Detection and Counting of On-Tree Citrus Fruit for Crop Yield Estimation

Malik, Zainab; Ziauddin, Sheikh; Raza, Basit; Safi, Ali-Farid

doi:10.14569/ijacsa.2016.070569

Cited by 26 publications

(13 citation statements)

References 17 publications

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“…These features are represented in vectors that undergo segmentation techniques based on lesions [ 65 ], Otsu [ 66 ], ROIs [ 67 ] or edges [ 68 ] to separate ROIs from the background. This is followed by a fusion process to combine the feature vectors in a final vector.…”

Section: Feature Representation In Shallow Classifiersmentioning

confidence: 99%

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Hasan

Yusuf

Alzubaidi

2020

Plants

151

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Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.

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Section: Feature Representation In Shallow Classifiersmentioning

confidence: 99%

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Hasan

Yusuf

Alzubaidi

2020

Plants

151

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“…Although the described system is very practical, the performance of the fruit counting algorithm remains limited. Another orange fruit counting algorithm was proposed in [ 20 ]. The algorithm proceeds by a shadow removal via converting the acquired image into a LAB space.…”

Section: Introductionmentioning

confidence: 99%

Vision System for Automatic On-Tree Kiwifruit Counting and Yield Estimation

Mekhalfi¹,

Nicolò²,

Ianniello³

et al. 2020

Sensors

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Yield estimation is an essential preharvest practice among most large-scale farming companies, since it enables the predetermination of essential logistics to be allocated (i.e., transportation means, supplies, labor force, among others). An overestimation may thus incur further costs, whereas an underestimation entails potential crop waste. More interestingly, an accurate yield estimation enables stakeholders to better place themselves in the market. Yet, computer-aided precision farming is set to play a pivotal role in this respect. Kiwifruit represents a major produce in several countries (e.g., Italy, China, New and Zealand). However, up to date, the relevant literature remains short of a complete as well as automatic system for kiwifruit yield estimation. In this paper, we present a fully automatic and noninvasive computer vision system for kiwifruit yield estimation across a given orchard. It consists mainly of an optical sensor mounted on a minitractor that surveys the orchard of interest at a low pace. Afterwards, the acquired images are fed to a pipeline that incorporates image preprocessing, stitching, and fruit counting stages and outputs an estimated fruit count and yield estimation. Experimental results conducted on two large kiwifruit orchards confirm a high plausibility (i.e., errors of 6% and 15%) of the proposed system. The proposed yield estimation solution has been in commercial use for about 2 years. With respect to the traditional manual yield estimation carried out by kiwifruit companies, it was demonstrated to save a significant amount of time and cut down on estimation errors, especially when speaking of large-scale farming.

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“…From the domain of image processing and machine learning (ML) techniques, various methods for detecting and classifying citrus disease are presented. [1] introduced an automatic system to detect, segment and measure citrus fruits. This technique comprises of preprocessing, shadow reduction, separating the object, K-means clustering, and blob detection stages.…”

Section: Introductionmentioning

confidence: 99%

An Effective Classification of Citrus Fruits Diseases using Adaptive Gamma Correction with Deep Learning Model

Senthilkumar,

Kamarasan

2019

IJEAT

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In farming sector, diseases affected in plants are mainly accountable for the minimized profit that leads to financial loss. In case of plants, citrus is utilized as a main resource of nutrients namely vitamin C globally. But citrus diseases greatly affect the productivity as well as quality. In recent days, computer vision and image processing approaches are commonly applied for detecting and classifying the plant diseases. This paper presents a novel deep learning (DL) based citrus disease detection and classification model. A new DL based AlexNet architecture is employed for effective identification of diseases. The presented model involves four main processes namely pre-processing, segmentation, feature extraction, and classification. Initially, pre-processing takes place to improve the quality of the image. Then, the Otsu method is applied to segment the images. Next, Alex-Net model is applied as a feature extractor. Finally, random forest (RF) classifier is used to classify the different kinds of citrus diseases. Besides, adaptive gamma correction (AGC) model is applied to improve the contrast of the applied citrus images. A comprehensive experimentation takes place on Citrus Disease Image Gallery Dataset. The results are examined under several cases and the outcome ensured the effective characteristics of the presented AGC-A model

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Detection and Counting of On-Tree Citrus Fruit for Crop Yield Estimation

Cited by 26 publications

References 17 publications

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Vision System for Automatic On-Tree Kiwifruit Counting and Yield Estimation

An Effective Classification of Citrus Fruits Diseases using Adaptive Gamma Correction with Deep Learning Model

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