The Academic Resources Recommender System (ARReS) includes collection of Theses and Dissertations available in the Camarines Sur Polytechnic Colleges Library.
The research, “Rice Paddy Moisture Content Classification using Convolutional Neural Network,” was created to assist rice farmers and dealers in classifying rice paddy to determine moisture content. Also, the researcher used an experimental research design to perform research in an objective and controlled manner in training the model. The researchers collected 1000 palay image datasets and developed a model to determine the moisture content of the palay. Newly harvested palay that haven’t exposed to the sun classified as High. On the other hand, palay exposed to sun for two to five hours classified as High Medium. The moisture content that is milled to produce rice is Medium Low, whereas the moisture palay level that can stock is Low. the training with an image size of 144x144 results less training time and approximately 80%accuracy rate. Trying other image sizes with the same epoch batch size makes significant changes in training time and accuracy, a higher image size of 256x256 takes a lot of computing power and longer training time, it also makes the accuracy high but sometimes it results in low accuracy. It also applied when training low image size but less training time, computing power, and resources. Hence, it concluded that the Convolutional Neural Network algorithm can take any images of different image sizes as long as the computing power and resources are available. In this case, the researcher used this algorithm to classify the image of the palay into four classifications to determine its moisture content.
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,...
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...
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...