JMI UPIYPTKJMI UPIYPTK

Majalah Ilmiah UPI YPTKMajalah Ilmiah UPI YPTK

Rice plant disease is one of the main factors causing decreased productivity and threatening national food security. Farmers limited knowledge in recognizing early symptoms of disease often leads to delays in treatment. This community service aims to develop and implement computer vision-based technological innovations in education to support early warning systems for rice diseases. The methods used include collecting rice leaf images in the field, digital image processing, and applying computer vision models to recognize visual patterns and detect disease symptoms in rice. The community service activities provided to students at Azrina Private Madrasah SMP Ibtidaiyah are carried out through training and mentoring as well as the introduction of technological innovations for agriculture as an early detection tool. The expected results of this community service are an increase in the understanding and ability of students at Azrina Private Madrasah SMP Ibtidaiyah in understanding how to identify rice diseases more quickly and accurately with computer vision-based technology, so that it can support appropriate decision-making in disease control and increase rice agricultural productivity sustainably. The impact/benefit provided in this service is that students can recognize and understand that computer technology can be useful in the agricultural sector, so that it is not limited to daily needs and styles but can be used in all sectors.

The community service conducted for students at Azrina Private Madrasah SMP Ibtidaiyah demonstrated a highly motivating and enhanced understanding of knowledge in rice disease detection.6% of students demonstrated a very good understanding, 13.3% showed understanding, and none exhibited a poor understanding.This indicates that the students have a good grasp of the material on computer vision innovation for rice diseases and technological developments.

Berdasarkan hasil penelitian ini, terdapat beberapa saran penelitian lanjutan yang dapat dilakukan. Pertama, penelitian lebih lanjut dapat dilakukan untuk mengembangkan sistem deteksi penyakit padi berbasis computer vision yang lebih akurat dan efisien, dengan mempertimbangkan variasi kondisi pencahayaan dan kualitas gambar di lapangan. Kedua, perlu dilakukan studi tentang efektivitas penggunaan sistem ini dalam meningkatkan praktik pertanian berkelanjutan di kalangan petani, termasuk analisis dampak terhadap pengurangan penggunaan pestisida dan peningkatan hasil panen. Ketiga, penelitian dapat difokuskan pada pengembangan modul pelatihan yang lebih interaktif dan mudah dipahami bagi petani dan siswa, dengan memanfaatkan teknologi augmented reality atau virtual reality untuk visualisasi penyakit dan metode pengendaliannya. Penelitian-penelitian ini diharapkan dapat memberikan kontribusi signifikan dalam meningkatkan ketahanan pangan dan kesejahteraan petani di Indonesia.

  1. Plant leaf disease detection and classification using artificial intelligence techniques: a review |... doi.org/10.11591/ijeecs.v38.i2.pp1308-1323Plant leaf disease detection and classification using artificial intelligence techniques a review doi 10 11591 ijeecs v38 i2 pp1308 1323
  2. Implementasi Deep Learning Menggunakan Arsitektur Vgg-19 Untuk Deteksi Penyakit Pada Tebu Berdasarkan... doi.org/10.59188/jcs.v4i5.3155Implementasi Deep Learning Menggunakan Arsitektur Vgg 19 Untuk Deteksi Penyakit Pada Tebu Berdasarkan doi 10 59188 jcs v4i5 3155
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