No abstract

No abstract

As the population increases, rice consumption will also increase. Therefore, the productivity of rice must be increased. However, the productivity of rice usually declines due to the brown planthopper. This study aims to create an image processing algorithm on a drone so that it can detect the color of rice leaves that are attacked by brown planthoppers and to create a mechanism for spraying insecticides. The methodology in this research begins with the design of mechanical and electronic systems on drones and spraying mechanisms, to the design of color detection programs and spraying mechanism programs. The testing of the spraying mechanism is carried out at various drone flight heights, ranging from ±1 meter to ±1.5 meters. The results of this study showed that the spraying mechanism was able to spray liquid and the image processing algorithm that had been made was able to detect brown rice leaf color and was able to spray liquid automatically when the color of the specified object was detected.

Penelitian ini bertujuan membahas tentang diagnosis kesalahan pembacaan sensor temperatur tempat penyimpanan gandum, menggunakan metode teknik analisis struktural. Analisis struktural berbasis data digunakan untuk menganalisis kondisi dari sistem. Perbandingan kinerja dan kecepatan pembacaan dari sensor juga dibahas. Metode yang digunakan pada penelitian ini yaitu analisis redudansi dan perbandingan data dari sistem sensor. Data pembacaan sensor utama dibandingkan dengan data yang telah disimpan pada sistem. Ketika data dari sensor memiliki kemiripan dengan data yang berada pada sistem, maka sensor dianggap normal. Akan tetapi jika data tersebut tidak sesuai, data sensor backup akan menjadi pembanding selanjutnya. Apabila data dinyatakan tidak memiliki kemiripan, maka sensor dianggap gagal. Dari beberapa percobaan dihasilkan perbandingan kecepatan respon perubahan temperatur dari sensor. Kecepatan pembacaan perubahan temperatur dari sensor texas instruments (LM35) lebih baik dibandingkan sensor dari maxim integrated (DS18B20). Akan tetapi akurasinya berbanding terbalik. Untuk kecepatan pendeteksian kesalahan dan penggantian sensor, piranti dari texas instruments lebih baik dari maxim integrated. Untuk kecepatan pembacaan kegagalan sensor DS18B20 lebih sensitif terhadap debu / parasit dengan kecepatan 87,8 ms, sedangkan untuk sensor LM35 lebih baik yaitu 77,5 ms. Untuk kondisi penggantian sensor ke sensor backup pada penggunaan sensor LM35 dan DS18B20 memiliki kecepatan yang sama, waktu yang tercepat untuk kedua sensor ini sebesar 14,1 ms

Drying grain requires a large area with heavy work because farmers have to turn the grain that lies on the field every hour and requires a lot of energy because it is done under the hot sun. Drying of grain by farmers involves still checking the level of dryness manually using only the human senses as a measuring tool. One of the drawbacks is that manual checking process is not accurate enough and will affect rice production. So we need an innovation in existing technology to measure the level of drought.This robot is designed in the form of a prototype where the grain mixer system is in the middle. The movement of the robot is only forward and backward which will be controlled using a smartphone by the operator and the width of the grain area to be stirred is 50 cm or along the stirrer. Temperature and humidity sensors will be installed at the bottom which will make it easier to measure the temperature and humidity of the stirred grain making it easier for farmers to monitor the level of grain dryness.