Image Processing Applications in Precision Agriculture

In this page, you will learn about image processing applications for precise agriculture. If you want to boost your project with the newest technology advancements in artificial intelligence, request a call from RSIP Vision’s experts.

Image processing holds an effective set of tools for the analysis of imagery used in precise agriculture. From the farmers’ perspective, automating analysis of yield limiting factors and building rational management plans saves both time and money.  Automating this analysis is especially beneficial for those farmers to which expert knowledge and advice is not readily available or affordable. Technological advances in the development of precision agriculture machinery and software will then prove to be cheaper and faster than on-ground human intervention and data collection.

Advancements in both image processing routines and communication systems now (literally) change the picture for farmers. The amount of image processing applications in precise agriculture is growing steadily with the availability of higher-quality measurements coupled with modern algorithms and increased possibility to fuse multiple sources of information from satellite imagery and sensors positioned in fields. This article focuses on the applications of image processing in precision agriculture.

Major concerns in agriculture are water stress, quality of yields, and the use of pesticides. Providing data and monitoring irrigation, whether artificial or natural, is possible by tracking satellite imaging of fields over time. Applications in precision agriculture allow mapping of irrigated lands at lower costs. Water also affects the thermal properties of plants. Therefore, processing infrared imaging provides additional means to analyze and monitor irrigation. The analysis from infrared imaging can then be used in pre-harvesting operations, to decide whether or not or even where to harvest.

Foreign plants (weeds) growing in farms can also be detected by combining image processing and machine learning techniques. Edge based machine learning classifiers can identify weeds in color images. In addition, classification based on plant color features can be added and information regarding the texture of plants integrated to enhance classification accuracy. The partial success of these algorithms has motivated further development in herbicide applications. Fuzzy algorithms based on green color analysis of plants have provided weed coverage estimation and allowed for the integration of this knowledge into farm management plans.

The quality of yield is another concern of farmers. Automated quality analysis of food products is a great money and labor saving process, especially in light of heavy regulations on fruit quality and safety standards.  Image processing is an accurate and reliable method for sorting and grading fresh products (fruits, grains, bakery products, etc.) characterized by color, size and shape. By combining analysis of these features, RSIP Vision has developed algorithms for sorting and grading that are currently embedded within industrial production machinery.

From the applications in precision agriculture listed above, we can easily imagine the future of the role of image processing in agricultural processes. As fields and farms grow bigger, better monitoring systems are needed for automated management and reduced expenses. In addition, the availability of both hardware and software at relatively affordable pricesmakes the integration of image processing techniques in field management plans and food quality examination processes easy and affordable. In the era of information, the fusion of images and sensor data will prove to be straightforward and beneficial for farmers and consumers alike.

 

Image by hiyori13 at http://www.flickr.com/photos/hiyori13/31685467/
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