Weed Identification in Sugarcane Plantation Through Images Taken from Remotely Piloted Aircraft (RPA) and kNN Classifier

Abstract

Abstract:The sugarcane is one of the most important crops in Brazil, the world´s largest sugar producer and the second largest ethanol producer. The presence of weeds in the sugarcane plantation can cause losses up to 90% of the production, caused by the competition for light, water and nutrients, between the crop and the weeds. Usually sugarcane plantations occupy large fields, and due to this, the weeds control is mostly chemical, which is more practical and cheaper than mechanical control. In the chemical control, the dosage and type of herbicides has been calculated by sampling, which causes problems of waste and misapplication of herbicides, since the degree of infestation may be variant from one location to another, as well as the species presents in the plantation. In order to avoid unnecessary waste in the herbicides application, there are some studies about weed identification using images taken from satellites, solution that have proved to have the advantage of covering the whole plantation, solving the problems of sample surveying, nevertheless, this method its dependent of a high weed density to ensure a good pattern recognition and its affected by the influence of clouds in the imagery quality. This work proposes a system for weed identification based on pattern recognition in imagery taken from a Remotely Piloted Aircraft (RPA). The RPA is able to fly at low altitude, so it is possible to take images closer to the plants and make the weed identification even in low infestation levels. In an initial evaluation, the system reached an overall accuracy of 83.1% and kappa coefficient of 0.775, using k-Nearest Neighbors (kNN) classifier.

A novel semi-supervised framework for UAV based crop/weed classification

“…It was essential to validate the developed technique, for which a comparison was made with well-established classification techniques such as Support Vector Machine (SVM) [45], K-Nearest Neighbor (KNN) [46], CNN [47]. SVM, working on the principle of statistical learning theory, is a machine learning method that uses nonlinear kernel functions for mapping original input data into high dimensional features seeking separate hyperplane, and the data is optimally separated into two categories by using the constructed N-dimensional hyperplane [45].…”

“…The nearest neighbor class is assigned when the value of the object is 1. Euclidean distance is a commonly used metric for calculating the closest k elements distance [46]. CNN is a highly accurate technique normally used to classify images [48].…”

RGB Image-Derived Indicators for Spatial Assessment of the Impact of Broadleaf Weeds on Wheat Biomass

“…Several image classifiers based on machine learning have been successfully applied to classify plants and to identify weed species [19][20][21][22]. The recent development of high resolution UAV imagery [20,[23][24][25] allowed fast and accurate crop weed recognition.…”

High-resolution data for mapping the spatio-temporal variability of sugarcane fields

“…Also, the influence of luminosity on images requires the same field conditions during the data acquisition. On the other hand, there is a greater advance on development of proximal imaging systems, such as weed management strategies for local interventions (YANO et al, 2017;GÉE and DENIMAL, 2020), and indirect measurements of above-ground biomass at early phenological crop stages (LU et al, 2019;NIU et al, 2019). Other studies used digital cameras embedded in aerial platforms as an alternative to map crop diseases (MATTUPALLI et al, 2018), phenological features of the plants using time-series analysis (BURKART et al, 2018), classification of vegetation types (KOMÁREK et al, 2019), biomass estimation (BENDIG et al, 2014;ACORSI et al, 2019), rice protein content estimation (ONOYAMA et al, 2018), environmental monitoring (TMUŠIĆ et al, 2020), among others.…”