Visual detection of green mangoes by an unmanned aerial vehicle in orchards based on a deep learning method

Recent advances in computer vision have allowed broad applications in every area of life, and agriculture is not left out. For the agri-food industry, the use of advanced technology is essential. Owing to deep learning’s capability to learn robust features from images, it has witnessed enormous application in several fields. Fruit detection and classification remains challenging due to the form, color, and texture of different fruit species. While studying the impact of computer vision on fruit detection and classification, we pointed out that till 2018 many conventional machine learning methods were utilized while a few methods exploited the application of deep learning methods for fruit detection and classification. This has prompted us to pursue an extensive study on surveying and implementing deep learning models for fruit detection and classification. In this article, we intensively discussed the datasets used by many scholars, the practical descriptors, the model’s implementation, and the challenges of using deep learning to detect and categorize fruits. Lastly, we summarized the results of different deep learning methods applied in previous studies for the purpose of fruit detection and classification. This review covers the study of recently published articles that utilized deep learning models for fruit identification and classification. Additionally, we also implemented from scratch a deep learning model for fruit classification using the popular dataset “Fruit 360” to make it easier for beginner researchers in the field of agriculture to understand the role of deep learning in the agriculture domain.

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“…). YOLO V2 was used by Xiong et al [88] for the visual detection of green mangos in orchards with the aid of an unmanned aerial vehicle (UAV).…”

Section: You Only Look Once (Yolo V2mentioning

confidence: 99%

Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques

Ukwuoma

Qin

Heyat

et al. 2022

Mathematical Problems in Engineering

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“…The YOLOv2 architecture (39) was used to detect and count mangos in images photographed by an unmanned aerial vehicle in the work conducted by Xiong and Liu (51). The images were manually labeled.…”

Section: Pre-harvest Fruit Image Processingmentioning

confidence: 99%

Pre-Harvest Fruit Image Processing: A Brief Review

Gremes¹,

Krummenauer²,

Andrade³

et al. 2021

BJEDIS

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Agriculture is essential for the development of human civilization. Methods that can precisely estimate the yield of a crop or to perform the harvest automatically using robots can decrease the costs involved and increase production efficiency. With the advancement of agriculture 4.0, current technologies like the internet of things, big data, and artificial intelligence have become more and more common. Systems that use image processing with Deep Learning methods are becoming viable in solving agricultural problems. Deep Learning is part of a large family of methods based on artificial neural networks that can mimic the human brain's work in data processing and pattern recognition for decision-making. Indeed, applications of Deep Learning techniques in agriculture are relatively recent. However, with the rapid advance in Deep Learning and its successful application in agriculture, many articles have been published in recent years. Thus, the main objective of this work was to carry out a brief bibliographic review of pre-harvest fruit image processing techniques, emphasizing the most recent applications using Deep Learning. As seen in the literature, Deep Learning is a promising tool for various agricultural activities, including fruit counting and automatic fruit harvesting using robots.

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“…For instance, one of the most common vegetative indices is the normalized difference vegetation index (NDVI), which is derived from red and near-infrared (NIR) spectral bands and is widely used as a representation of biomass, grain yield, and crop N status [ 57 ]. The 2D and 3D structural models can be reconstructed from RGB, multispectral, and thermal imagery to derive important agronomic traits for various crops under different environments such as flowering time of rice [ 58 ] and wheat [ 43 ]; crop biomass of field peas [ 42 ] and wheat [ 41 ]; plant height and biomass of rice [ 59 ] and barley [ 60 ]; seed characteristics of lentils [ 61 ], rice [ 62 ], and field peas [ 63 ]; architectural and physiological properties of apple trees [ 64 ]; height and morphological characteristics of blueberries [ 65 ]; canopy temperature of black poplars [ 66 ]; bunch architecture of grapevines [ 67 ]; and ripeness estimation [ 68 ] and fruit counts [ 69 ] of mangos. Recent advances in computer algorithms and machine learning have significantly improved the throughput of raw data processing and analysis, where the processing pipelines have enabled data capture, analysis, and extraction of multiple patterns and features simultaneously [ 70 ].…”

Section: Phenomics To Unlock the Genetic Potential Of Genebank Germentioning

confidence: 99%

Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm

Nguyen

Norton

2020

Plants

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Genetically diverse plant germplasm stored in ex-situ genebanks are excellent resources for breeding new high yielding and sustainable crop varieties to ensure future food security. Novel alleles have been discovered through routine genebank activities such as seed regeneration and characterization, with subsequent utilization providing significant genetic gains and improvements for the selection of favorable traits, including yield, biotic, and abiotic resistance. Although some genebanks have implemented cost-effective genotyping technologies through advances in DNA technology, the adoption of modern phenotyping is lagging. The introduction of advanced phenotyping technologies in recent decades has provided genebank scientists with time and cost-effective screening tools to obtain valuable phenotypic data for more traits on large germplasm collections during routine activities. The utilization of these phenotyping tools, coupled with high-throughput genotyping, will accelerate the use of genetic resources and fast-track the development of more resilient food crops for the future. In this review, we highlight current digital phenotyping methods that can capture traits during annual seed regeneration to enrich genebank phenotypic datasets. Next, we describe strategies for the collection and use of phenotypic data of specific traits for downstream research using high-throughput phenotyping technology. Finally, we examine the challenges and future perspectives of genebank phenomics.

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Visual detection of green mangoes by an unmanned aerial vehicle in orchards based on a deep learning method

Cited by 68 publications

References 18 publications

Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques

Recent Advancements in Fruit Detection and Classification Using Deep Learning Techniques

Pre-Harvest Fruit Image Processing: A Brief Review

Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm

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