Data from: Apple flower detection using deep convolutional networks

To optimize fruit production, a portion of the flowers and fruitlets of apple trees must be removed early in the growing season. The proportion to be removed is determined by the bloom intensity, i.e., the number of flowers present in the orchard. Several automated computer vision systems have been proposed to estimate bloom intensity, but their overall performance is still far from satisfactory even in relatively controlled environments. With the goal of devising a technique for flower identification which is robust to clutter and to changes in illumination, this paper presents a method in which a pre-trained convolutional neural network (CNN) is fine-tuned to become specially sensitive to flowers. Experimental results on a challenging dataset demonstrate that our method significantly outperforms three approaches that represent the state of the art in flower detection, with recall and precision rates higher than 90%. Moreover, a performance assessment on three additional datasets previously unseen by the network, which consist of different flower species and were acquired under different conditions, reveals that the proposed method highly surpasses baseline approaches in terms of generalization capability. This dataset comprises mp4 video sequences illustrating each combination of datasets and methods.

• Resource Title: Supplementary data - Video mmc1 (7MB).

  File Name: 1-s2.0-S016636151730502X-mmc1.mp4

  Resource Description: Dataset = AppleA.

  Method on left-hand side: second baseline algorithm mentioned in the paper, where HSV is hue-saturation-value, and 'Bh' is Bhattacharyya distance.

  Method on right-hand side: our proposed method, the CNN + SVM, where CNN = convolutional neural network and SVM = support vector machine.

  True Positives (blue), False Positives (cyan), and False Negatives (red).




• Resource Title: Supplementary data - Video mmc2 (7MB).

  File Name: 1-s2.0-S016636151730502X-mmc2.mp4

  Resource Description: Dataset = AppleA.

  Method on left-hand side: third baseline algorithm mentioned in the paper, HSV + SVM, where HSV is hue-saturation-value and SVM is support vector machine.

  Method on right-hand side: our proposed method, the CNN + SVM, where CNN = convolutional neural network and SVM = support vector machine.

  True Positives (blue), False Positives (cyan), and False Negatives (red).




• Resource Title: Supplementary data - Video mmc3 (7MB).

  File Name: 1-s2.0-S016636151730502X-mmc3.mp4

  Resource Description: Dataset = AppleA.

  Method on left-hand side: first baseline algorithm mentioned in the paper, where HSV is hue-saturation-value.

  Method on right-hand side: our proposed method, the CNN + SVM, where CNN = convolutional neural network and SVM = support vector machine.

  True Positives (blue), False Positives (cyan), and False Negatives (red).




• Resource Title: Supplementary data - Video mmc4 (3MB).

  File Name: 1-s2.0-S016636151730502X-mmc4.mp4

  Resource Description: Dataset = AppleB.

  Method on left-hand side: third baseline algorithm mentioned in the paper, HSV + SVM, where HSV is hue-saturation-value and SVM is support vector machine.

  Method on right-hand side: our proposed method, the CNN + SVM, where CNN = convolutional neural network and SVM = support vector machine.

  True Positives (blue), False Positives (cyan), and False Negatives (red).




• Resource Title: Supplementary data - Video mmc5 (3MB).

  File Name: 1-s2.0-S016636151730502X-mmc5.mp4

  Resource Description: Dataset = AppleC.

  Method on left-hand side: third baseline algorithm mentioned in the paper, HSV + SVM, where HSV is hue-saturation-value and SVM is support vector machine.

  Method on right-hand side: our proposed method, the CNN + SVM, where CNN = convolutional neural network and SVM = support vector machine.

  True Positives (blue), False Positives (cyan), and False Negatives (red).




• Resource Title: Supplementary data - Video mmc6 (3MB).

  File Name: 1-s2.0-S016636151730502X-mmc6.mp4

  Resource Description: Dataset = Peach.

  Method on left-hand side: third baseline algorithm mentioned in the paper, HSV + SVM, where HSV is hue-saturation-value and SVM is support vector machine.

  Method on right-hand side: our proposed method, the CNN + SVM, where CNN = convolutional neural network and SVM = support vector machine.

  True Positives (blue), False Positives (cyan), and False Negatives (red).