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Advanced Classification of Oil Palm Fruit Ripeness Deep Learning for Enhanced Agricultural Efficiency
Accurate determination of oil palm fruit ripeness is crucial for optimizing oil yield and enhancing agricultural efficiency. This study utilizes deep learning, specifically Convolutional Neural Networks (CNNs), to classify oil palm fruit ripeness into four stages: raw, under-ripe, ripe, and overripe. The model leverages extensive data preprocessing and augmentation techniques to handle variations in lighting, angles, and fruit orientation, ensuring high classification accuracy. The approach addresses limitations of traditional methods, such as human error and inconsistencies, by providing an automated and reliable solution for real-time ripeness detection. Results demonstrate an overall accuracy of 97%, with robust precision, recall, and F1 scores across all categories. This study highlights the importance of diverse datasets and proposes further integration of contextual factors like environmental conditions to enhance applicability. The system offers a practical tool for precision agriculture, improving harvesting efficiency, reducing waste, and supporting sustainable practices
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advanced classification of oil palm fruit ripeness deep learning for enhanced agricultural efficiency accurate determination of oil palm fruit ripeness is crucial for optimizing oil yield and enhancing agricultural efficiency this study utilizes deep learning specifically convolutional neural networks cnns to classify oil palm fruit ripeness into four stages raw under ripe ripe and overripe the model leverages extensive data preprocessing and augmentation techniques to handle variations in lighting angles and fruit orientation ensuring high classification accuracy the approach addresses limitations of traditional methods such as human error and inconsistencies by providing an automated and reliable solution for real time ripeness detection results demonstrate an overall accuracy of 97 with robust precision recall and f1 scores across all categories this study highlights the importance of diverse datasets and proposes further integration of contextual factors like environmental conditions to enhance applicability the system offers a practical tool for precision agriculture improving harvesting efficiency reducing waste and supporting sustainable practices
Jaro Winkler
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|---|---|---|---|
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| 2. vvv | 114 words | 0.53 | advanced : 0.49, classification : 0, of : 0, oil : 0, palm : 0, fruit : 0, ripeness : 0, deep : 0, learning : 0, for : 0, enhanced : 0, agricultural : 0, efficiency : 0, accurate : 0, determination : 0, is : 0, crucial : 0, optimizing : 0, yield : 0, and : 0, enhancing : 0, this : 0, study : 0, utilizes : 0, specifically : 0, convolutional : 0.47, neural : 0, networks : 0, cnns : 0, to : 0, classify : 0, into : 0, four : 0, stages : 0, raw : 0, under : 0, ripe : 0, overripe : 0.49, the : 0, model : 0, leverages : 0.48, extensive : 0, data : 0, preprocessing : 0, augmentation : 0, techniques : 0, handle : 0, variations : 0.53, in : 0, lighting : 0, angles : 0, orientation : 0, ensuring : 0, high : 0, accuracy : 0, approach : 0, addresses : 0, limitations : 0, traditional : 0, methods : 0, such : 0, as : 0, human : 0, error : 0, inconsistencies : 0, by : 0, providing : 0.48, an : 0, automated : 0, reliable : 0, solution : 0, real : 0, time : 0, detection : 0, results : 0, demonstrate : 0, overall : 0.49, 97 : 0, with : 0, robust : 0, precision : 0, recall : 0, f1 : 0, scores : 0, across : 0, all : 0, categories : 0, highlights : 0, importance : 0, diverse : 0.49, datasets : 0, proposes : 0, further : 0, integration : 0, contextual : 0, factors : 0, like : 0, environmental : 0.47, conditions : 0, enhance : 0, applicability : 0, system : 0, offers : 0, a : 0, practical : 0, tool : 0, agriculture : 0, improving : 0.48, harvesting : 0.48, reducing : 0, waste : 0, supporting : 0, sustainable : 0, practices : 0 |
Average Result
66.5%
0.665