In England, agricultural technology and machinery experts are working with IDS Imaging Development Systems GmbH (Obersulm, Germany) to develop a robotic solution to automate lettuce harvesting.
Recognizing organic and multi-variant objects is no easy challenge in machine vision. However, the situation is different when using intelligent cameras such as those of the IDS NXT series.
By placing the camera inside the gripper, the position of the camera is fully controllable. The camera can point its optical axis at the apple, reducing image distortion and eliminating repetitive calibration steps during apple picking.
The LemnaTec system, called Scanalyzer3D, utilizes advanced cameras from Allied Vision to provide researchers with previously unavailable data on plant growth, root development, water absorption and drying, and photosynthesis.
BOWERY FARMING ACQUIRES TRAPTIC, 3D VISION AND ROBOTICS HARVESTING STARTUP, TO ACCELERATE THE COMMERCIALIZATION OF FRUITING AND VINE CROPS
Traptic's 3D vision, robotic arms and artificial intelligence (AI) combined with Bowery's world-class technology deployed in its farms will be used to harvest ripe strawberries.
Ground-breaking technology partnership will significantly improve plant yields and quality in AeroFarms' vertical farms through analytics, AI, drones and wireless networking.
By using a computer vision-controlled system to map terrain and identify the size and location of rocks embedded in the soil, the heavy lifting is done by automated machinery which saves farmers a lot of time and money.
Image processing with cameras and AI enables applications that were previously unthinkable. In agriculture, for example, it supports the optimal application of fertilizers, the visual monitoring of products and growth phases, and the processing of harvested crops.
Anyone who wants to realize applications in which objects with a wide variety of characteristics - such as vegetables or fruit - are to be reliably recognized, cannot afford to ignore image processing with artificial intelligence.
Digitized and automated processes are becoming increasingly important in modern agricultural enterprises. Machine vision methods play a key role here. Among other things, they can be used for a variety of precision farming applications.
Stuttgart-based technet GmbH has developed a system that uses a USB 3 uEye CP industrial camera from IDS to capture the contours and surface parameters of the parasites and determines the curvature energies of individual larvae from these data.
The all-in-one solution IDS NXT ocean includes not only industrial-grade camera hardware with a powerful AI core but also training software for creating individual neural networks.
European software developer 'Cognitive Technologies has developed the world's first industrial agrodroid for international agricultural market.
Multispectral cameras capture spectral signatures much like a thermometer measures the temperature of the human body. Changes in plant reflectance indicate stress, but additional information is required to identify the cause.
AgShift is building worlds first autonomous food inspection system using Deep learning and they plan to use the funding to strengthen product development and expand customer reach.
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Artificial intelligence can be used, for example, to classify fruit varieties or to identify damaged parts, e.g. apples with marks or color deviations. To cover all possible variances with classical image processing would be very time-consuming and costly. With artificial intelligence, however, these challenges can be solved in no time at all. IDS NXT ocean is a user-friendly all-one-one system which requires neither special knowledge in deep learning nor camera programming. Only sample images and knowledge on how to evaluate them (e.g. "good apples" / "bad apples") are needed. This makes the start into AI-based image processing particularly easy. Camera hardware, software, infrastructure and support come from a single company. For beginners, IDS offer the IDS NXT ocean Creative Kit, which includes all components and workflows to create, train and run a neural net.