As a major food crop, rice (Oryza sativa) is produced and consumed by nearly 90% of the population in Asia with less than 9% produced outside Asia. Hence, reports on large scale grain losses were alarming and resulted in a heightened awareness on the importance of rice plants’ health and increased interest against phytopathogens in rice. To serve this interest, this review will provide a summary on bacterial rice pathogens, which can potentially be controlled by plant growth-promoting bacteria (PGPB). Additionally, this review highlights PGPB-mediated functional traits, including biocontrol of bacterial rice pathogens and enhancement of rice plant’s growth. Currently, a plethora of recent studies address the use of PGPB to combat bacterial rice pathogens in an attempt to replace existing methods of chemical fertilizers and pesticides that often lead to environmental pollutions. As a tool to combat bacterial rice pathogens, PGPB presented itself as a promising alternative in improving rice plants’ health and simultaneously controlling bacterial rice pathogens in vitro and in the field/greenhouse studies. PGPB, such as Bacillus, Pseudomonas, Enterobacter, Streptomyces, are now very well-known. Applications of PGPB as bioformulations are found to be effective in improving rice productivity and provide an eco-friendly alternative to agroecosystems.

Plants play a vital role in providing food on a global scale. Several environmental factors contribute to the occurrence of plant leaf diseases, leading to substantial reductions in crop yields. Nevertheless, the process of manually detecting plant leaf diseases is both time-consuming and prone to errors. Adopting deep learning technologies can address these challenges, and the efficacy of deep learning techniques in precision agriculture has been explored over the past decades. However, despite these applications, several gaps in plant leaf disease research still need to be addressed for efficient disease control. This paper, therefore, provides an in-depth review of the trends in using convolutional neural networks for leaf disease detection and classification. In addition, we also present the existing plant leaf disease datasets. It was found that convolutional neural network models, such as VGG, EfficientNet, GoogleNet, and ResNet, provide the highest accuracy in classifying plant leaf disease images. This review will provide valuable information for scholars who are seeking effective deep learning-based classifiers for plant leaf disease detection and classification.

Aims This study sought to investigate the effect of bioformulation on the biocontrol efficacy, microbial viability and storage stability of a consortium of Pseudomonas aeruginosa DRB1 and Trichoderma harzianum CBF2 against Foc Tropical Race 4 (Foc‐TR4). Materials and Results Four bioformulations consisting of dry (pesta granules, talc powder and alginate beads) and liquid formulations were evaluated for their ability to control Foc‐TR4, sustain microbial populations after application and maintain microbial stability during storage. All tested bioformulations reduced disease severity (DS) by more than 43·00% with pesta granules producing the highest reduction in DS by 66·67% and the lowest area under the disease progress curve value (468·75) in a glasshouse trial. Microbial populations of DRB1 and CBF2 were abundant in the rhizosphere, rhizoplane and within the roots of bananas after pesta granules application as compared to talc powder, alginate beads and liquid formulations 84 days after inoculation (DAI). The stability of both microbial populations after 180 days of storage at 4°C was the greatest in the pesta granule formulation. Conclusion The pesta granule formulation was a suitable carrier of biological control agents (BCA) without compromising biocontrol efficacy, microbial population and storage stability as compared to other bioformulations used in this study. Significance and Impact of the Study Pesta granules could be utilized to formulate BCA consortia into biofertilizers. This formulation could be further investigated for possible applications under agricultural field settings.

Pantoea species (Pantoea spp.) is a diverse group of Gram-negative bacteria in the Enterobacteriaceae family that leads to devastating diseases in rice plants, thus affecting significant economic losses of rice production worldwide. Most critical rice diseases such as grain discoloration, bacterial leaf blight, stem necrosis and inhibition of seed germination have been reported to be caused by this pathogen. To date, 20 Pantoea spp. have been identified and recognized as having similar phenotypic and diverse characteristics. Detection via phenotypic and molecular-based approaches, for example the polymerase chain reaction (PCR) and multiplex PCR give us a better understanding of the diversity of Pantoea genus and helps to improve effective disease control strategies against this emergent bacterial pathogen of rice. In this review, we focused on the significance of rice diseases caused by Pantoea spp. and insights on the taxonomy and characteristics of this destructive pathogen via phenotypic and molecular identification.

Jackfruit (Artocarpus heterophyllus) is one of the popular and the most widely grown fruit species in Malaysia (DOA, 2013). Under the Third National Agricultural Policy of Malaysia, jackfruit has been recognized as having the potential to meet domestic demand, for export and as a replacement for imports (MOA, 2011). Recently, a disease known as jackfruit bronzing, suspected to be caused by the bacterium Pantoea stewartii is constantly found in the jackfruit crop. Typical symptom of infected jackfruits is characterized by yellowish-orange to reddish discoloration of the affected pulps and rags in the fruit (Gapasin et al. 2014). During April 2016, diseased samples with bronzing symptom of jackfruits were collected in Muadzam Shah plantation of Pahang state. These fruits were excised, rinsed with sterilized distilled water containing 1% sodium hypochlorite (NaClO), placed on nutrient agar (NA) medium using streak plate method and incubated overnight at 28 ºC. Four representative bacterial colonies were obta...