To achieve an evidence-based agriculture, it is important to realize real-time monitoring of the degree of growth, abnormality detection, and quality prediction. This research aims to 1) construct a cultivation-database by using legacy farm record data sets and the AMeDAS meteorological data, 2) clarify the “index of growth” by objectively and continuously measuring the degree of growth, and 3) establish the measurement method. Specifically, we are focusing on the series characteristic of farm work and looking for methods of introducing and tuning of cascaded model, and developing the PASERI: “Photosynthetically Active Radiation Sensor for Evidence-based agRIculture” platform, which measures light transmittance over and under foliage in several greenhouses. We then model and correlate the data to leaf area index to validate the performance of our method. Now we are also developing a “Smart Greenhouse”, an application that maintains a desire environmental status in a greenhouse using macroclimate data obtained by WSNs.