Tomato (Solanum lycopersicum L.) is an economically important crop worldwide, particularly in tropical and subtropical regions. However, production is significantly and increasingly affected by the impacts of climate change, including heat and drought stress and emerging pests and diseases. This study specifically evaluated the effects of heat stress on root and shoot morphology, photosynthesis, and yield traits in five tomato genotypes, to identify the characteristics that differentiate heat tolerance from susceptibility. Heat stress experiments were conducted in a polyhouse, one during the summer under high temperatures, with a non-stress trial during the winter under conducive natural conditions. Significant reductions in yield, root traits and photosynthesis were observed across all genotypes under heat stress. However, the genotype MG785-1 maintained a relatively higher yield (298.01 ± 25.1 g), a 37.7% reduction compared to non-stress conditions, while CLN4786F1 showed resilience with a 32.3% decrease compared to its non-stress harvest index. Root dry weight (5.91 ± 0.53 g in MG785-1) and root shoot ratio (0.19 ± 0.01 in MG785-1) were identified as key traits for heat tolerance. Physiological traits, such as photosynthetic rate (11.71 ± 1.61 µmol CO₂ m⁻² s⁻¹ in MG785-1), were critical for maintaining growth under heat stress. In contrast, the heat-sensitive genotype CLN3961D exhibited a significant decline in yield and physiological performance. Root dry weight and root to shoot ratio were key indicators for heat tolerance, while the photosynthetic rate was critical for maintaining plant growth under stress. These findings underscore the importance of integrated root and physiological traits, providing valuable insights for breeding climate-resilient tomato varieties.

Agriculture encounters significant challenges, with the demand to increase food production by 50% by 2050 to sustain a growing global population while tackling the impacts of climate change and resource scarcity. Artificial intelligence (AI) has transformative potential for precision agriculture, optimizing crop management, resource allocation and sustainable farming practices. A systematic literature review (SLR) was conducted using the Scopus database, initially identifying 8145 articles. Based on eligibility criteria, 76 were selected for in-depth analysis. This paper focuses on AI applications in key areas of agriculture, including crop monitoring, irrigation management, weed and pest control, yield prediction, and smart spraying technologies. AI-driven techniques, such as machine learning, computer vision, robotics and the Internet of Things (IoT), enhance agricultural productivity and sustainability through data-driven decision-making and real-time monitoring. AI-based irrigation systems optimize water use efficiency by integrating sensor inputs with weather data, while robotic technologies enhance targeted weed and pest management. Resource efficiency is further enhanced by smart sprayers and yield estimation techniques. Despite these advancements, research gaps remain, particularly in integrating AI with emerging fields such as nutrient management and expanding the use of sensor systems. This paper highlights advancements in AI for precision agriculture, including crop monitoring, irrigation management and yield prediction, while identifying gaps in areas like nutrient management and sensor integration. Addressing these gaps is essential for developing more sustainable and resilient agricultural systems.

Rice is a vital cereal crop that is crucial to ensuring global food security. Developing high-yield varieties is essential to feed the growing world population. Selecting the best-performing genotypes based on genetic gain is critical for improving rice production. In this process, various selection indices help identify superior genotypes. Thus, the current study aimed to select superior genotypes through a multi-trait selection index. Among the multivariate indices, the widely used classical Smith-Hazel selection index (SI) and the multi-trait genotype ideotype index (MGIDI) were used to evaluate the studied genotypes for eleven quantitative traits simultaneously. Three scenarios were formulated namely, retaining multicollinearity (SI-1), removing multi-collinearity (SI-2) and using a path analysis-based index (SI-3) for estimating the Smith-Hazel selection index. Using these indices, maximum selection gains of 9.2% with the Smith-Hazel index and 18.5% with the MGIDI index for grain yield were predicted. Notably, genotypes K5 and H7 were selected by the Smith (SI-1, SI-2, SI-3) and MGIDI selection index at a selection intensity of 15%. This shows that these genotypes exhibited strong performance across various traits and were classified as elite, highlighting their potential to contribute significantly to future breeding programs aimed at improving grain yield along with multiple traits in rice. The selection of these genotypes makes a valuable resource for developing productive rice varieties.

The foliar application of nutrients and plant growth regulators (PGRs) at critical crop growth periods can improve the yield of field crops. Hence, the present study was conducted to quantify the effects of the combined application of nutrients and PGRs (crop-specific formulation) on maize, blackgram, greengram, groundnut, cotton, sugarcane, and coconut yield. In all the crops except coconut, the treatments included (i) a foliar spray of crop-specific nutrients and PGR combinations and (ii) an unsprayed control. In coconut, the treatments included (i) the root feeding of coconut-specific nutrients and PGR combinations and (ii) an untreated control. Crop-specific nutrient and PGR formulations were sprayed, namely, Tamil Nadu Agricultural University (TNAU) maize maxim 1.5% at the tassel initiation and grain-filling stages of maize, TNAU pulse wonder 1.0% at the peak flowering stage of green gram and black gram, TNAU groundnut-rich 1.0% at the flowering and pod-filling stages of groundnut, TNAU cotton plus 1.25% at the flowering and boll development stages of cotton, and TNAU sugarcane booster 0.5% at 45 days after planting (DAP), 0.75% at 60 DAP, and 1.0% at 75 DAP of sugarcane. The results showed that the foliar application of TNAU maize maxim, TNAU pulse wonder, TNAU groundnut-rich, TNAU cotton plus and TNAU sugarcane booster and the root feeding of TNAU coconut tonic increased the yield of maize, pulses, groundnut, cotton, sugarcane, and coconut, resulting in higher economic returns.