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Utilizing satellite and UAV data for crop yield prediction and monitoring through deep learning

Mathivanan Sandeep Kumar, Jayagopal Prabhu

Acta Geophysica

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2022

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Vol. 70, no 6

2991--3004

EN

Agriculture is sighted more use cases of drones, and with the expanding population, food yields are becoming more well organized. Drones are used in examining crops and exploiting data to determine what requires greater attention. This research study focuses on how deep learning (DL) has been used with drone technology to create solutions for detecting crop fields within a certain regions of interest (ROI). Extracting images from a drone and analysing them with a DL system to identify crop fields and yields for less-developed nations are solution to a prevalent challenge that land use–land cover (LULC) encounters. The limitations of drone spot-checking in the context of agricultural fields and the constraints of utilizing DL to detect yields. Also, a novel method is offered for detecting and tracking crop fields using a single camera on our UAV. The estimated background movements using a perspective transformation model given a sequence of video frames and then locate distinct locations in the background removed picture to detect moving objects. The optical flow matching is used to determine the spatiotemporal features of each moving item and then categorize our targets, which have considerably different motions than the backdrop. Kalman filter tracking has used to ensure that our detections are consistent across time. The hybrid crop field detection model is to evaluate on real uncrewed aerial vehicle (UAV) recordings. And the findings suggest that hybrid crop field detection successfully detects and tracks crop fields through tiny UAV’s with low computational resources. A crop field module, which aids in reconstruction quality evaluation by cropping specific ROIs from the whole field, and a reversing module, which projects ROIs-Vellore to relative raw pictures, are included in the proposed method. The results exhibit faster identification of cropping and reversing modules, impacting ROI height selection and reverse extraction of ROI location from raw pictures.

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Three-level neural network for data clusterization on images of infected crop field

Doudkin A. A., Inyutin A. V., Petrovsky A. I., Vatkin M. E., Sobkowiak B.

Journal of Research and Applications in Agricultural Engineering

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2007

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Vol. 52, nr 1

5-7

EN

The objective of this research was to use neural network approach for segmentation problem of agricultural landed-fields in remote sensing data. A neural network clusterization algorithm for segmentation of the color images of crop field infected by diseases that change usual color of agricultural plants is proposed. It can be applied for cartography of fields infected by plant diseases to reduce the use of plant protection products.

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The impact of a shelterbelt on soil properties an microbial activity in an adjacent corp field

Wojewoda D., Russel S.

Polish Journal of Ecology

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2003

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Vol. 51, nr 3

291-307

EN

Studies were carried out in a 8 years old shelterbelt planted across croplands (D. Chłapowski Landscape Park, region of Wielkopolska, western Poland) and in the easterly adjacent field. The effect of the shelterbelt on: soil organic matter content, soil respiration, microbial biomass, dehydrogenase activity, mineralization potential of N and total number of bacteria and fungi in the upper soil layer was analysed. Samples were collected along parallel sites situated in the central part of the strip (S), in the wood edge (Es), field edge (Ef) and in the field 10 m (F10) and 50 m (F50) away from the strip. All studied microbial parameters, except for plate counted total numbers of bacteria and fungi, showed a high and significant correlation with soil respiration and microbial biomass. The highest values were found in the wood soil, lowest in the field soil and intermediate in the field edge. Marked vertical differences between layers 0-3 and 3-10 cm were noticed on sites S, Es and Ef while stratification in the field was visible not earlier than 11 months after ploughing. Some parameters: soil organic matter contents, dehydrogenase activity, microbial biomass estimated by fumigation-extraction method and mineralization potential of N, showed a regular pattern along the gradient from the shelterbelt to the middle of the field. The study suggests a favourable effect of a shelterbelt on organic matter content in the adjacent field. This effect may increase with age of the wood strip.