Machine vision monitoring of plant health.
Techniques and algorithms to detect and diagnose disorders in plants grown in a controlled environment have been developed. A video camera senses features of plants which are indicative of disorders. Images are calibrated for size and color variations by using calibration templates. Different image segmentation techniques for separating object from background, have been implemented. Plant size and color properties have been investigated, temporal, spectral and spatial variation of leaves were extracted from the segmented images. Neural network and statistical classifiers were used to determine plant condition.
Hetzroni
A
A
Department of Agricultural Engineering, Purdue University, West Lafayette, IN 47907-1146, USA.
Miles
G E
GE
Engel
B A
BA
Hammer
P A
PA
Latin
R X
RX
D000465:Algorithms; D002138:Calibration; D003116:Color; D018504:Ecological Systems, Closed; D004867:Equipment Design; D018527:Hydroponics / Q000295:instrumentation / Q000379:methods; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted; D007501:Iron / Q000378:metabolism; D000090463:Iron Deficiencies; D018545:Lactuca / Q000033:anatomy & histology / Q000254:growth & development / Q000378:metabolism / Q000502:physiology*; D008021:Life Support Systems / Q000295:instrumentation; D009584:Nitrogen / Q000172:deficiency / Q000378:metabolism; D010935:Plant Diseases / Q000145:classification; D012984:Software; D014741:Video Recording; D015032:Zinc / Q000172:deficiency / Q000378:metabolism
D007501:Iron; D015032:Zinc; D009584:Nitrogen