A high-throughput plant phenotyping system automatically observes and grows many plant samples. Many plant sample images are acquired by the system to determine the characteristics of the plants (populations). Stable image acquisition and processing is very important to accurately determine the characteristics. However, hardware for acquiring plant images rapidly and stably, while minimizing plant stress, is lacking. Moreover, most software cannot adequately handle large-scale plant imaging. To address these problems, we developed a new, automated, high-throughput plant phenotyping system using simple and robust hardware, and an automated plant-imaging-analysis pipeline consisting of machine-learning-based plant segmentation. Our hardware acquires images reliably and quickly and minimizes plant stress. Furthermore, the images are processed automatically. In particular, large-scale plant-image datasets can be segmented precisely using a classifier developed using a superpixel-based machine-learning algorithm (Random Forest), and variations in plant parameters (such as area) over time can be assessed using the segmented images. We performed comparative evaluations to identify an appropriate learning algorithm for our proposed system, and tested three robust learning algorithms. We developed not only an automatic analysis pipeline but also a convenient means of plant-growth analysis that provides a learning data interface and visualization of plant growth trends. Thus, our system allows end-users such as plant biologists to analyze plant growth via large-scale plant image data easily.

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Broiler merupakan unggas yang membutuhkan suhu udara yang konstan untuk tumbuh. Perubahan suhu yang ekstrim menyebabkan broiler menderita stres, lambat tumbuh, dan bahkan mati. Brooder diperlukan untuk memberikan panas agar suhu kandang tidak berada di bawah nilai minimum suhu optimal bagi pertumbuhan broiler. Penelitian ini bertujuan modifikasi brooder listrik konvensional agar dapat menjaga suhu udara kandang broiler pada kisaran optimum secara otomatis dan menganalisis hasil implementasi microcontroller pada brooder listrik hasil modifikasi tersebut. Metode yang digunakan adalah metode deskripsi analitik dan rekayasa. Uji verifikasi dan uji validasi dilakukan terhadap brooder dalam empat tingkat penyalaan pemanas: 500 W, 1.000 W, 1.500 W dan 2.000 W. Hasilnya adalah sistem pengendali yang membuat brooder listrik dapat dioperasikan secara otomatis dengan mudah serta secara sekaligus merekam data suhu udara secara real-time.