Predicting crop yield at preharvest is pivotal for agricultural policy and strategic decision making. Despite global agricultural targets, labour-intensive surveys for yield estimation pose challenges. Using unmanned aerial vehicle (UAV)-based multispectral sensors, this study assessed crop phenology and biotic stress conditions using various spectral vegetation indices. The goal was to enhance the accuracy of predicting key agricultural parameters, such as leaf area index (LAI), soil and plant analyser development (SPAD) chlorophyll, and grain yield of maize. The study’s findings demonstrate that during the kharif season, the wide dynamic range vegetation index (WDRVI) showcased superior correlation coefficients (R), coefficients of determination (R²), and the lowest root mean square errors (RMSEs) of 0.92, 0.86, and 0.14, respectively. However, during the rabi season, the atmospherically resistant vegetation index (ARVI) achieved the highest R and R² and the lowest RMSEs of 0.83, 0.79, and 0.15, respectively, indicating better accuracy in predicting LAI. Conversely, the normalised difference red-edge index (NDRE) during the kharif season and the modified chlorophyll absorption ratio index (MCARI) during the rabi season were identified as the predictors with the highest accuracy for SPAD chlorophyll prediction. Specifically, R values of 0.91 and 0.94, R² values of 0.83 and 0.82, and RMSE values of 2.07 and 3.10 were obtained, respectively. The most effective indices for LAI prediction during the kharif season (WDRVI and NDRE) and for SPAD chlorophyll prediction during the rabi season (ARVI and MCARI) were further utilised to construct a yield model using stepwise regression analysis. Integrating the predicted LAI and SPAD chlorophyll values into the model resulted in higher accuracy compared to individual predictions. More exactly, the R² values were 0.51 and 0.74, while the RMSE values were 9.25 and 6.72, during the kharif and rabi seasons, respectively. These findings underscore the utility of UAV-based multispectral imaging in predicting crop yields, thereby aiding in sustainable crop management practices and benefiting farmers and policymakers alike.

The fraction of absorbed photosynthetically active radiation (FAPAR) and the photosynthesis rate (Pn) of maize canopies were identified as essential photosynthetic parameters for accurately estimating vegetation growth and productivity using multispectral vegetation indices (VIs). Despite their importance, few studies have compared the effectiveness of multispectral imagery and various machine learning techniques in estimating these photosynthetic traits under high vegetation coverage. In this study, seventeen multispectral VIs and four machine learning (ML) algorithms were utilized to determine the most suitable model for estimating maize FAPAR and Pn during the kharif and rabi seasons at Tamil Nadu Agricultural University, Coimbatore, India. Results demonstrate that indices such as OSAVI, SAVI, EVI-2, and MSAVI-2 during the kharif and MNDVIRE and MSRRE during the rabi season outperformed others in estimating FAPAR and Pn values. Among the four ML methods of random forest (RF), extreme gradient boosting (XGBoost), support vector regression (SVR), and multiple linear regression (MLR) considered, RF consistently showed the most effective fitting effect and XGBoost demonstrated the least fitting accuracy for FAPAR and Pn estimation. However, SVR with R² = 0.873 and RMSE = 0.045 during the kharif and MLR with R² = 0.838 and RMSE = 0.053 during the rabi season demonstrated higher fitting accuracy, particularly notable for FAPAR prediction. Similarly, in the prediction of Pn, MLR showed higher fitting accuracy with R² = 0.741 and RMSE = 2.531 during the kharif and R² = 0.955 and RMSE = 1.070 during the rabi season. This study demonstrated the potential of combining UAV-derived VIs with ML to develop accurate FAPAR and Pn prediction models, overcoming VI saturation in dense vegetation. It underscores the importance of optimizing these models to improve the accuracy of maize vegetation assessments during various growing seasons.

Agriculture faces significant challenges including climate change, resource inefficiency, environmental degradation, and necessitating sustainable solutions. Silica nanoparticles (SiNPs), with their unique physio-chemical properties, have emerged as a promising tool to enhance agricultural productivity while reducing ecological impact. This review article explores the potential of SiNPs to revolutionize modern farming by addressing critical inefficiencies in traditional methods. The overreliance on synthetic inputs has led to soil degradation, water contamination, and declining crop resilience. SiNPs offer an innovative alternative by improving nutrient delivery systems, enhancing stress tolerance, and reducing the environmental footprint of agricultural practices. SiNPs significantly enhance nutrient use efficiency (NUE) through controlled and sustained release mechanisms, minimizing losses and ensuring consistent crop uptake. Their application also bolsters plant resilience against abiotic stresses such as drought and salinity, as well as biotic threats from pests and pathogens. Mechanistically, SiNPs improve photosynthetic efficiency, regulate stress-responsive genes, and fortify plant cell walls, creating both biochemical and mechanical defenses. Moreover, SiNPs are biocompatible and environmentally safe, degrading into bioavailable monosilicic acid that enriches soil health and supports beneficial microbial communities. They mitigate heavy metal toxicity and reduce dependency on conventional agrochemicals, aligning with global sustainability goals. This assessment explores the functional properties, application and mechanism of SiNPs for management of biotic and abiotic stress controlling and paves the way for sustainable agriculture.

Seaweed is an increasingly recognized source of secondary metabolites with a significant role as a plant biostimulant. With the rising popularity of organic farming, there is a pressing need to investigate the utilization of organic solvents derived from natural sources for seaweed extraction. Understanding the composition of these extracts is crucial for elucidating their role as biostimulants in plant growth. Hence, the present study aimed to explore the phytochemical composition and untargeted metabolomics of four extracts of Caulerpa racemosa (SW), prepared organically using vinegar, fermented buttermilk, and cow urine, in comparison with the conventionally used solvent, water. Approximately 95 volatile metabolites that belong to 22 chemical classes were identified through GC–MS analysis. The major classes detected include benzene and its substituted derivatives (24%), fatty acyls (17%), phenols (12%), benzofurans (8%), organo-oxygen compounds (6%), steroids and steroid derivatives (5%), and indole and its derivatives (4%). A significant shift in the composition of volatile metabolites was observed among the organic extracts as determined through a combination of various statistical analyses. A principal component analysis (PCA) highlighted seven principal components (PCs), which accounted for 99.9% of the total variance. Hierarchical cluster analysis delineated two distinct clusters, which signify a clear differentiation among the organic extracts. Furthermore, partial least squares discriminant analysis (PLS-DA) highlighted 49 volatile metabolites of significant importance, evident from variable importance in projection (VIP) scores of more than 1. These results underscore the intricate and distinct metabolomes inherent in Caulerpa racemosa organic extracts, hinting at their potential as biostimulants for plant growth. This study establishes a foundation for deeper investigations into seaweed extraction utilizing easily accessible organic solvents, presenting potential benefits for farmers.

Regenerative Agriculture is a promising concept that aims to address the challenges posed by conventional farming methods, which often lead to soil degradation and reduced productivity over time. The ultimate goal of regenerative agriculture is to revitalize the soil and landscape while offering environmental, economic and social advantages to communities. Prioritizing soil health and carbon sequestration are the key principles of regenerative agriculture. These principles include maintaining soil cover, minimizing disturbance, sustaining living roots year-round, fostering species diversity, incorporating livestock, and reducing synthetic inputs such as herbicides and fertilizers. The implementation of crop diversification and rotation techniques is a key strategy in regenerative agriculture. These techniques promote the cycling of nutrients into the soil and enhance the diversity of soil microorganisms such as bacteria. Agroforestry, another component of regenerative agriculture, contributes to carbon sequestration by utilizing stable deep-rooted systems and storing carbon within plant biomass. However, the overall efficacy of these methods may vary in different environments. Potential limitations include the upper thresholds for carbon sequestration and increased nitrogen demand. Although there are challenges to be addressed, regenerative agriculture shows promise in improving soil quality, crop productivity, and overall farm economics. This represents a shift towards more sustainable and resilient farming practices that could benefit the environment and communities.

Nutrients play a pivotal role in the growth, development and overall health of plants. However, the widespread use of inorganic fertilizers in modern agriculture has led to significant environmental and health concerns, including soil degradation, water contamination and the disruption of beneficial soil microbiota. These issues underscore the urgency of exploring alternative, sustainable fertilization methods. This review critically examines the potential of organic fertilizers in addressing the nutritional deficiencies in soil that are exacerbated by the reliance on inorganic inputs. Our research hypothesizes that organic fertilizers not only replenish essential nutrients but also restore soil health by enhancing microbial diversity and activity. This hypothesis is significant and timely, given the growing global demand for sustainable agricultural practices. For the first time, our synthesis reveals overlooked interdisciplinary connections between soil microbiology, plant nutrition and sustainable agricultural economics, offering a novel perspective on the integration of organic fertilizers into mainstream agricultural practices. The breakthrough identified in this review lies in the comprehensive understanding of how organic fertilizers contribute to long-term soil fertility and crop productivity, beyond merely supplying nutrients. Moreover, this review identifies promising business opportunities in the development and commercialization of organic fertilizers tailored to specific crop and soil needs. Prospective directions for further research include the exploration of advanced organic formulations and the assessment of their economic viability on a large scale.