Predictive analysis, data visualization and artificial intelligence tools for agricultural research and communication - Prahadeeswaran, M; Senthilnathan, S; Sumathi, C S; Sivakumar, S D; Suresh Kumar, D (Plant Science Today, 2025)
Data Science offers powerful tools that predict and promise to enhance crop yields, optimize the farm resource utilization. Artificial Intelligence (AI) can perform tasks that typically require human intelligence, which encompasses a wide range of applications in agriculture starting from seed selection to robotic harvest. Synergy between data science and AI will grow strong to drive technological advancements and shape the future of agriculture sector. This paper presents application of few data science and AI tools in agriculture. Trends, patterns and variations in cost, benefit and returns from crop like Paddy, Groundnut, Sugarcane, Cotton and Maize across different states from 2011 to 2020 were analysed through data visualization. An experimental attempt was also made through web scrapping to understand the United States consumer preference towards the brands (Spice Train, Tellicherry), quantity (11 and 14 oz) and price (15-25 USD package) of pepper. Predictive modeling had shown that the export of pepper may fluctuate over the years while export of basmati rice, cashew nuts and tea will gradually increase for the next few years. High-definition images of tomatoes of different shape, colour, size were used to train the algorithm and tested for identification and classification of tomatoes and images of FAW infested maize were trained and tested for deduction accuracy, which was 82.5 %. Prediction accuracy will increase when the algorithm is trained with large number of images. AI avatars are widely used in social communication for various purposes like short communications, storytelling and documentation, which will be also used effectively for agricultural research communication and learning purposes.