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Fruit Detection and Counting Model research for Yield mapping and Robotic harvesting

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Fruit Detection and Ripeness Analysis in Mango Orchards

Detection Results:

Yolov4 to the Left and Yolov5 to the Right

29th July 2020

YoloV4 and V5 models are trained and tested under consistent conditions of augmentations and pre-processing.

YoloV4 and YoloV5 Training Conditions:

Mangos are annotated in their respective formats which can be accessed from YoloV4 augmented Dataset and YoloV5 augmented Dataset folders.

Pre-Processing:

  • Auto-orientation of pixel data
  • Resizing to 416x416

Augmentation:

  • 50% probability of horizontal flip
  • 50% probability of vertical flip
  • Random rotation of between -15 and +15 degrees
  • Random shear of between -15° to +15° horizontally and -15° to +15° vertically
  • Random brigthness adjustment of between 0 and +85 percent
  • Random exposure adjustment of between -27 and +27 percent
  • Salt and pepper noise was applied to 1 percent of pixels

Here are the results obtained with their best weights.

Parameters Yolov4(6000 epochs) Yolov5(300 epochs) Yolov4-tiny(6000 epochs) Detectron 2 Faster RCNN
Precision 0.76 0.5676 0.64
Recall 0.83 0.8427 0.68
F1-score 0.80 0.678 0.66
mAP@0.5 0.82 0.791 0.6146

References and Similar Work Results.

Title Architectures Used Data Augmentation Techniques Evaluation of Detection Performance Graphs Plotted Results
Deep Fruit Detection in Orchards Faster R-CNN Image Flipping and Rescaling Tiling approach FR-CNN Average Precision Response Area under Precision Recall Curve, F1-score Avg.Precision vs Number of Training images F1 score > 0.9; Precision = 0.958; Recall = 0.863
Fast implementation of real-time fruit detection in apple orchards usingdeep learning LedNet Two level scale amplification Average Precision Response, IoU, Precision, Recall Focal Loss and MSE functions vs Object confidence Score Recall = 0.821; Accuracy = 0.853

Ripeness Analysis:

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