Plant growth-promoting rhizobacteria (PGPR) is a microbial population found in the rhizosphere of plants that can stimulate plant development and restrict the growth of plant diseases directly or indirectly. In this study, 90 rhizospheric soil samples from five agro climatic zones of chilli (Capsicum annuum L.) were collected and rhizobacteria were isolated, screened and characterized at morphological, biochemical and molecular levels. In total, 38% of rhizobacteria exhibited the antagonistic capacity to suppress Ralstonia solanacearum growth and showed PGPR activities such as indole acetic acid production by 67.64% from total screened rhizobacteria isolates, phosphorus solubilization by 79.41%, ammonia by 67.75%, HCN by 58.82% and siderophore by 55.88%. We performed a principal component analysis depicting correlation and significance among plant growth-promoting activities, growth parameters of chilli and rhizobacterial strains. Plant inoculation studies indicated a significant increase in growth parameters and PDS1 strain showed maximum 71.11% biocontrol efficiency against wilt disease. The best five rhizobacterial isolates demonstrating both plant growth-promotion traits and biocontrol potential were characterized and identified as PDS1—Pseudomonas fluorescens (MN368159), BDS1—Bacillus subtilis (MN395039), UK4—Bacillus cereus (MT491099), UK2—Bacillus amyloliquefaciens (MT491100) and KA9—Bacillus subtilis (MT491101). These rhizobacteria have the potential natural elicitors to be used as biopesticides and biofertilizers to improve crop health while warding off soil-borne pathogens. The chilli cv. Pusa Jwala treated with Bacillus subtilis KA9 and Pseudomonas fluorescens PDS1 showed enhancement in the defensive enzymes PO, PPO, SOD and PAL activities in chilli leaf and root tissues, which collectively contributed to induced resistance in chilli plants against Ralstonia solanacearum. The induction of these defense enzymes was found higher in leave tissues (PO—4.87-fold, PP0—9.30-fold, SOD—9.49-fold and PAL—1.04-fold, respectively) in comparison to roots tissue at 48 h after pathogen inoculation. The findings support the view that plant growth-promoting rhizobacteria boost defense-related enzymes and limit pathogen growth in chilli plants, respectively, hence managing the chilli bacterial wilt.

Microbial volatiles benefit the agricultural ecological system by promoting plant growth and systemic resistance against diseases without harming the environment. To explore the plant growth-promoting efficiency of VOCs produced by Pseudomonas fluorescens PDS1 and Bacillus subtilis KA9 in terms of chili plant growth and its biocontrol efficiency against Ralstonia solanacearum, experiments were conducted both in vitro and in vivo. A closure assembly was designed using a half-inverted plastic bottle to demonstrate plant–microbial interactions via volatile compounds. The most common volatile organic compounds were identified and reported; they promoted plant development and induced systemic resistance (ISR) against wilt pathogen R. solanacearum. The PDS1 and KA9 VOCs significantly increased defensive enzyme activity and overexpressed the antioxidant genes PAL, POD, SOD, WRKYa, PAL1, DEF-1, CAT-2, WRKY40, HSFC1, LOX2, and NPR1 related to plant defense. The overall gene expression was greater in root tissue as compared to leaf tissue in chili plant. Our findings shed light on the relationship among rhizobacteria, pathogen, and host plants, resulting in plant growth promotion, disease suppression, systemic resistance-inducing potential, and antioxidant response with related gene expression in the leaf and root tissue of chili.

Crops’ production and quality of yields are heavily affected by crop diseases which cause adverse impacts on food security as well as economic losses. In India, agriculture is a prime source of income in most rural areas. Hence, there is an intense need to employ novel and accurate computer vision-based techniques for automatic crop disease detection and their classification so that prophylactic actions can be recommended in a timely manner. In literature, numerous computer vision-based techniques by utilizing divergent combinations of machine learning, deep learning, CNN, and various image-processing techniques along with their associated merits and demerits have already been discussed. In this study, we systematically reviewed recent research studies undertaken by a variety of scholars and researchers of fungal and bacterial plant disease detection and classification and summarized them based on vital parameters like type of crop utilized, deep learning/machine learning architecture used, dataset utilized for experiments, performance matrices, types of disease detected and classified, and highest accuracy achieved by the model. As per the analysis carried out, in the category of machine learning-based approaches, 70% of studies utilized real-field plant leaf images and 30% utilized laboratory condition plant leaf images for disease classification while in the case of deep learning-based approaches, 55% studied employed laboratory-conditioned images from the PlantVillage dataset, 25% utilized real-field images, and 20% utilized open image datasets. The average accuracy attained with deep learning-based approaches is quite higher at 98.8% as compared to machine learning-based approaches at 92.2%. In the case of deep learning-based methods, we also analyzed the performances of pretrained and training from scratch models that have been utilized in various studies for plant leaf disease classification. Pretrained models perform better with 99.64% classification accuracy compared to training from scratch models which achieved 98.64% average accuracy. We also highlighted some major issues encountered in the computer vision-based disease detection and classification approach used in literature and provided recommendations that will help and guide researchers to explore new dimensions in crop disease recognition.