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.
Highlights
Computer vision often abbreviated as CV is defined as the process of analyzing images and videos automatically to obtain meaningful inference or measurements without human intervention
E multiple classifier system (MCS) is a way of using many classifiers to make a final decision in a classification process. e ensemble of different classifiers has been used lately in pattern recognition to improve the performance and aims at increasing the accuracy of the single classifier system [19]. e idea is that two or more diverse classifiers when grouped help in negating the errors made by the individual classifiers [20]. ere are two types of decision functions that are commonly used in the design of MCSs. ey are classifier fusion and classifier selection [21, 22]
According to [43], to evaluate the performance of the classifier in a multiclass classification case, for each separate class Ci, TPi, true negative (TNi), false positives (FPi), false negative (FNi), Accuracyi, Recalli, and Specificityi can be calculated from the counts, counti from each class Ci. e performance of the classifier is calculated in two ways, one using macroaveraging and another microaveraging
Summary
Computer vision often abbreviated as CV is defined as the process of analyzing images and videos automatically to obtain meaningful inference or measurements without human intervention. E automatic weeding machine was proposed in [2], which was based on computer vision techniques [3]. Automation of various agricultural tasks such as disease detection in crops, precise spraying of pesticides, prediction of crop yield, estimation of soil texture, automatic grading of fruits, estimation of crop biomass, management of water balance in the irrigation system, and monitoring plant growth has been done using computer vision techniques [4, 5]. Wireless sensor networks and wireless visual sensor networks are contributing to sending the sensed data of the field either in image or in text form to the remote machine where it will be processed and analyzed for some kind of decision-making [8]. In [10], a wireless sensor network was implemented to predict the water requirement in semi-arid
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