A real-time plant discrimination system utilising discrete reflectance spectroscopy

An advanced, proof-of-concept real-time plant discrimination system is presented that employs two visible (red) laser diodes (635. nm, 685. nm) and one near-infrared (NIR) laser diode (785. nm). The lasers sequentially illuminate the target ground area and a linear sensor array measures the intensities of the reflected laser beams. The spectral reflectance measurements are then processed by an embedded microcontroller running a discrimination algorithm based on dual Normalised Difference Vegetation Indices (NDVI). Pre-determined plant spectral signatures are used to define unique regions-of-classification for use by the discrimination algorithm. Measured aggregated NDVI values that fall within a region-of-classification (RoC) representing an unwanted plant generate a spray control signal that activates an external spray module, thus allowing for a targeted spraying operation. Dynamic outdoor evaluation of the advanced, proof-of-concept real-time plant discrimination system, using three different plant species and control data determined under static laboratory conditions, shows that the system can perform green-from-green plant detection and accomplish practical discrimination for a vehicle speed of 3. km/h

Plant discrimination by Support Vector Machine classifier based on spectral reflectance

Support Vector Machine (SVM) algorithms are developed for weed-crop discrimination and their accuracies are compared with a conventional data-aggregation method based on the evaluation of discrete Normalised Difference Vegetation Indices (NDVIs) at two different wavelengths. A testbed is especially built to collect the spectral reflectance properties of corn (as a crop) and silver beet (as a weed) at 635 nm, 685 nm, and 785 nm, at a speed of 7.2 km/h. Results show that the use of the Gaussian-kernel SVM method, in conjunction with either raw reflected intensities or NDVI values as inputs, provides better discrimination accuracy than that attained using the discrete NDVI-based aggregation algorithm. Experimental results carried out in laboratory conditions demonstrate that the developed Gaussian SVM algorithms can classify corn and silver beet with corn/silver-beet discrimination accuracies of 97%, whereas the maximum accuracy attained using the conventional NDVI-based method does not exceed 70%

Laser-stabilised real-time plant discrimination sensor for precision agriculture

A novel proximal spectral-reflectance-based plant discrimination sensor for use in selective herbicide spraying systems is developed and its dynamic outdoor performance is experimentally assessed for two plants. For plant illumination, the sensor uses a new stabilized three-wavelength laser diode module that sequentially emits identically polarized laser light beams through a common aperture, along one optical path. Each laser beam enters a multi-spot beam generator, which produces 15 parallel, collimated laser beams spaced over a 230-mm span. The intensity of the reflected light from each beam is detected by a high-speed line scan image sensor. Plant discrimination is based on calculating two different normalised difference vegetation indices, and experimental results show that by improving the stability of the laser diodes, a plant discrimination rate greater than 90% can be achieved with a travelling speed of 7.5 km/h for canola and wild radish, which is a dominant weed in the canola crop field