Abstract
Determining the harvesting period can be done visually, physically, computationally, and chemically. Since the harvesting process is crucial, late harvesting will affect post-harvest and production quality. Leafy vegetables have a relatively short ready-to-harvest period. Visual recognition of the harvesting period combined with image processing can recognize harvesting vegetables' visual characteristics. This study aims to build a deep learning-based mobile model to detect real-time vegetable plant objects such as bok choy, spinach, kale, and curly kale to determine whether these vegetables are ready for harvest. Mobile-based architecture is chosen due to latency, privacy, connectivity, and power consumption reason since there is no round-trip communication to the server. In this research, we use MobileNetV3 as the base architecture. To find the best model, we experiment using different image input size. We have obtained a maximum MAP score of 0. 705510 using a 36,000 image dataset. Furthermore, after implementing the model into the Android mobile application, we analyze the best practice in using the application to capture distance. In real-time detection usage, the detection can be done with an ideal distance of 5 cm and 10 cm.
Highlights
Determining the harvesting period can be done visually, physically, computationally, and chemically
Dengan mengadopsi menggunakn 16 dengan jumlah data 36.000 gambar Tensorflow lite, file hasil generate tersebut dimasukkan maka algoritma ini akan menggunakan 16 sempel data atau sebagai pengganti file bawaan dari tensorflow lite. pertama dari 36.000 data yang dimiliki kemudian Pengembangan dari TensorFlow Lite sesuai dengan disebarkankan atau dilatih oleh neural network sampai kebutuhan penelitian ini seperti realtime detection, selesai kemudian mengambil kembali 16 sampel data import from gallery, dan captured
[18] Qimei Wang, Feng Qi, Minghe Sun, Jianhua Qu, and Jie Xue, [25] Maulana, F., and Rochmawati, N., "Klasifikasi Citra Buah "Identification of Tomato Disease Types and Detection of Menggunakan Convolutional Neural Network", JINACS, vol
Summary
Panen dan pasca panen merupakan proses yang tidak dapat dipisahkan dalam pertanian. Panen merupakan rangkaian kegiatan pengambilan hasil budidaya berdasarkan umur dan waktu, dengan cara yang sesuai sifat dan/atau karakter produk. Alur dalam metode penelitian realtime object warna RGB menjadi HIS sehingga hasil penelitian detection masa siap panen tanaman sayuran seperti dengan pengujian 20 sampel buah dimana 10 buah Gambar 1. Sehingga dapat disimpulkan Pengumpalan data gambar adalah gambar tanaman bahwa metode CNN yang dirancang pada penelitian ini sayuran dengan resolusi 2304 x 4608 dan ekstensi jpg dapat mengklasifikasi citra dengan baik [25]. Lebih jauh lagi pada bidang pertanian dengan Dari hasil prepocessing, data gambar diberi label dengan membangun sebuah model object detection untuk tools LabelImg. Data gambar yang sudah diberikan label deteksi masa panen (siap panen atau belum siap panen) kemudian divalidasi oleh ahli pertanian tanaman tanaman sayuran. Hasil dari tingkat akurasi dalam mendeteksi masa siap panen prepocesing data kemudian diberikan label dengan tools (belum atau siap panen) tanaman sayuran.
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