Plum Ripeness Analysis in Real Environments Using Deep Learning with Convolutional Neural Networks

Rolando Miragaia,Antonio Vivas,Maria Henar Prieto,Maria José Moñino,Francisco Chávez,Josefa Díaz

doi:10.3390/agronomy11112353

Rolando Miragaia, Antonio Vivas + Show 4 more

Open Access

https://doi.org/10.3390/agronomy11112353

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Abstract

Digitization and technological transformation in agriculture is no longer something of the future, but of the present. Many crops are being managed by using sophisticated sensors that allow farmers to know the status of their crops at all times. This modernization of crops also allows for better quality harvests as well as significant cost savings. In this study, we present a tool based on Deep Learning that allows us to analyse different varieties of plums using image analysis to identify the variety and its ripeness status. The novelty of the system is the conditions in which the designed algorithm can work. An uncontrolled photographic acquisition method has been implemented. The user can take a photograph with any device, smartphone, camera, etc., directly in the field, regardless of light conditions, focus, etc. The robustness of the system presented allows us to differentiate, with 92.83% effectiveness, three varieties of plums through images taken directly in the field and values above 94% when the ripening stage of each variety is analyzed independently. We have worked with three varieties of plums, Red Beaut, Black Diamond and Angeleno, with different ripening cycles. This has allowed us to obtain a robust classification system that will allow users to differentiate between these varieties and subsequently determine the ripening stage of the particular variety.

Highlights

We focus on a deep learning (DL) technique for supervised learning known as deep convolutional neural networks (CNN) [1,2], which has shown outstanding performances for visual objects recognition
We demonstrate the mean image representation obtained for a training dataset, and we show mean pixel values computed for the same training dataset
This modernization process results in important cost savings and in higher quality harvests

Summary

Introduction

Extremadura is a region in Spain with the highest agricultural potential and one of the largest exporters of raw materials in the field of agriculture. Our region is characterized by its exports, among others, of fruits such as plums. In the European Union as a whole, Spain is considered to be one of the largest producers of plums. The volume of tonnes produced has been growing, most of which is destined for export, creating an strong foreign market. According to the the United Nations Food and Agriculture. Organization (FAO), production in Spain in 2019 reached 179,840 tonnes of plums, making it one of the top 13 producing countries in the world. Extremadura, with 4885 ha of area in production, in 2018 is the leading national producer, with more than 74,000 tonnes.

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