Abstract
much needed to meet the needs of both industry and households. However, tomato plants still require serious handling in increasing the yields. Data from the Central Bureau of Statistics shows that the number of tomatoes produced is not in accordance with a large number of market demands, resulting from the decrease of tomato yields. One of the obstacles in increasing tomato production is that the crops are attacked by septoria leaf spot disease due to the fungus or the fungus Septoria Lycopersici Speg. Most farmers have limited knowledge of the early symptoms, which are not obvious, and also facing difficulty in detecting this disease earlier. The problem has been causing disadvantages such as crop failure or plant death. Based on this problem, a study will be conducted with the aim of designing a tool that can be used to detect septoria leaf spot disease based on deep learning using the Convolutional Neural Network (ConvNets or CNN) model, where an algorithm that resembles human nerves is one of the supervised learning and widely used for solving linear and non-linear problems. In addition, the researcher used the Raspberry Pi as a microcontroller and used the Intel Movidius Neural Computing Stick (NCS) which functions to speed up the computing process so that the detection process is easier because of its portable, fast and accurate nature. The average accuracy rate is 95.89% with detection accuracy between 84.22% to 100%.
Highlights
Tomato is a horticultural commodity that has the potential to be developed, because it has high economic value, and is much needed to meet the needs of both industry and households
Data from the Central Bureau of Statistics shows that the number of tomatoes produced is
of the obstacles in increasing tomato production is that the crops are attacked by
Summary
Kumpuluan citra daun tomat yang akan digunakan diunduh dari website Kaggle. Sebelumnya, pra-proses dijalankan agar data yang digunakan memiliki dimensi yang sama. Serta pelatihan menggunakan model EfficientNet seperti yang terlihat pada gambar 9 dan 10 memiliki nilai akurasi yang stabil di 1,00 dan nilai loss yang kecil dengan rata-rata nilai loss adalah 0. Penelitian ini memilih model yang dilatih menggunakan InceptionV3 karena EfficientNet sendiri masih belum didukung oleh teknologi OpenVINO, sehingga model EfficientNet tidak dapat digunakan untuk optimasi menggunakan inference engine pada OpenVINO. Hasil dan Pembahasan menggunakan model Sequential menghasilkan nilai perbandingan akurasi training dan validasi yang tidak Sistem yang dihasilkan berfungsi untuk mendeteksi stabil dimana nilai akurasi pelatihan sekitar 0,5 dan nilai penyakit bercak daun septoria pada tanaman tomat akurasi validasi mengalami perubahan yang signifikan. Luaran dari penelitian ini adalah menggunakan model Sequential menghasil nilai loss sistem yang dapat digunakan untuk mendeteksi penyakit cukup besar yaitu diatas 0,70.
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