Weed detection using ultrasonic signal processing employing artificial neural network (ANN) with efficient extracted features

In recent decades, different technologies such as image processing, spectral processing, and ultrasound techniques have been used to detect various weed species. In this paper, in addition to reviewing the conventional methods of weed detection, an alternative method based on processing of ultrasonic signals is introduced. In this regard, with the aid of a proper setup with the capability of sending and receiving 40 kHz ultrasonic waves, five weed species namely namely Portulacacea, Chenopodiumalbum L., Tribulusterrestris L.,  Amaranthusretroflexus L. and Salsolaiberica were identified .The continuous 40 kHz ultrasonic waves are sent to weed canopy and received back by an ultrasonic receiver. These signals are then transferred to a laptop (DELL INSPIRON 5010) and stored in MATLAB 2013a software for several signal features extraction, using artificial neural network (ANN) to discriminate the weeds and ultimately weed classification. Overall, the results showed that by eliminating about 20% of the inefficient signal features, the maximum detection accuracy of the ANN performance could be reached as high as 80%

A Crop Canopy Localization Method Based on Ultrasonic Ranging and Iterative Self-Organizing Data Analysis Technique Algorithm

To protect crops from diseases and increase yields, chemical agents are applied by boom sprayers. To achieve the optimal effect, the boom and the crop canopy should be kept at an appropriate distance. So, it is crucial to be able to distinguish the crop canopy from other plant leaves. Based on ultrasonic ranging, this paper adopts the fuzzy iterative self-organizing data analysis technique algorithm to identify the canopy location. According to the structural characteristics of the crop canopy, based on fuzzy clustering, the algorithm can dynamically adjust the number and center of clusters so as to get the optimal results. Therefore, the distances from the sensor to the canopy or the ground can be accurately acquired, and the influence of lower leaves on the measurement results can be alleviated. Potted corn plants from the 3-leaf stage to the 6-leaf stage were tested on an experiment bench. The results showed that the calculated distances from the sensor to the canopy using this method had good correlation with the manually measured distances. The maximum error of calculated values appeared at the 3-leaf stage. With the growth of plants, the error of calculated values decreased. The increased sensor moving speeds led to increased error due to the reduced data points. From the 3-leaf stage to the 5-leaf stage, the distances from the sensor to the ground can also be obtained at the same time. The method proposed in this paper provides a practical resolution to localize the canopy for adjusting the height of sprayer boom