A Century of Practice and Experiences of the Restoration of Land-Use Types and Ecosystems

Zerbe, Stefan

doi:10.1007/978-3-030-95572-4_9

Cited by 2 publications

(2 citation statements)

References 342 publications

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“…The more traditional cultivars remain as part of historic orchards or forgotten woody meadows. However, their value is still high since the traditional cultivars provide genetic variability and are typically well adapted to local climate and other environmental conditions [1][2][3]. In addition, they are robust against several diseases.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Apple Cultivar Classification Using Multiview Images

Krug,

Hutschenreuther

2024

J. Imaging

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Apple cultivar classification is challenging due to the inter-class similarity and high intra-class variations. Human experts do not rely on single-view features but rather study each viewpoint of the apple to identify a cultivar, paying close attention to various details. Following our previous work, we try to establish a similar multiview approach for machine-learning (ML)-based apple classification in this paper. In our previous work, we studied apple classification using one single view. While these results were promising, it also became clear that one view alone might not contain enough information in the case of many classes or cultivars. Therefore, exploring multiview classification for this task is the next logical step. Multiview classification is nothing new, and we use state-of-the-art approaches as a base. Our goal is to find the best approach for the specific apple classification task and study what is achievable with the given methods towards our future goal of applying this on a mobile device without the need for internet connectivity. In this study, we compare an ensemble model with two cases where we use single networks: one without view specialization trained on all available images without view assignment and one where we combine the separate views into a single image of one specific instance. The two latter options reflect dataset organization and preprocessing to allow the use of smaller models in terms of stored weights and number of operations than an ensemble model. We compare the different approaches based on our custom apple cultivar dataset. The results show that the state-of-the-art ensemble provides the best result. However, using images with combined views shows a decrease in accuracy by 3% while requiring only 60% of the memory for weights. Thus, simpler approaches with enhanced preprocessing can open a trade-off for classification tasks on mobile devices.

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Section: Introductionmentioning

confidence: 99%

Enhancing Apple Cultivar Classification Using Multiview Images

Krug,

Hutschenreuther

2024

J. Imaging

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“…However, most of the traditional old cultivars are grown in traditional orchards with large trees. These habitats are very common all over Europe and are a vital element in biodiversity preservation [6,7]. Often, these orchards lose their purpose when fruit production shifts to specialized farming.…”

Section: Introductionmentioning

confidence: 99%

A Case Study toward Apple Cultivar Classification Using Deep Learning

Krug

Hutschenreuther

2023

AgriEngineering

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Machine Learning (ML) has enabled many image-based object detection and recognition-based solutions in various fields and is the state-of-the-art method for these tasks currently. Therefore, it is of interest to apply this technique to different questions. In this paper, we explore whether it is possible to classify apple cultivars based on fruits using ML methods and images of the apple in question. The goal is to develop a tool that is able to classify the cultivar based on images that could be used in the field. This helps to draw attention to the variety and diversity in fruit growing and to contribute to its preservation. Classifying apple cultivars is a certain challenge in itself, as all apples are similar, while the variety within one class can be high. At the same time, there are potentially thousands of cultivars indicating that the task becomes more challenging when more cultivars are added to the dataset. Therefore, the first question is whether a ML approach can extract enough information to correctly classify the apples. In this paper, we focus on the technical requirements and prerequisites to verify whether ML approaches are able to fulfill this task with a limited number of cultivars as proof of concept. We apply transfer learning on popular image processing convolutional neural networks (CNNs) by retraining them on a custom apple dataset. Afterward, we analyze the classification results as well as possible problems. Our results show that apple cultivars can be classified correctly, but the system design requires some extra considerations.

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A Century of Practice and Experiences of the Restoration of Land-Use Types and Ecosystems

Cited by 2 publications

References 342 publications

Enhancing Apple Cultivar Classification Using Multiview Images

Enhancing Apple Cultivar Classification Using Multiview Images

A Case Study toward Apple Cultivar Classification Using Deep Learning

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