An experimental approach based on universal multi-branch general-purpose convolutional neural network

• An experimental approach for a real-time tomato plant monitoring system.
• The proposed network recognizes diseases even when they occupy less than 5% of the image.
• It provides accurate detection and counting in indoor artificial light environments.
• It offers real-time optimized farm management solutions.

Real-time monitoring of tomato plants in plant factories is necessary to identify and classify diseases at the early stages to prevent possible outbreaks. The proposed DeepD381v4plus network exhibits higher class-wise accuracy, sensitivity, specificity, precision, F1 score and Matthews correlation coefficient scores exceeding 0.96 for multi-varietal tomato leaf diseases. During the reproductive stage, bud formation, flower appearance, bite marks and fruit set also need to be monitored to confirm pollination. The detector DeepDet381v4 – YOLOv4M achieves the highest mean average precision (mAP) (0.90) and lowest mAP (0.68) in the TFl_Blooming class and the lowest mAP (0.68) in the TFl_Transforming class. However, in real-world simulations, DeepDet381v4 – YOLOv4M can detect and count ripe tomatoes at a distance of 40 cm with little to no error. Both networks used for classification and detection–counting tasks are small in size with high classification and detection efficiency (>27 fps). Overall, the proposed experimental approach will help farmers prevent disease outbreaks, monitor flower shapes that can set fruits at the highest rate, detect and count ripened fruits or recognize damaged fruits due to surface cracks or diseases for harvesting at their optimal maturity stage. This will reduce labor costs, improve cultivation management and ensure harvested tomatoes are of excellent quality.

Reference URL: https://www.sciencedirect.com/science/article/pii/S0168169924005921

Bibliographic Information

Artificial intelligence assisted tomato plant monitoring system – An experimental approach based on universal multi-branch general-purpose convolutional neural network,
M.P. Islam, K. Hatou,
Computers and Electronics in Agriculture, 224,109201,
doi:10.1016/j.compag.2024.109201, 2024(June 27).

Media

• 

  AI-assisted multi-task remote monitoring system for tomatoes

  A graphical representation of the proposed AI-assisted tomato plant monitoring system

  credit : M.P. Islam, Ehime University
  Usage Restriction : Please get copyright permission