Abstract

Convolutional neural networks (CNNs) have proven their efficiency in various applications in agriculture. In crops such as date, they have been mainly used in the identification and sorting of ripe fruits. The aim of this study was the performance evaluation of eight different CNNs, considering transfer learning for their training, as well as five hyperparameters. The CNN architectures evaluated were VGG-16, VGG-19, ResNet-50, ResNet-101, ResNet-152, AlexNet, Inception V3, and CNN from scratch. Likewise, the hyperparameters analyzed were the number of layers, the number of epochs, the batch size, optimizer, and learning rate. The accuracy and processing time were considered to determine the performance of CNN architectures, in the classification of mature dates’ cultivar Medjool. The model obtained from VGG-19 architecture with a batch of 128 and Adam optimizer with a learning rate of 0.01 presented the best performance with an accuracy of 99.32%. We concluded that the VGG-19 model can be used to build computer vision systems that help producers improve their sorting process to detect the Tamar stage of a Medjool date.

Highlights

  • The date palm fruit (Phoenix dactylifera L.) is a berry composed of a fleshy mesocarp, covered by a thin epicarp and an endocarp covering all of its seed [1]

  • WE looked at AlexNet, Inception Version 3, and a Convolutional neural networks (CNNs) from scratch

  • The lowest performance was for AlexNet (64.19%) and ResNet152 (64.17%), for a learning rate (0.001), and CNN from scratch (46.62% and 53.38%), with a learning rate (0.01)

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Summary

Introduction

The date palm fruit (Phoenix dactylifera L.) is a berry composed of a fleshy mesocarp, covered by a thin epicarp and an endocarp covering all of its seed [1] The name of this fruit is “date,” which comes from the Greek word “Daktylos,” which means “finger” [2]. This fruit has been the primary source of food in several countries in the Middle East, playing an essential role in the economy, society, and environment [3]. This fruit’s growth presents a progressive maturity level in four stages known by their. In the last stage (Tamar), the fruit is ripe and ready to be harvested [4]

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