Application of Machine Learning to Study Effect of Environmental Manipulation in Frame of Smart Agriculture on the Stomata of Capsicum annuum

Abstract

Red chili (Capsicum Annuum) is a horticultural crop with high economic value and is one of the commodities contributing to inflation. This study aimed to explore the effect of environment manipulation on the number and the size of the stomata pore of red chili leaves. The study was conducted in screen house size 60 m2. Chili plants 95 days old were cultivated in the polybag and the stomata were measured 60 days after planting. Microclimate condition was recorded using automatic weather equipment. The crop water requirements were calculated using Penman-Monteith based on microclimate data. The stomata of leaves were observed in laboratory using by using a microscope mounted with an Opti-lab 2.2 camera. The Artificial Neural Network was applied to understand the role of microclimate parameters on stomata. The results showed that the average ETc value was 5.06 mm/day. The difference in ETc values occurs due to differences in plant coefficient values and climatic conditions in the study area and the seasons in the region. The largest porous stomata area was found in the afternoon, which is 144.88 µm2. The highest stomatal density the morning and afternoon between 284.94 mm-2-330.541 mm-2. The algorithm multilayer perception test revealed that the light factor was the most significant influence on the stomatal response of 0.463 (100%), followed by the humidity factor of 0.260 (56.1%). Other factors are under 50%.

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