Kinnow production is hampered due to the lack of micronutrient applications such as zinc (Zn), iron (Fe), and manganese (Mn), which play a significant role in the metabolic activities of the plant, affecting yield and quality. The farmers of the region use mineral micronutrient fertilizers, but it leads to phytotoxicity due to unoptimized fertilizer application dose. In the present investigation, an attempt has been made to optimize the Zn, Mn, and Fe minerals dose as tank mix foliar application for improvement of fruit yield, quality, and uptake of nutrients. The twelve combinations of different doses of zinc sulphate, manganese sulphate, and ferrous sulphate fertilizers replicated three times were tested at kinnow orchards established at Krishi Vigyan Kendra, Bathinda, Punjab, India. The data revealed that the fruit drop was significantly low in the treatment F₁₂ (43.4%) (tank mix spray of 0.3% ZnSO₄ + 0.2% MnSO₄ + 0.1% FeSO₄ ) compared to control treatment. The fruit yield per tree was significantly higher in the treatment F12 compared to untreated control. The juice percentage was also recorded higher in treatment F12 as compared to control, and the juice percentage improved by 2.6%. The leaf nutrient analysis also revealed translocation of higher amount of nutrient from leaf to fruit under optimized supply of micronutrient. Thus, the application of tank mix spray of 0.3% ZnSO₄ + 0.2% MnSO₄ + 0.1% FeSO₄ may be used for better fruit yield and quality.

In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.

In this investigation the effect of collection seasons of explants (winter, spring and summer), type of explants (leaf disc and intermodal segments) and length of explants (0.5, 1.0 and 1.5 cm) for callusing in low-chill peach were standardized. The maximum callus induction (97.78%) in the low-chill peach was obtained from the intermodal segments of 0.5 cm in length used as an explant collected during spring season. The structural changes on the surface of the callus (5–7 weeks old yellowish green compact callus) during the progress of somatic embryogenesis of low-chill peach from the both intermodal segment as well as leaf disc derived callus were also examined with the use of scanning electron microscope (SEM). The SEM studies indicated that callus derived from internodal segment explant had the highest frequency of somatic embryos than callus from leaf discs. The SEM investigation, also demonstrated the sequential events/steps leading to low-chill peach somatic embryogenesis which was originating from somatic embryo mother cells through one unicellular pathway. Two types of calli were morphologically distinguished in both leaf disc and intermodal segment generated callus and these were the compact, well organized yellowish green embryogenic callus, containing large number of small, rich cytoplasmic, starch containing meristematic cells and soft and unorganized non-embryogenic callus containing sparsely cytoplasmic, vacuolated, and large cells devoid of metabolic reserves. The present SEM studies clearly demonstrated that somatic cells from peach explants generated callus could develop into fully differentiated somatic embryos through the characteristic embryological patterns of differentiation.

Plant growth-promoting rhizobacteria (PGPR) offer eco-friendly alternatives to chemical fertilizers, promoting sustainable agriculture by enhancing soil fertility, reducing pathogens, and aiding in stress resistance. In agriculture, they play a crucial role in plant growth promotion through the production of agroactive compounds and extracellular enzymes to promote plant health and protection against phytopathogens. In the rhizosphere, diverse microbial interactions, including those with bacteria and fungi, influence plant health by production of antimicrobial compounds. The antagonism displayed by rhizobacteria plays a crucial role in shaping microbial communities and has potential applications in developing a natural and environmentally friendly approach to pest control. The rhizospheric microbes showcase their ecological importance and potential for biotechnological applications in the context of plant-microbe interactions. The extracellular enzymes produced by rhizospheric microbes like amylases, chitinases, glucanases, cellulases, proteases, and ACC deaminase contribute to plant processes and stress response emphasizing their importance in sustainable agriculture. Moreover, this review highlights the new paradigm including artificial intelligence (AI) in sustainable horticulture and agriculture as a harmonious interaction between ecological networks for promoting soil health and microbial diversity that leads to a more robust and self-regulating agricultural system for protecting the environment in the future. Overall, this review emphasizes microbial interactions and the role of rhizospheric microbial extracellular enzymes which is crucial for developing eco-friendly approaches to enhance crop production and soil health.