Paper Title

First Author1[0000-1111-2222-3333] and Second Author2[1111-2222-3333-4444]

1 Princeton University, Princeton NJ 08544, USA

2 Springer Heidelberg, Tiergartenstr. 17, 69121 Heidelberg, Germany
lncs@springer.com

Abstract. Real-time variable choice of precision spraying operations in fields benefits from the constant acquisition of crop morphology. Costly LiDAR and ultrasonic radar can't maximise their high precision. They may slow down decision speed because the current spraying amount control needs to include more information about branches and leaves, which highly relies on the statistical features of regional morphology. Since most agricultural tools in China are medium in size and relatively inexpensive, it makes sense to create a unique identification system that combines deep learning with cheap monocular vision. A crop's growth cycle was a helpful gauge for direct spraying because of the knowledge gained. For this reason, let's examine cotton as an example: This study aims to develop a single-camera acquisition system and propose an algorithm based on the Convolutional Neural Network (CNN) to identify different growth stages of cotton plants. An optimal CNN model structure is chosen among nine possible model structures using an optimization procedure based on the matrix of confusion and recognition efficiency, and its dependability is established by repeatedly switching the training and test sets by the k-fold test concept. This CNN model has an F1-score of 96.39%, an accuracy of 94.27%, a precision of 95.31%, a recall of 95.76%, and a recognition speed of 72.46 ms per picture. Also, the convolution neural network, the model suggested in this study, performs better than those of VGG16 and AlexNet. Finally, spraying deposition tests were conducted over three distinct cotton growth stages to validate the accuracy of the developed identification system and the viability of the sprayed decision-making algorithm based on CNN. The findings of the studies confirm that the ideal spray parameters were applied during distinct growth times correspondingly, leading to an increase of up to 63.24% in the optimum index over the operations that did not discriminate growth periods, yielding an overall increase of 43.29%.

Keywords: Metabolic illness, Neuropathic Ulcer, Blood vessels, Neural Network Models, Foot Ulcer Classification.

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Author, F.: Article title. Journal 2(5), 99–110 (2016).

Author, F., Author, S.: Title of a proceedings paper. In: Editor, F., Editor, S. (eds.) CONFERENCE 2016, LNCS, vol. 9999, pp. 1–13. Springer, Heidelberg (2016).

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