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Wireless sensor network applications in the agricultural sector are gaining popularity with the advancement of the Internet of Things technology. Predominantly, wireless sensor networks are used in agriculture to sense the important agricultural field parameters, such as temperature, humidity, soil moisture level, nitrite content in the soil, groundwater quality, and so on. These sensed parameters will be sent to a remote station, where it will be processed and analyzed to build a decision support system. This paper describes the implementation of a wireless visual sensor network for precision agriculture to monitor paddy crop for weeds using Raspberry Pi. Bluetooth 4.0 was used by visual sensor nodes to send the data to the base station. Base station forwarded the data to the remote station using IEEE 802.11 a/b/g/n standard. The solar cell battery was used to power up the sensor nodes and the base station. At the remote station, images were preprocessed to remove soil background and different shape features were extracted. Random forest and support vector machine classifiers were used to classify the paddy crop and weed based on the shape features. The results and observations obtained from the experimental setup of the system in a small paddy field are also reported. This system could be expected to enhance the crop production by giving timely advice to the crop producers about the presence of weeds so that steps can be taken to eradicate weeds.

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.

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Computers have become an integral part of human lives. Computers are used in almost every field even in agriculture. Technologies like computer vision-based pattern recognition are being used to detect diseases and pests like weeds affecting the crop. The Weeds are unwanted plants growing among crops competing for nutrients, water, and sunlight. It can significantly reduce the quality and yield of the crops incurring a huge loss to the farmers. This paper investigates the use of texture features extracted from Laws' texture masks for discrimination of Carrot crops and weeds in digital images. Laws' texture method is one of the popular methods used to extract texture features in medical image processing, though not much explored in plant-based images or agricultural images. This experiment was carried out on two categories of benchmark digital image datasets of Carrot crop and Carrot weed respectively, which are publicly available. A total of 70 texture features were extracted. The dimensionality reduction technique was used to get the optimal features. These features were then used to train the Random Forest classifier. The results and observations from the experiment showed that the classifier achieved above 94% accuracy.