Internet of things (IoT) results in massive amount of streaming data, often referred to as “big data”, which brings new opportunities to monitor agricultural and food processes. Besides sensors, big data from social media is also becoming important for the food industry. In this review we present an overview of IoT, big data, and artificial intelligence (AI) and their disruptive role in shaping the future of agri-food systems. Following an introduction to the fields of IoT, big data, and AI, we discuss the role of IoT and big data analysis in agriculture (including greenhouse monitoring, intelligent farm machines, and drone-based crop imaging), supplychain modernization, social media (for open innovation and sentiment analysis) in food industry, food quality assessment (using spectral methods and sensor fusion), and finally, food safety (using gene sequencing and blockchain based digital traceability). A special emphasis is laid on the commercial status of applications and translational research outcomes.

The latest farming technology utilizes specialized electrochemical soil sensors for precision agriculture. Soil pH is the primary soil attribute which influences the crop yield by affecting the availability of nutrients to plants. Hence, monitoring and maintaining optimal soil pH is of utmost importance for enhancing the farm output. In this study, a low-cost, all-solid-state electrochemical extended gate field effect transistor (EGFET) based soil pH sensor is fabricated by exploring the pH sensing and specific conductive properties of Titanium Nitride (TiN) in conjunction with a low-drift planar Ag/AgCl reference electrode (PARE). The thickness dependence and hydrophilicity of sputtered TiN pH sensing films on pH response is investigated. The TiN pH electrode with a thickness of 50 nm demonstrated a higher sensitivity of 57.25 mV/pH and is relatively more hydrophilic with a water contact angle of 46.425°. The fabricated TiN-PARE soil pH sensor measured the soil pH and pH _Ca with a maximum relative error of 2.98% and 4.31%, when compared with a laboratory pH meter, and demonstrated a linear pH response with R ² > 0.99. The reusability and longevity are ensured by postconditioning of the TiN pH electrode with Oxalic Acid. Hence, the developed TiN-PARE soil pH sensor is promising for reliable pH measurements.

This study introduced a novel approach to 3D image segmentation utilizing a neural network framework applied to 2D depth map imagery, with Z axis values visualized through color gradation. This research involved comprehensive data collection from mechanically harvested wild blueberries to populate 3D and red–green–blue (RGB) images of filled totes through time-of-flight and RGB cameras, respectively. Advanced neural network models from the YOLOv8 and Detectron2 frameworks were assessed for their segmentation capabilities. Notably, the YOLOv8 models, particularly YOLOv8n-seg, demonstrated superior processing efficiency, with an average time of 18.10 ms, significantly faster than the Detectron2 models, which exceeded 57 ms, while maintaining high performance with a mean intersection over union (IoU) of 0.944 and a Matthew’s correlation coefficient (MCC) of 0.957. A qualitative comparison of segmentation masks indicated that the YOLO models produced smoother and more accurate object boundaries, whereas Detectron2 showed jagged edges and under-segmentation. Statistical analyses, including ANOVA and Tukey’s HSD test (α = 0.05), confirmed the superior segmentation performance of models on depth maps over RGB images (p < 0.001). This study concludes by recommending the YOLOv8n-seg model for real-time 3D segmentation in precision agriculture, providing insights that can enhance volume estimation, yield prediction, and resource management practices.