Multimodal perishable fruits and vegetables dataset

There is a growing need in the agricultural industry for non-invasive methods to classify the freshness and quality of produce. To address this, we developed a multimodal dataset comprising six commonly exported fruits and vegetables from India: guava, carrot, tomato, Indian gooseberry, banana, and mango. The specimens were allowed to undergo decomposition in an indoor environment with natural lighting, ambient temperature fluctuations and controlled air-flow. During this process, IR-Fusion images, sRGB images, and methane concentration readings were collected over a varying period and compiled. The dataset supports research in classification, food spoilage detection, shelf-life prediction, multimodal data fusion, non-invasive fruit quality assessment, and deep learning-based freshness assessment, particularly for export-oriented supply chains. The dataset is motivated by the need to reduce post-harvest losses and improve food quality monitoring, where spoilage indicators are often not detectable through visual inspection alone; the integration of imaging and gas-based sensing enables more reliable and automated freshness assessment. The dataset, with a total size of 18. 99 GB, contains over 14, 000 sRGB images, 14, 500 IR-fusion images, and 18 methane sensor files, organized into Normal and Classified (Spoiled/Notₛpoiled) categories. This multimodal design enables the study of thermal, visual, and chemical spoilage indicators simultaneously. This work aligns with the principles of smart agriculture, which promote the use of modern, data-driven technologies to optimize resource use and enable real-time monitoring for sustainable and efficient agricultural practices. Within the Agriculture 5. 0 (AG 5. 0) paradigm, the integration of Artificial Intelligence (AI), the Internet of Things (IoT), and complementary sensing modalities plays a central role in advancing innovation across farming and post-harvest management. In this context, the proposed multimodal dataset supports AG 5. 0 objectives by enabling intelligent, automated, and non-invasive produce quality assessment, thereby improving decision-making and reducing waste throughout agricultural and export-oriented supply chains.