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.

Standard unit operations/equipment have not evolved for the traditional rice varieties of the Cauvery Deltaic region of Tamil Nadu. The fame of traditional rice is increasing nowadays owing to its health benefits. Non-standard unit operations may cause rice grains to crack during milling, accumulating more broken rice and yields in products of inferior quality. As a result, research into the physical properties of rice is crucial for the development of rice processing equipment that minimizes post-harvest losses during milling. Hence, an assessment was made to evaluate 30 traditional rice cultivars on their Physical (grain length, width, thickness, shape, and size), gravimetric (bulk, true, tapped density, porosity, Carr’s index, and Hausner ratio), and engineering characteristics (equivalent, arithmetic, square mean, and geometric mean diameter) using standard protocols, with the goal of reviving and preserving older varieties. The results from the analysis showed significant variations (p < 0.05) between all properties that were evaluated. According to length, a substantial amount of traditional rice varieties were long grain (76.7%), whereas (16.7%) belonged to the medium type and (3.3%) were short-grain types, respectively. There were variations among the three different categories of local rice grains when it comes to size, ranging from 3.26 to 4.69 mm for arithmetic mean diameter, 2.84 to 4.00 mm for geometric mean diameter, and 3.02 to 4.28 mm for square mean diameter, respectively. Sphericity, aspect ratio, and surface area measurements of the samples varied from 37.7% to 81.2%, 0.26 to 1.00, and 25.4 to 50.1 mm², respectively. Of the 30 varieties, 28 were under the high amylose category, and 2 belonged to the intermediate type. The Pearson correlation was established to study the interrelationships between the dimensions and engineering properties. Principal component analysis (PCA) reduced the dimensionality of 540 data into five principal components (PC), which explained 95.7% of the total variance. These findings suggest that it is possible to revive old landraces through careful selection and analysis of these properties. The superior characteristics of these traditional varieties can be further evaluated for breeding programs in order to improve the cultivation of these cherished rice landraces to enhance nutritional security.

Rice ( Oryza sativa L.) is a staple crop feeding over half of the global population, especially in Asia where it contributes 90% of global production. Weed infestation remains a critical constraint in rice ecosystems, significantly reducing yield. To evaluate the efficacy of non-chemical weed management practices in transplanted rice, a field experiment was conducted at the Wetland Farm of Tamil Nadu Agricultural University, Coimbatore, during rabi 2022 and kharif 2024 seasons. The study assessed growth, physiological traits and yield of rice under 12 weed management strategies arranged in a randomized block design (RBD) with three replications. The treatments included traditional chemical control as a standard treatment for comparison, hand weeding and a range of non-chemical treatments including ITK-based foliar spray (cow urine, lime solution, khadi soap and common salt), dual cropping and incorporation of Azolla , intercropping with dhaincha , rice bran application, rice hull extract, double green manuring, cono-weeding and mycoherbicide applications. The standard chemical control treatment (Butachlor @ 1 kg ha ⁻¹ at 3 DAT + Bispyribac sodium @ 25 g ha ⁻¹ at 25 DAT – T ₉ ) produced the highest grain and straw yield (5427 and 7238 kg ⁻¹ ha in rabi 2022 and 5385 and 7189 kg ⁻¹ ha in kharif 2024), followed by hand weeding twice (T ₁₁ ). Among the non-chemical treatments, the foliar spray of ITK-based farmers' practice combined with hand weeding (T ₁ ) recorded the highest value in growth, physiology and yield followed by dual cropping with Azolla (T ₅ ) and cono-weeding (T ₈ ). The findings highlights that non-chemical weed management strategies can effectively suppress weeds and sustain rice productivity, offering viable alternatives to herbicide-based approaches.

Low productivity is a major challenge in organic farming and needs to be addressed to achieve sustainability. Due to their capacity to fix nitrogen, pulses require less organic fertilizer, which makes organic farming feasible. However, a 20% loss in pulse productivity under organic cultivation has been reported. Conventional farming boosts yields with energy-intensive chemical inputs but causes long-term issues like pollution, greenhouse gas emissions, reduced soil fertility and land degradation. The yield loss can be minimised by alternative inputs like bio-formulations preferably derived from farm waste and other sustainable sources. These bio-formulations often include a mixture of beneficial microorganisms, organic nutrients and natural growth-promoting substances which are cost-effective when compared to conventional inputs and help in improved nutrient absorption, enhanced plant growth and yield, disease and pest resistance, stress tolerance and enhanced soil health. There are several approaches for applying bio-formulations to plants such as foliar spray, seed treatment, soil application, drenching, mulch integration and fertigation, all of which are designed to guarantee maximum absorption and efficacy. This review explores the various organic seed treatments and foliar fertilisation using bio-formulations such as panchagavya, jeevamrutha, beejamrutha, fish fermented waste extract, seaweed extract, vermiwash and biofertilizers to improve the productivity of pulses under organic cultivation.