Artificial intelligence cannot fundamentally replace biological science. However, whereas researchers previously conducted experiments for many years when formulating hypotheses, it is now possible to make rapid predictions based on large volumes of data. This transformation is particularly evident in plant biology, where satellite data, drones, and camera imagery enable the automatic detection of plant ecology, stress factors, and signs of disease.
This was stated by Professor Sayyara Ibadullayeva, Acting Director of the Institute of Botany PLE under the Ministry of Science and Education. She noted that rapid modeling of protein structures helps develop plant varieties that are more resistant to drought, heat, and diseases. As a result, botanical science not only studies plants but also enables their design within nature, making the process faster and more efficient.
S. Ibadullayeva emphasized that artificial intelligence is no longer merely an auxiliary tool in biology but has become one of its primary instruments. In plant biology, it integrates large volumes of image, genomic, and sensor data to predict how a plant will grow, which diseases it may contract, and how productive it will be. For example, it is highly useful for early disease detection, selecting new species diversity, and identifying plants adaptable to climate change. Overall, it saves researchers time, reduces errors, and strengthens the link between laboratory research and computer modeling. It also makes plant introduction smarter and more sustainable.
The professor added that key applications of artificial intelligence in botany include detecting growth patterns, diseases, and stress from plant images; selecting species diversity through genomic data; improving photosynthesis and nutrient uptake; and creating new plant traits (for example, through synthetic pathways). In general biology, drug discovery and protein design are also prominent areas. In the coming years, AI-designed and gene-edited plant varieties (such as drought-resistant wheat or higher oil-yielding soybeans) are expected to become widespread. Plant root systems and their interactions with microbes will be modeled more accurately, diseases will be detected earlier and prevented, and productivity will increase.
Of course, issues such as data protection and transparency of results must also be carefully addressed. Ultimately, artificial intelligence is guiding plant science from discovery toward practical design and sustainable agriculture.
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