Smart Dairy Cattle Farming and In-Heat Detection through the Internet of Things (IoT)

The Internet of Things (IoT) technology has been being revolutionized in various aspects of agriculture around the world ever since. Its application has already found its success in some countries. On the contrary, this technology has yet to find its substantial breakthrough in the Philippines. This study shows the application of IoT in improving the detection efficiency of standing-heat behaviors of cows through automated detection using Pan-tilt-zoom cameras and a Python-driven Web Application. The dimensions of the barn were measured, and the Cameras' Field of Views (FOVs) were pre-calculated for the strategic positions of the cameras atop of the cowshed. The program detects the cows and any estrus events through the surveillance cameras. The results will be sent to the cloud server to display on the web application for analysis. The web app can allow updates on cow information, inseminations, pregnancy, and calving records, estimate travel time from the user's geolocation to the farm, provide live monitoring and remote camera accessibility and control through the cameras and deliver reliable cross-platform push-notification and call alerts on the user's device(s) whenever an estrus event is detected. Based on the results, the program performed satisfactorily at 50% detection efficiency

Rice plant disease identification and detection technology through classification of microorganisms using fuzzy neural network

This paper describes a method of using sound signal processing system to efficiently detect and identify the three common microorganisms that cause diseases in the rice farmland of the Philippines: (1) Xanthomonas oryzae, (2) Thanatephorus cucumeris and (3) Magnaporthe oryzae. Sound signals from samples of rice leaves infected by the above mentioned bacteria were recorded using a designed anechoic chamber through an electret condenser microphone and were processed via spectral subtraction to eliminate the effects of noise. Mel Frequency Cepstral Coefficient was used to extract the needed features of each input for the ANFIS learning algorithm. The Fuzzy neural network was applied to train the system based on 450 recorded sound data where 80% were used for training and 20% for testing. A program was also developed that will generate a report in PDF format showing the diagnosis and curing methods for the infected sample to prevent its further infestation. Test results showed recognition accuracy of the bacteria, Xanthomonas oryzae, Magnaporthe oryzae, and Thanatephorus cucumeris, of 93.33%, 100% and 96.67% repectively. © 2016 Penerbit UTM Press. All rights reserved

Coupling school risk reduction strategies with LAMESA (life-saving automated “mesa” to endure seismic activity) for kindergarten

The study linked school risk reduction and disaster preparedness strategy using a designed automated study desk for kindergarten. This desk, LAMESA (Life-saving Automated “Mesa” to Endure Seismic Activity), aimed to provide the education system with a resilient study desk for kindergarten. Design and development research used lightweight but highly strong and elastic materials to build the automated desk conforming to the kindergarten standards. The system and program designs ensured good peak ground acceleration (PGA) and a fix response time (4 sec.) to effectively and efficiently facilitate “duck (drop), cover, hold” actions of kindergartens to shield them from debris in the eventuality of a strong seismic activity. Purposively chosen experts (engineers, scientists, and programmers) and stakeholders (kindergarten teachers, the laboratory school principal, parents, and district supervisor) evaluated the automated desk as excellent in features, design, and visual; as a warning system when earthquakes occur; as safety infrastructure for students; and as a learning tool. For holistic packaging, the desk may undergo strength test and is also recommended to include ad materials and training kits.© 2019, Department of Science and Technology. All rights reserved

SoilMATe: Soil macronutrients and pH level assessment for rice plant through digital image processing using artificial neural network

In this study, digital image processing technique was used to efficiently identify the Macronutrients and pH level of Soil in the farmland of Philippines: (1) Nitrogen, (2) Phosphorus, (3) Potassium and (4) pH. The composition of the system is made of four sections namely, image acquisition, image processing, training system, and result. The Artificial neural network was applied in this study for its features that make it well suited in offering fast and accurate performance for the image processing. The system will base on 448 captured image data, 70% for training, 15% for testing and 15% for validation. Based on the result, the program will generate a report in printed form. Overall, this study identifies the soil macronutrient and pH level of the soil and gives fertilizer recommendation for inbred rice plant and was proven 98.33% accurate

Smart Dairy Cattle Farming and In-Heat Detection through the Internet of Things (IoT)

The Internet of Things (IoT) technology has been being revolutionized in various aspects of agriculture around the world ever since. Its application has already found its success in some countries. On the contrary, this technology has yet to find its substantial breakthrough in the Philippines. This study shows the application of IoT in improving the detection efficiency of standing-heat behaviors of cows through automated detection using Pan-tilt-zoom cameras and a Python-driven Web Application. The dimensions of the barn were measured, and the Cameras' Field of Views (FOVs) were pre-calculated for the strategic positions of the cameras atop of the cowshed. The program detects the cows and any estrus events through the surveillance cameras. The results will be sent to the cloud server to display on the web application for analysis. The web app can allow updates on cow information, inseminations, pregnancy, and calving records, estimate travel time from the user's geolocation to the farm, provide live monitoring and remote camera accessibility and control through the cameras and deliver reliable cross-platform push-notification and call alerts on the user's device(s) whenever an estrus event is detected. Based on the results, the program performed satisfactorily at 50% detection efficiency