Application of an inductive sensor system for identifying ripeness and forecasting harvest time of oil palm

Sinambela, Rismen; Mandang, Tineke; Subrata, I Dewa Made; Hermawan, Wawan

doi:10.1016/j.scienta.2020.109231

Cited by 30 publications

(12 citation statements)

References 19 publications

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“…Decisions on appropriate import and export can be made on the basis of reliable forecasts [57]. In order to implement an appropriate harvesting strategy, oil palm harvest time is predicted based on fruit growth and ripeness level with regression analysis [58]. An oil palm prediction model to estimate production from cultivated area images and tree age estimation is proposed in [59].…”

Section: Prediction/estimationmentioning

confidence: 99%

Oil Palm and Machine Learning: Reviewing One Decade of Ideas, Innovations, Applications, and Gaps

et al. 2021

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Machine learning (ML) offers new technologies in the precision agriculture domain with its intelligent algorithms and strong computation. Oil palm is one of the rich crops that is also emerging with modern technologies to meet global sustainability standards. This article presents a comprehensive review of research dedicated to the application of ML in the oil palm agricultural industry over the last decade (2011–2020). A systematic review was structured to answer seven predefined research questions by analysing 61 papers after applying exclusion criteria. The works analysed were categorized into two main groups: (1) regression analysis used to predict fruit yield, harvest time, oil yield, and seasonal impacts and (2) classification techniques to classify trees, fruit, disease levels, canopy, and land. Based on defined research questions, investigation of the reviewed literature included yearly distribution and geographical distribution of articles, highly adopted algorithms, input data, used features, and model performance evaluation criteria. Detailed quantitative–qualitative investigations have revealed that ML is still underutilised for predictive analysis of oil palm. However, smart systems integrated with machine vision and artificial intelligence are evolving to reform oil palm agri-business. This article offers an opportunity to understand the significance of ML in the oil palm agricultural industry and provides a roadmap for future research in this domain.

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Section: Prediction/estimationmentioning

confidence: 99%

Oil Palm and Machine Learning: Reviewing One Decade of Ideas, Innovations, Applications, and Gaps

et al. 2021

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“…Those methods could potentially be suited for detection applications in the mill after harvesting as a mechanism for quality control. From the literature, it is found that the methods featuring colour images [21], LiDAR [22], and inductive sensor [23] are being developed into the application phase currently.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection of Ripe Oil Palm Fresh Fruit Bunch Based on YOLOv4

et al. 2022

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Fresh Fruit Bunch (FFB) is the main ingredient in palm oil production. Harvesting FFB from oil palm trees at its peak ripeness stage is crucial to maximise the oil extraction rate (OER) and quality. In current harvesting practices, misclassification of FFB ripeness can occur due to human error, resulting in OER loss. Therefore, a vision-based ripe FFB detection system is proposed as the first step in a robotic FFB harvesting system. In this work, live camera input is fed into a Convolutional Neural Network (CNN) model known as YOLOv4 to detect the presence of ripe FFBs on the oil palm trees in real time. Once a ripe FFB is detected on the tree, a signal is transmitted via ROS to the robotic harvesting mechanism. To train the YOLOv4 model, a large number of ripe FFB images were collected using an Intel Realsense Camera D435 with the resolution of 1920×1080. During data acquisition, a subject matter expert assisted in classifying the FFBs in terms of ripe or unripe. During the testing phase, the result of the mean Average Precision (mAP) and recall are 87.9 % and 82 % as the detection is fulfilled the Intersect over Union (IoU) with more than 0.5 after 2000 iterations and the system operated at the real-time speed of roughly 21 Frame Per Second (FPS).

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“…Recently, image processing-based technology has been widely used in numerous disciplines such as medicine, industry, geology, marine science, and agriculture. The method has been developed for a variety of agricultural applications, including the classification of mango [1], apples [2], chili [3], and automated fruit harvesting systems [4].…”

Section: Introductionmentioning

confidence: 99%

Chili Classification Using Shape and Color Features Based on Image Processing

Sihombing

Septiarini

Kridalaksana

et al. 2022

SJI

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Abstract. Purpose: Chili is an agricultural product that has several varieties and is in great demand. It can be consumed directly or processed first. This study aims to classify the types of chili using color and shape features. The types of chili are divided into five classes: cayenne pepper, green chili, big green chili, big red chili, and curly chili. The chili classification method was evaluated using three parameters: precision, recall, and accuracy.Methods: This study applied the K-Nearest Neighbors (KNN) method with the Euclidean and Manhattan distance calculation algorithm and used two feature types: color and shape. The color features were extracted based on RGB color space by obtaining the mean and standard deviation values. Meanwhile, the shape features used aspect ratio, area, and boundary.Result: The evaluation results of the classification method were able to achieve the precision, recall, and accuracy values of 1.0, which means that all test data were classified correctly. The evaluation was applied with 210 training images and 90 test images based on the test results.Novelty: This study extracted two types of features: color and shape. Those features fed the KNN method by applying the Euclidean and Manhattan distance calculation algorithm; hence, the optimal results were achieved.

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Application of an inductive sensor system for identifying ripeness and forecasting harvest time of oil palm

Cited by 30 publications

References 19 publications

Oil Palm and Machine Learning: Reviewing One Decade of Ideas, Innovations, Applications, and Gaps

Oil Palm and Machine Learning: Reviewing One Decade of Ideas, Innovations, Applications, and Gaps

Real-Time Detection of Ripe Oil Palm Fresh Fruit Bunch Based on YOLOv4

Chili Classification Using Shape and Color Features Based on Image Processing

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