Smartphones are equipped with various types of sensors which make them a promising tool to assist diverse digital farming tasks because of their mobility, cost, accessibility, and computing power allow us to perform real-time practical applications. This paper presents the utilization of various non-destructive methods of nutrient and disease classification techniques using smartphone collected images, processed through various image segmentation algorithms. Both in vivo and in vitro estimations shows comparable results with both chlorophyll and nitrogen contents of a crop shoot. Moreover, the correlation between SPAD measured values and nitrogen of crop shoot showed a significant linear association (R2 =0.7309), revealing the potency of in vivo observation for prediction of actual chlorophyll content in tea crop. SPAD values and yield have a strong linear relationship (R2 =0.7103), in which SPAD-meter performed better detection at very low values. The study concluded that the proposed techniques could be used for automatic detection as well as classification of foliar diseases and nutrients in tea.

Development of non-destructive methods to estimate functional traits and field evaluation in tea plantations using a smartphone