Paddy Crop and Weed Discrimination: A Multiple Classifier System Approach

Abstract

Weeds are unwanted plants that grow among crops. These weeds can significantly reduce the yield and quality of the farm output. Unfortunately, site-specific weed management is not followed in most of the cases. That is, instead of treating a field with a specific type of herbicide, the field is treated with a broadcast herbicide application. This broadcast application of the herbicide has resulted in herbicide-resistant weeds and has many ill effects on the natural environment. This has prompted many research studies to seek the most effective weed management techniques. One such technique is computer vision-based automatic weed detection and identification. Using this technique, weeds can be detected and identified and a suitable herbicide can be recommended to the farmers. Therefore, it is important for the computer vision technique to successfully identify and classify the crops and weeds from the digital images. This paper investigates the multiple classifier systems built using support vector machines and random forest classifiers for plant classification in classifying paddy crops and weeds from digital images. Digital images of paddy crops and weeds from the paddy fields were acquired using three different cameras fixed at different heights from the ground. Texture, color, and shape features were extracted from the digital images after background subtraction and used for classification. A simple and new method was used as a decision function in the multiple classifier systems. An accuracy of 91.36% was obtained by the multiple classifier systems and was found to outperform single classifier systems.

Weed Detection in Rice Fields Using Remote Sensing Technique: A Review

“…Successfully controlling weeds at this stage can deliver a 95% weed-free yield [23]. This is agreed with by Kamath et al [24] because the effect of weeds in this stage will be at maximum. However, if we fail to prevent weeds from spreading in the vegetative stage, they will dominate the area, leading to a lack of sufficient space, light, and nutrients to grow and develop [25].…”

Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

“…Several image processing algorithms can be used to extract distinguishing features from images or videos obtained from a scene. Numerous types of image-based features including colour [10][11][12], shape [13][14][15], texture [16][17][18], wavelet transform [19][20][21], and fusion of different features [22][23][24] have been applied to plant type identification with acceptable accuracies encouraging the further application of these image-based features for crop-weed discrimination.…”

The Application of Hyperspectral Remote Sensing Imagery (HRSI) for Weed Detection Analysis in Rice Fields: A Review

“…In the first, second and third years of a three-year case study, overall accuracies of 90.19%, 92.36%, and 93.8%, respectively, were achieved. Kamath et al [79] looked at how to categorize paddy crops and weeds from digital images utilizing several classifier systems developed with support vector machines (SVM) and random forest classifiers (RFs) in which the dataset included paddy plants and weeds from the seedling stage (1-leaf seedling) to the flowering stage. The results with an accuracy of 91.36% showed that multiple classifier systems were shown to outperform single classifier systems and the extracted features are good for paddy crops and weeds classification.…”