Researchers at the School of Biotechnology, Jawaharlal Nehru University, New Delhi have developed an Artificial Intelligence-based tool to predict the level of metabolite content in neem fruits as either low or high based on images of neem leaves and fruits. The AI-based approach makes the use of expensive and cumbersome analytical equipment like high-performance liquid chromatography (HPLC) redundant. The classification of fruits can be carried out in the field even by untrained people. The results have been posted on a preprint server; preprints are not peer-reviewed.
The key metabolite of neem fruits is azadirachtin, which is used as a biopesticide. However, azadirachtin content in the seed kernel varies from one tree to another. Since the fruits with different concentrations of azadirachtin are mixed and bagged together in the field, the final azadirachtin content in each sack becomes low. Currently, there is no easy tool to evaluate the azadirachtin content level (high or low) in neem fruits inexpensively. Therefore, only a few fruits from each sack with varying azadirachtin content or from a single batch of multiple sacks are taken to determine the azadirachtin content using HPLC.
The team led by Dr. Binay Panda from JNU collected 1,045 neem leaves and fruits from trees across India and imaged them and also determined the content of five metabolites — azadirachtin, salannin, deacetylsalannin, nimbin, and nimbolide — from neem fruits using HPLC. Synthetic image augmentation while training using the deep learning frameworks ensured the sufficiency of images, which served the purpose during training. “We used images of leaves and fruits and with the corresponding HPLC-measured metabolite values within different deep learning-based frameworks to test for accuracy,” says Dr. Panda. “Eighty percent of the data comprised of images and their corresponding metabolite values were first used for training, followed by 10% for model validation and the remaining 10% for model testing.”
