Grapes are one of the world’s most widely distributed crops and are cultivated in more than 100 countries in the global scheme. Due to climate change and improper vine growth variable selection, production has significantly decreased across countries. Therefore, the primary purpose of this study was to develop a land suitability analysis method using a fuzzy expert system at a regional scale. The fuzzy membership function was used in the ArcGIS^® environment to perform the spatial analysis, and the overlay function was used to generate the final suitability map for Afghanistan considering policy planning. The results indicated that 23% (15,760,144 ha) of the areas were potential and located in the highly suitable region for grape production; however, 11% (7,370,025 ha) of the regions were not suitable for vineyards throughout the country of Afghanistan. In the present study, it was observed that most of the vineyards were in highly suitable areas (90%, 80,466 ha), while 0.01% (5 ha) of the vineyards were in less suitable areas. The present analysis demonstrated that the significant extension of grape vines can be possible in highly suitable areas. The results of this research can support decision-makers, farm managers and land developers to find more prospective acreage for expanding vineyards in Afghanistan.

Non-destructive diameter estimation of cabbage heads and yield prediction employing Unmanned Aerial Vehicle (UAV) imagery are superior to conventional approaches, which are labor intensive and time consuming. This approach assesses spatial variability across the field, effective allocation of resources, and supports variable application rates of fertilizer and supply chain management. Here, individual cabbage head diameters were estimated using deep learning-based pose estimation models (YOLOv8s-pose and YOLOv11s-pose) using high spatial resolution RGB images acquired from UAV 6 m during the cabbage-growing season in 2024. With a mean relative error (MRE) of 4.6% and a high mean average precision (mAP) 98.5% at 0.5, YOLOv11s-pose emerged as the best-performing model, verifying its accuracy for pragmatic agricultural use. The approximated diameter was then combined with climatic variables (temperature and rainfall) and canopy reflectance indices (normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and green chlorophyll index (CIg)) that were extracted from the multispectral images with 6 m resolution and fed into AI models to develop individual cabbage head fresh weight. Among the machine learning models (MLMs) tested, CatBoost achieved the lowest Mean Squared Error (MSE = 0.025 kg/cabbage), highest R² (0.89), and outperformed other models based on the Diebold–Mariano statistical test (p < 0.05). This finding suggests that an integrated AI-powered framework enhances non-invasive and precise yield estimation in cabbage farming.