied to a random forest classifier, and the classification results were evaluated by using a confusion matrix. The results indicate that the PRI was the optimal index for SCR classification based on the SCUSI, with an overall accuracy of 81.30% for mixed samples. The results lay the foundation for SCR detection in the incubation period and reveal potential for SCR detection based on UAV and satellite imageries, which may provide a rapid, timely and cost-effective detection method for SCR monitoring.