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.

The sustainability of irrigated agriculture is jeopardized by catastrophic climate change, with projected forecasts indicating that by 2025, one out of every four people on the planet will be experiencing extreme water scarcity. In this context, an attempt was made for scheduling irrigation at a regional scale combining satellite data and agrometeorological indices over major rice growing tracts of Palakkad district in Kerala. Normalized Difference Vegetation Index (NDVI) product of MODIS (MOD13Q1) with a temporal resolution of 16 days and a spatial resolution of 250 m was utilized to establish a relationship with crop coefficient (Kc) of rice during the mundakan rice season of 2020-21 and 2021-22 in 30 ground truth locations. The results revealed that NDVI values have strong relationship with Kc values with an R2 value of 0.81. Crop coefficient (Kc) maps developed using satellite derived NDVI provided Kc values at a regional scale during different stages of crop growth and it helped to estimate crop evapotranspiration with greater accuracy. Based on this crop water demands maps depicting the spatial and temporal distribution of irrigation requirement were generated for the whole study area. These maps can be used as a tool for the estimation of the crop water requirement of a rice field if the geographical coordinates of the location are known. The total crop water requirements estimated during mundakan season 2020-21 and 2021-22 in Palakkad district were in the range of 700-975 mm and 560-897 mm respectively. Integration of remote sensing & agrometeorological techniques has scope for regional-scale crop water requirement estimation in a cost-effective and time-bound manner.

Proper calculation of rice cultivation area well before harvest is critical for projecting rice yields and developing policies to assure food security. This research looks at how Remote Sensing (RS) and Geographic Information System (GIS) can be used to map rice fields in Palakkad district of Kerala. The area was delineated using three multi-temporal cloud free Sentinel-2 data with 10 m spatial resolution, matching to crop's reproductive stage during mundakan season (September-October to December-January), 2020-21. To make classification easier, the administrative boundary of district was placed over the mosaicked image. The rice acreage estimation and land use classification of the major rice tract of Palakkad district comprising five blocks was done using Iterative Self-Organisation Data Analysis Technique (ISODATA) unsupervised classification provision in ArcGIS 10.1 software, employing False Colour Composite (FCC) including Blue (B2), Green (B3), Red (B4) and Near-infrared (B8) Bands of Sentinel-2 images. The classification accuracy was determined by locating a total of 60 validation points throughout the district, comprising 30 rice and 30 non-rice points. The total estimated area was 24742.76 ha, with an average accuracy of 88.33% and kappa coefficient 0.766 in five blocks of Palakkad district. The information generated will be helpful in assessing the anticipated production as well as the water demand of the rice fields.

Mango area mapping is crucial for forecasting production prior to harvest and developing policies that ensure food and nutritional security. This study focuses on the integration of remote sensing and machine learning algorithms for real time prediction of mango cropped area in Krishnagiri and Dharmapuri districts of Tamil Nadu state in India. The cloud free images of Sentinel 2 were acquired corresponding to the fruit setting stage of the crop during the main crop season (January to June 2024) and pre-processed in ArcGIS 10.8 software. Supervised classification was carried out using eCognition Developer 10.3 software by combining object-based image analysis (OBIA) and Random Forest algorithm. Based on the analysis, the area under mango cultivation in Krishnagiri district was 31824.22 hectares, showing a 1.5 % deviation from the Department of Economics and Statistics data. The overall classification accuracy was 0.85, with a kappa index of 0.70. In Dharmapuri district, the mango area was slightly overestimated (14950.24 ha) compared to the government data (14589 ha), with a percent deviation of -2.5 %. The overall classification accuracy was 0.88, with a kappa index was 0.76. Accurate mango area mapping supports precision agriculture, efficient resource management and yield optimization, thereby aiding farmers, scholars and policymakers in informed decision-making.