Climate change is an emerging threat to global food and nutritional security. The tropical fruits such as mango, bananas, passionfruit, custard apples, and papaya are highly sensitive to weather changes especially; changes of monsoon onset and elevated temperature are influencing crop growth and production. There is a need for more specific studies concerning individual crops and regional variations. Long-term effects and interactions of weather parameters and increased concentration of greenhouse gases, especially carbon dioxide, with phenological stages of the plant, pests, and diseases remain understudied, while adaptation strategies require further exploration for comprehensive understanding and effective mitigation. Few researchers have addressed the issues on the effect of climate change on tropical fruits. This paper focuses on the impact of abiotic (temperature, rainfall, humidity, wind speed, evaporation, carbon dioxide concentration) and biotic (pest and pathogens dynamics) factors affecting the fruit crop ecosystem. These factors influence flowering, pollination, fruit set, fruit yield and quality. This review paper will help develop adaptive strategies, policy interventions and technological innovations aimed at mitigating the adverse effects of climate change on tropical fruit production and safeguarding global food and nutritional security.

Efficient irrigation scheduling is essential for optimizing crop yields and water-use efficiency. This review examines crop simulation models and methods for improving irrigation management, with a focus on integrating weather forecast data. The FAO (Food and Agriculture Organization) developed models such as AquaCrop, WOFOST (WOrld FOod Studies), DSSAT (Decision Support System for Agrotechnology Transfer), and APSIM (Agricultural Production Systems sIMulator), exploring the incorporation of forecasted ETo (reference evapotranspiration) calculated based on forecasted values of weather through the Penman-Monteith method and rainfall data into the models using modified rule-based approaches with various forecast horizons, which enhances irrigation planning. Optimization methods, including genetic algorithms coupled with crop models, are also assessed and have shown significant water savings and profit gains compared with traditional farming practices. Emerging real-time irrigation scheduling tools, including simulation-optimization, field data assimilation, and human–machine interactions, further improve productivity and water conservation. Studies have also shown that web-based decision support using satellite remote sensing and crop models can be used to effectively monitor crop water status and predict real-time irrigation needs. Ongoing innovations like coupling crop models with optimization techniques, weather forecasting, remote sensing, and recommendations based on field experiments have shown promise for transforming irrigation planning and management.

Understanding the pattern of regional climatic extremes is essential for creating an important adaptation measure to safeguard farmers from monsoon tantrums. This paper focuses on the rainfall variability and intensity for spatially different locations of Tamil Nadu. The daily rainfall data over a period of 30 years (1990-2019) for the study locations were collected from the constituent research centres of TNAU. The results indicated that an increasing trend in SWM rainfall was observed in Coimbatore (209.3 to 300.6mm), Ooty (681.4 to 703.1mm), Aduthurai (227.8 to 320.6mm), Kovilpatti (132.8 to 141.3 mm) while the decreasing trend was observed in rest of the places. A decreasing trend was reported in general for all the places during NEM. The decreasing trend in the number of rainy days was registered in Kovilpatti, Virudhunagar and Killikulam that exhibits an alert in modifying the crop planning programme in those areas. The frequency of rainfall intensity revealed that except Ooty, the number of Heavy Rain (HR) to VHR(VHR) was found to be meagre to absent in most of the study locations.

Magnaporthe oryzae, the source of the rice blast, is a serious threat to the world’s rice supply, particularly in areas like Tamil Nadu, India. In this study, weather-based models were developed based on count time series and machine learning techniques like INGARCHX, Artificial Neural Networks (ANNs), and Support Vector Regression (SVR), to forecast the incidence of rice blast disease. Between 2015 and 2023, information on rice blast occurrence was gathered weekly from three locations (Thanjavur, Tirunelveli, and Coimbatore), together with relevant meteorological data like temperature, humidity, rainfall, sunshine, evaporation, and sun radiation. The associations between the occurrence of rice blast and environmental factors were investigated using stepwise regression analysis, descriptive statistics, and correlation. Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) were used to assess the model’s prediction ability. The best prediction accuracy was given by the ANN, which outperformed SVR and INGARCHX in every location, according to the results. The complicated and non-linear relationships between meteorological variables and disease incidence were well-represented by the ANN model. The Diebold–Mariano test further demonstrated that ANNs are more predictive than other models. This work shows how machine learning algorithms can improve the prediction of rice blast, offering vital information for early disease management. The application of these models can help farmers make timely decisions to minimize crop losses. The findings suggest that machine learning models offer promising potential for accurate disease forecasting and improved rice management.

Rice (Oryza sativa) is a staple food crucial for food security and economic stability, especially in developing countries. However, rice cultivation faces significant challenges, with rice blast disease, caused by the fungal pathogen Magnaporthe oryzae, being one of the most severe threats, potentially leading to yield losses of up to 30 %. This study aims to develop and apply regression models, including stepwise regression, multiple linear regression (MLR) and ARIMA (Autoregressive Integrated Moving Average), to predict the severity of rice blast disease based on weather parameters. Weekly data over 7 years (2017-2023) were collected from the Paddy Breeding Station at Tamil Nadu Agricultural University, Coimbatore, encompassing various weather factors such as temperature, relative humidity, rainfall and solar radiation. Data pre-processing included handling missing values, detecting outliers and creating time-lagged variables. The study revealed distinct seasonal patterns in rice blast incidence, with peak occurrences observed from mid-November to late January. Among the regression models, the ARIMA model incorporating weather variables as external regressors demonstrated superior performance with an R-squared value of 0.92, compared to 0.55 for stepwise regression and 0.57 for MLR. Accurate predictions of rice blast outbreaks could enable farmers and agricultural managers to implement timely and targeted disease management strategies, reducing dependence on broad-spectrum fungicides and minimizing crop losses. This study contributes to data-driven agriculture and disease management, potentially leading to more effective, economically viable and environmentally sustainable rice cultivation practices.

With the rapid pace of urbanization, the Urban Heat Island (UHI) effect has emerged as a significant obstacle to sustainable urban living, characterized by elevated temperatures in cities relative to surrounding rural areas. This article explores the intricate mechanisms driving UHI development, emphasizing key contributors such as the reduction of green spaces, heat-retaining construction materials, compact city layouts, and anthropogenic heat emissions. Meteorological factors further compound UHI intensity, underlining its multifaceted nature. The consequences are far-reaching ranging from increased energy demands and diminished air quality to elevated greenhouse gas emissions and negative impacts on public health and thermal comfort. Climate change exacerbates these effects by altering local weather dynamics and intensifying heat stress. A comprehensive assessment of detection techniques is provided, alongside a diverse set of mitigation approaches. These include nature-based interventions such as green roofs, vertical gardens, urban forestry, and blue infrastructure, as well as technological innovations like reflective roofs and permeable pavements. The article also evaluates the complex role of solar panels, which can both alleviate and contribute to heat accumulation in urban settings. This work contributes to the creation of heat-resilient cities and promotes a shift from concrete-dominated landscapes toward cooler, greener, and more sustainable urban environments.