In traditional agriculture, crop disease control often relies on the extensive spraying of chemical pesticides. However, this “one-size-fits-all” approach not only wastes resources but also risks environmental pollution and threats to human health. With global climate change and increasing pathogen resistance, reducing pesticide dependence through smarter, more precise methods while ensuring crop yields has become a major challenge for sustainable agriculture. Is there a technology that can rapidly identify diseases and apply pesticides precisely, achieving “targeted treatment”?
Dr. Roaf Ahmad Parray from ICAR-indian agricultural research institute (ICAR-IARI) and his colleagues provide an answer in a study published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2024572). In this research, an international team of scientists from India, Denmark, and the United States developed an innovative technology integrating spectral sensors, machine learning models, and an intelligent spraying system, successfully applying it to control black rot disease in cauliflower.