Impact of Elevated Temperature and Carbon dioxide on Seed Physiology and Yield

Food security is of utmost priority to humankind. This is the implication of various interconnected factors that lead to climate change. Elevated temperature and carbon dioxide levels are just 2 of these. The nutrient is an inseparable aspect of food. The change in climate is posing threat not only to the amount of available food but also to the nutrients laden in the food items. Seeds are the miniature form of plants and are a reflection of their future health and nutritional status. The changes in environmental factors predominantly challenge the growth and development of a seed. This review is an attempt to understand the impact of elevated CO2 and temperature on seed germination, the nutritional status of the seed and the yield in form of total seed production. It gives a direction for analysis and future studies that may use the latest available tools like gene editing to tackle and counteract the retarding effect of climate change on these parameters of seed, thereby offering a climate resilient agriculture

Linear mathematical models for yield estimation of baby corn (Zea mays L.)

Linear mathematical models have been developed for predicting baby corn yield in terms of cob volume for two cycles of maize (Zea mays L.). Cob volume is directly proportional to morphological parameters such as length, weight, and girth; hence, linear mathematical models have been developed. Primary data for a random selection of 60 cobs for each cycle were collected, and lab work was carried out to measure the corn ears and cob growth parameters. An irregular distribution was observed among all six growth parameters examined in the study. Descriptive statistical measures were employed to facilitate the description of growth parameters. The final volume of the baby corn cob was used for crop yield estimation. The water displacement method was employed to measure the actual volume of cobs, which was then compared with the volumes estimated using the developed mathematical models. For both cycles, similar trends were observed in both estimated and actual volumes of cobs, providing numerical confirmation for the validity of the developed mathematical models. The theoretical validity of these models was also established using statistical measures such as R2, adjusted R2, F-test, P-value, and correlation coefficient. Any deviations between estimated and actual volumes would indicate changes in the dependent variables of the model, attributed to the effects of climate change, as other internal and external factors are held constant. These models offer a critical predictive tool for stakeholders, enabling improved yield predictions and optimized resource allocation. As a result, they facilitate strategic planning for increased profitability

A Systematic Review and Comparative Meta-analysis of Non-destructive Fruit Maturity Detection Techniques

The global fruit industry is growing rapidly due to increased awareness of the health benefits associated with fruit consumption. Fruit maturity detection plays a crucial role in fruit logistics and maintenance, enabling farmers and fruit industries to grade fruits and develop sustainable policies for enhanced profitability and service quality. Non-destructive fruit maturity detection methods have gained significant attention, especially with advancements in machine vision and spectroscopic techniques. This systematic review provides a concise overview of the techniques and algorithms used in fruit quality grading by farmers and industries. The study reviewed 63 full-text articles published between 2012 and 2023 along with their bibliometric analysis. Qualitative analysis revealed that researchers from various disciplines contributed to this field, with techniques falling into 3 categories: machine vision (mathematical modelling or deep learning), spectroscopy and other miscellaneous approaches. There was a high level of diversity among these categories, as indicated by an I-square value of 88.37% in the heterogeneity analysis. Meta-analysis, using odds ratios as the effect measure, established the relationship between techniques and their accuracy. Machine vision showed a positive correlation with accuracy across different categories. Additionally, Egger's and Begg's tests were used to assess publication bias and no strong evidence of its occurrence was found. This study offers valuable insights into the advantages and limitations of various fruit maturity detection techniques. For employing statistical and meta-analytical methods, key factors such as accuracy and sample size have been considered. These findings will aid in the development of effective strategies for fruit quality assessment

Role of acetylcholine and acetylcholinesterase in improving abiotic stress resistance/tolerance

Abiotic stress that plants face may impact their growth and limit their productivity. In response to abiotic stress, several endogenous survival mechanisms get activated, including the synthesis of quaternary amines in plants. Acetylcholine (ACh), a well-known quaternary amine, and its components associated with cholinergic signaling are known to contribute to a variety of physiological functions. However, their role under abiotic stress is not well documented. Even after several studies, there is a lack of a comprehensive understanding of how cholinergic components mitigate abiotic stress in plants. Acetylcholine hydrolyzing enzyme acetylcholinesterase (AChE) belongs to the GDSL lipase/acylhydrolase protein family and has been found in several plant species. Several studies have demonstrated that GDSL members are involved in growth, development, and abiotic stress. This review summarizes all the possible mitigating effects of the ACh-AChE system on abiotic stress tolerance and will try to highlight all the progress made so far in this field