The Academic Resources Recommender System (ARReS) includes collection of Theses and Dissertations available in the Camarines Sur Polytechnic Colleges Library.
Harvesting the rice plant too early resulted in a higher percentage of unfilled or immature grains, lowering production and increasing grain breakage during milling. However, harvesting rice too late resulted in severe losses and breakage. This problem pushed the researchers to create a model that helped with the harvest period prediction. Further, the objectives were to build an image classification model implementing the CNN algorithm, to determine the effectiveness of the implemented CNN algorithm using a confusion matrix and to develop a prototype that applies the model involving a confusion matrix. Quantitative research method was used for this study for it required data collection wherein researchers gathered images as their primary source in building dataset and added some images from the internet as their secondary source then directly proceed to data labeling. Afterwards, model training was done where researchers used VGG-16 to train the model and evaluated later on using Confusion Matrix in determining its accuracy. Finally, results and findings were presented. The study output predicted the palay harvest time with the use of the gathered dataset. The prototype predicted the rice plant harvesting time through uploaded rice plant images. CNN algorithm and Python language was highly effective in prediction of palay harvest time. Upon model evaluation, using confusion matrix, it reached 95% accuracy. Recommendations for future researchers were enhancing the existing dataset and improving its images’ quality. Also, different rice variety and output such as rice plant’s current phase might be provided instead.
Janet B. Sasaluya; Caren F. Vega; Crystallene R. Lagatic; Louis Carlo SF. Fornoles
2022 Computer ScienceHarvesting the rice plant too early resulted in a higher percentage of unfilled or immature grains, lowering production and increasing grain breakage during milling. However, harvesting rice too late resulted in severe losses and breakage. This problem pushed the researchers to create a model that h...
Jonalyn L. Bugnot; Kim Clarice Garcia; Jude Romar B. Cerdeño; Maria Jessica T. Sayin
2022 Computer ScienceClassification of waste is one common problem in waste management, waste can bring a huge impact in everyday living causing pollution and some other infectious diseases. The traditional way of sorting waste manually takes much time and brings negative effect as it is already contaminated. Generally,...
Hamadie Seed D. Alswaidi; Anthony B. Amilano; Felix Jose I. Awa; Marian Mildred D. Bedural
2022 Computer ScienceCoconut diseases cause production problem. In connection with this, the study aimed to gather and collect datasets of healthy coconut leaves and with coconut diseases particularly Graphiola Leaf Spot, Stigmina Leaf Spot, and Whiteflies; create a CNN model for detecting the specified coconut diseases...
Namirah L. Balindong; Arnold S. Sayat Jr.; Mat Andrew V. Tantiado; Rose Ann B. Sabilla; Mary Rose T. Lazaro
2022 Computer ScienceThe convolutional neural network is one of the most promising applications in computer vision. Deep learning-based classification has recently enabled the recognition of chickens from images. However, no research has been done on using the Convolutional Neural Network (CNN) Algorithm in image proces...