There are probably some smart algorithms that will be helpful (Image Analyst mentioned some hints). But here's a lower-level solution that relies on a subjetive threshold such that when the change in y-values drops below the threshold, it triggers the detection.
I only downloaded your figure and extracted the y values from each line and this works with your 3 lines. In this code, y is a vector of y-values from one of your lines.
How it works: It uses a sliding window (currently set to 5 coordinates) and compares the mean of the first 4 values and the last value. If the fraction y(5)/mean(y(1:4)) is less than 0.99, it detects the drop.
%Define window size: number of samples within window
win = 5;
% Sliding window that compares the last value in the window with the
% mean of the preceeding values in the window. If the mean/last is < .99
% it detects the change. Assumes drop is not prior to the index win-1
dropFound = false; % search flag
i = 1; %starting index
while ~dropFound
dropFound = y(i+win-1)/mean(y(i:i+win-2)) < 0.99;
i = i+1;
if i == numel(y)-win
% if you get to the end and haven't found a drop,
% quit the loop and throw a warning
warning('Drop not found')
end
end
yChangeIdx = i+win-2; % Index of coordinate that drops
xline(x(yChangeIdx)) % Draw a vertical line at the drop
