A method for monitoring a plant health state, comprising receiving (S110) first plant health state information provided by a plant health state measuring device, wherein the first plant health state information at least comprises current plant image information; determining (S121) the first plant health state information by using a convolutional neural network model to obtain a first poor plant health probability; determining (S122) whether the first poor plant health probability is greater than a set threshold; when the first poor plant health probability is greater than the set threshold, confirming (S123) that the at least one plant is at risk of poor health and determining the orientation information of the at least one plant at risk of poor health; receiving (S130) second plant health state information in an orientation where at least one plant at risk of poor plant health is located, wherein the second plant health state information at least comprises current plant image information corresponding to the orientation where the at least one plant at risk of poor health is located; determining (S141) the second plant health state information by using a convolutional neural network model to obtain a second poor plant health probability; determining (S142) whether the second poor plant health probability is greater than a set threshold; and when the second poor plant health probability is greater than the set threshold, confirming (S150) that the at least one plant is in poor health, and determining the degree of poor plant health and orientation information of the at least one plant in poor health. receiving (S110) first plant health state information provided by a plant health state measuring device, wherein the first plant health state information at least comprises current plant image information; determining (S121) the first plant health state information by using a convolutional neural network model to obtain a first poor plant health probability; determining (S122) whether the first poor plant health probability is greater than a set threshold; when the first poor plant health probability is greater than the set threshold, confirming (S123) that the at least one plant is at risk of poor health and determining the orientation information of the at least one plant at risk of poor health; receiving (S130) second plant health state information in an orientation where at least one plant at risk of poor plant health is located, wherein the second plant health state information at least comprises current plant image information corresponding to the orientation where the at least one plant at risk of poor health is located; determining (S141) the second plant health state information by using a convolutional neural network model to obtain a second poor plant health probability; determining (S142) whether the second poor plant health probability is greater than a set threshold; and when the second poor plant health probability is greater than the set threshold, confirming (S150) that the at least one plant is in poor health, and determining the degree of poor plant health and orientation information of the at least one plant in poor health. <EOS>