Apple fruit detection and counting using computer vision techniques

Syal, Anisha; Garg, Divya; Sharma, Shanu

doi:10.1109/iccic.2014.7238364

Cited by 16 publications

(10 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Companies generally use manual calculation methods to estimate harvest yields with the help of manual labors. In general, large companies cost around $600,000 to $1,000,000 for manual calculations per year [5]. In addition to manual calculation costs, many other evaluation costs such as overhead costs that include insurance and total equipment repair costs.…”

Section: Harvest System Automation Needsmentioning

confidence: 99%

Smartphone for palm oil fruit counting to reduce embezzlement in harvesting season

Aripriharta¹,

Firmansah²,

Mufti³

et al. 2020

Bull.Socinf.The.App

View full text Add to dashboard Cite

Harvest estimation is an essential parameter in the agriculture industries to estimate transportation facilities and storage areas in the harvesting season. Meanwhile, companies are required to calculate crop yields quickly and accurately. This paper reports on an experimental study in the form of a smart application to count oil palm fruit in the field quickly and accurately. The system used a single shot detector algorithm to count the number of fresh fruit bunches (FFB) on-site using a smartphone camera. The cutting area (CA) at the top of the collection was collected in various positions in the database. Our research documented that the algorithm matched the CA with the picture taken by the operator. Hence, the application automatically calculated the number of harvests per-site in the FFB unit. The data were then sent to the cloud database via a wireless router in a warehouse or through a cellular network. The main advantage of this application is reducing the theft that usually occurs on the spot. The model used performs very well for agricultural applications, with 94% to 99% accuracy.

show abstract

Section: Harvest System Automation Needsmentioning

confidence: 99%

Smartphone for palm oil fruit counting to reduce embezzlement in harvesting season

Aripriharta¹,

Firmansah²,

Mufti³

et al. 2020

Bull.Socinf.The.App

View full text Add to dashboard Cite

show abstract

“…Syal et al [12] utilized computer vision approach to make an effective and automated counting system. The fruit regions are segmented from the input image by using the segmentation approach based on minimum Euclidean distance.…”

Section: Literature Reviewmentioning

confidence: 99%

Block Level Optimization Based Mapping and Ensemble Classification Based Segmentation for Fruit Flower Detection

Samraj*

2019

IJEAT

View full text Add to dashboard Cite

In the field of agricultural, the crop estimation task entirely depends on the process of detecting and counting the number of fruits on trees. The crop estimation task is mainly used in the agricultural field. Currently, counting the fruits and vegetables manually is performed at several locations. Manual counting has several disadvantages since it consumes too much time and needs an excessive amount of labor. The agriculture productivity can be improved by incorporating the automatic fruit counting technique with a crop management system to gather the information to predict the yield. This information may be used to schedule the harvesting. This research approach creates maps about flowers at the block level. This research work employed the Modified Bat Optimization technique for the generation of a direct map from blocks with no computationally intensive refinement phase. Once the block-level mapping is done, then the ensemble classification is introduced for flower identification, which is reliable against uncertain environments and suitable for various flower species. The proposed technique depends on the Support Vector Machine (SVM) classifier and an Enhanced Convolutional Neural Network (ECNN) for carrying out semantic segmentation. To improve its sensibility towards flowers, this Ensemble Classification Framework (ECF) is fine-tuned with the help of a weighted majority function of apple flower images. Also, a refinement technique is used for all the classifiers to better differentiate between different flower instances. The dataset with pixel-accurate labeling is used for the implementation of the proposed technique. The dataset has images with high resolution.

show abstract

“…Speed and efficiency of a system can be further improved by using ARM9 or ARM11 processor for the same purpose. Another paper [4] about fruit detection and counting gives a major idea to generate samples of scanned image. This process focuses on object counting on field.…”

Section: Review Of Literaturementioning

confidence: 99%

“…Once known the axis point and the centre point, a line through the centre point which is vertical to the line from axis point to centre point will be crossed with the edge sequences, two edge points that on the line will be searched. Suppose the two points is (x1, y1) and (x2, y2) in order to improving the system's speed, the diameter is calculated by (4) indicates the fruit's real maximal diameter in image. This method can find the axis point accurately in a fruit image.…”

Section: ) Finding the Fruit's Axis In Imagementioning

confidence: 99%