Integration of Plant Electrophysiology and Time-Lapse Video Analysis via Artificial Intelligence for the Advancement of Precision Agriculture

Maria Stolarz ()
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Maria Stolarz: Department of Plant Physiology and Biophysics, Institute of Biological Sciences, Maria Curie-Skłodowska University, Akademicka 19, 20-033 Lublin, Poland

Sustainability, 2025, vol. 17, issue 12, 1-17

Abstract: Biological research and agriculture are increasingly benefiting from the use of artificial intelligence algorithms, which are becoming integral to various areas of human activity. Fundamental knowledge of the mechanisms of plant germination, growth/development, and reproduction is the basis for plant cultivation. Plants provide food and valuable biochemicals and are an important element of a sustainable natural environment. An interdisciplinary approach involving basic science (biology and informatics), technology (artificial intelligence), and farming practice can contribute to the development of precision agriculture, which in turn increases crop and food production. Nowadays, a progressive elucidation of the mechanisms of plant growth/development involves studies of interrelations between electrical phenomena occurring inside plants and movements of plant organs. Recently, there have been increasing numbers of reports on methods for classifying plant electrograms using statistical and artificial intelligence algorithms. Artificial intelligence procedures can identify diverse electrical signals—signatures associated with specific environmental abiotic and biotic factors or stresses. At the same time, a growing body of research shows methods of precise and fast analysis of time-lapse videos via automated image analysis and artificial intelligence to study the movement and growth/development of plants. In both research fields, scientists introduce modern and promising methods of studying plant growth/development. Such basic research along with technological innovations will contribute to the development of precision agriculture and an increase in yields and production of healthier food in future.

Keywords: artificial intelligence; electrome; electrical signals; electrodes; electrophysiology; machine learning; phenomics; plant electrograms; plant movements; precision agriculture; time-lapse video (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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