This paper investigates the potential of a consumer-grade infrared stereo camera, i.e. the Intel RealSense D435, to automatically extract crop status information, such as Normalized Difference Vegetation Index (NDVI), in arable and permanent crops. The sensing device includes two infrared (IR) sensors for depth calculation and one colour sensor, which provide, for each point of the scene, both IR and visible light information thus making it possible pixel per pixel NDVI estimations. Measurements were performed on various arable crops including corn (Zea mays) and barley (Ordeum vulgare) and on two vine varieties, Freisa and Malvasia, and were compared to measurements taken by a Trimble GreenSeeker handheld crop sensor. Results show that the RealSense camera tends to underestimate NDVI values compared to the GreenSeeker, with squared correlation coefficient r2 = 0.68. The fitted regression equation is successively applied to correct new camera observations, resulting in good agreement with the GreenSeeker output. The use of the RGB-D camera to simultaneously provide canopy height measurements by a farmer robot is also demonstrated in a Malvasia field, showing that the proposed system can be effectively adopted for fully automated plant-scale monitoring of vineyards.
KEYWORDS: Image segmentation, Semantics, RGB color model, Deep learning, Cameras, Decision trees, Education and training, Machine learning, Data modeling, Agriculture
In-field sensing systems for automatic yield monitoring are gaining increasing importance as they promise to give a considerable boost in production. The development of artificial intelligence and sensing technologies to assist the human workforce also meets sustainability needs, which impact the ecological goals of current and future agricultural processes. In this context, image acquisition and processing systems are widely adopted to extract useful information for farmers. Although RGB-D cameras have been used in many applications for ground-based proximal sensing, relatively few works can be found that include depth information in image analysis. In this work, both semantic and depth information from RGB-D vineyard images is used in processing pipeline composed of a decision tree algorithm and a deep learning model. The goal is to reach coherent semantic segmentation of a set of natural images acquired at both long and short distances, using a low-cost RGB-D camera in an experimental vineyard. Depth information of each image is fed into a decision tree to predict the distance of the acquired vines from the camera. Before feeding the deep learning models, the images to be segmented are manipulated according to the predicted distance. The results of semantic segmentation with and without using the decision tree are compared, showing how depth information appears to be highly relevant in enhancing the accuracy and precision of the predicted semantic maps.
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