Design and Development of Eggplant Fruit and Shoot Borer (Leucinodes Orbonalis) Detector Using Near-Infrared Spectroscopy


  • Maria Patrice Lajom Department of Computer, Electronics and Electrical Engineering, Cavite State University – Don Severino Delas Alas Campus, Indang, Cavite, Philippines Author
  • Joseph Paul Remigio Department of Computer, Electronics and Electrical Engineering, Cavite State University – Don Severino Delas Alas Campus, Indang, Cavite, Philippines Author
  • Edwin Arboleda Department of Computer, Electronics and Electrical Engineering, Cavite State University – Don Severino Delas Alas Campus, Indang, Cavite, Philippines Author
  • Rhen John Rey Sacala Department of Computer, Electronics and Electrical Engineering, Cavite State University – Don Severino Delas Alas Campus, Indang, Cavite, Philippines Author



Insect Infestation, Spectroscopy, Support Vector Machine, Machine learning


An Eggplant Fruit and Shoot Borer (EFSB) is a disease that affects the entirety of the eggplant fruit if not detected. Hence, a detector was proposed in the form of a handheld gun. It was designed and developed to non-invasively classify eggplant fruits that are non-infested and infested with EFSB. Using an Arduino Nano as its microcontroller and a near-infrared spectroscopy (NIRS) module, insect infestation is determined and displayed through its OLED display. Measured reflectance data through the NIRS module of the detector is then stored inside a MicroSD module for further use. Since the prototype was developed for online monitoring, portability was given of utmost importance, pattering the design in the form of a handheld gun, inside of which was powered by a 9V rechargeable battery. The 3D-printed chassis of the detector houses the aforementioned components and modules, alongside with switches for power and near-infrared detection. Through Support Vector Machine (SVM), the classifier model was trained and developed using Jupyter and was extracted as a C++ code for the Arduino Nano module. Compared with a farmer's traditional performance in terms of accuracy, precision, and speed, the prototype performed better with an accuracy of 84%, precision of 72.83%, and an average speed of 9.736 seconds.


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Design and Development of Eggplant Fruit and Shoot Borer (Leucinodes Orbonalis) Detector Using Near-Infrared Spectroscopy. (2024). Journal of Engineering and Sustainable Development, 28(4), 439-454.

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