crop monitoring
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2022 ◽  
Vol 277 ◽  
pp. 108419
Author(s):  
Qi Yang ◽  
Liangsheng Shi ◽  
Jingye Han ◽  
Zhuowei Chen ◽  
Jin Yu

2022 ◽  
pp. 345-359
Author(s):  
Himadri Nath Saha ◽  
Reek Roy ◽  
Monojit Chakraborty ◽  
Chiranmay Sarkar

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 228
Author(s):  
Manuel Forcén-Muñoz ◽  
Nieves Pavón-Pulido ◽  
Juan Antonio López-Riquelme ◽  
Abdelmalek Temnani-Rajjaf ◽  
Pablo Berríos ◽  
...  

Crop sustainability is essential for balancing economic development and environmental care, mainly in strong and very competitive regions in the agri-food sector, such as the Region of Murcia in Spain, considered to be the orchard of Europe, despite being a semi-arid area with an important scarcity of fresh water. In this region, farmers apply efficient techniques to minimize supplies and maximize quality and productivity; however, the effects of climate change and the degradation of significant natural environments, such as, the “Mar Menor”, the most extent saltwater lagoon of Europe, threatened by resources overexploitation, lead to the search of even better irrigation management techniques to avoid certain effects which could damage the quaternary aquifer connected to such lagoon. This paper describes the Irriman Platform, a system based on Cloud Computing techniques, which includes low-cost wireless data loggers, capable of acquiring data from a wide range of agronomic sensors, and a novel software architecture for safely storing and processing such information, making crop monitoring and irrigation management easier. The proposed platform helps agronomists to optimize irrigation procedures through a usable web-based tool which allows them to elaborate irrigation plans and to evaluate their effectiveness over crops. The system has been deployed in a large number of representative crops, located along near 50000 ha of the surface, during several phenological cycles. Results demonstrate that the system enables crop monitoring and irrigation optimization, and makes interaction between farmers and agronomists easier.


2021 ◽  
pp. 1-17
Author(s):  
Ankur Pandit ◽  
Suryakant Sawant ◽  
Jayantrao Mohite ◽  
Srinivasu Pappula

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhenwang Qin ◽  
Wensheng Wang ◽  
Karl-Heinz Dammer ◽  
Leifeng Guo ◽  
Zhen Cao

To date, unmanned aerial vehicles (UAVs), commonly known as drones, have been widely used in precision agriculture (PA) for crop monitoring and crop spraying, allowing farmers to increase the efficiency of the farming process, meanwhile reducing environmental impact. However, to spray pesticides effectively and safely to the trees in small fields or rugged environments, such as mountain areas, is still an open question. To bridge this gap, in this study, an onboard computer vision (CV) component for UAVs is developed. The system is low-cost, flexible, and energy-effective. It consists of two parts, the hardware part is an Intel Neural Compute Stick 2 (NCS2), and the software part is an object detection algorithm named the Ag-YOLO. The NCS2 is 18 grams in weight, 1.5 watts in energy consumption, and costs about $66. The proposed model Ag-YOLO is inspired by You Only Look Once (YOLO), trained and tested with aerial images of areca plantations, and shows high accuracy (F1 score = 0.9205) and high speed [36.5 frames per second (fps)] on the target hardware. Compared to YOLOv3-Tiny, Ag-YOLO is 2× faster while using 12× fewer parameters. Based on this study, crop monitoring and crop spraying can be synchronized into one process, so that smart and precise spraying can be performed.


2021 ◽  
Vol 13 (24) ◽  
pp. 4985
Author(s):  
Regina Kilwenge ◽  
Julius Adewopo ◽  
Zhanli Sun ◽  
Marc Schut

Crop monitoring is crucial to understand crop production changes, agronomic practice decision-support, pests/diseases mitigation, and developing climate change adaptation strategies. Banana, an important staple food and cash crop in East Africa, is threatened by Banana Xanthomonas Wilt (BXW) disease. Yet, there is no up-to-date information about the spatial distribution and extent of banana lands, especially in Rwanda, where banana plays a key role in food security and livelihood. Therefore, delineation of banana-cultivated lands is important to prioritize resource allocation for optimal productivity. We mapped the spatial extent of smallholder banana farmlands by acquiring and processing high-resolution (25 cm/px) multispectral unmanned aerial vehicles (UAV) imageries, across four villages in Rwanda. Georeferenced ground-truth data on different land cover classes were combined with reflectance data and vegetation indices (NDVI, GNDVI, and EVI2) and compared using pixel-based supervised multi-classifiers (support vector models-SVM, classification and regression trees-CART, and random forest–RF), based on varying ground-truth data richness. Results show that RF consistently outperformed other classifiers regardless of data richness, with overall accuracy above 95%, producer’s/user’s accuracies above 92%, and kappa coefficient above 0.94. Estimated banana farmland areal coverage provides concrete baseline for extension-delivery efforts in terms of targeting banana farmers relative to their scale of production, and highlights opportunity to combine UAV-derived data with machine-learning methods for rapid landcover classification.


2021 ◽  
pp. 199-210
Author(s):  
Smita Agrawal ◽  
Preeti Kathiria ◽  
Vishwam Rawal ◽  
Trushit Vyas
Keyword(s):  

2021 ◽  
Author(s):  
O. Vishali Priya ◽  
R. Sudha

In today’s world, technology is constantly evolving; various instruments and techniques are available in the agricultural field. And within the agrarian division, the IoT preferences are Knowledge processing. With the help of introduced sensors, all information can be gathered. The reduction of risks, the mechanization of industry, the enhancement of production, the inspection of livestock, the monitoring of environment conditions, the roboticization of greenhouses, and crop monitoring Nearly every sector, like smart agriculture, has been modified by Internet-of-Things (IoT)-based technology, which has shifted the industry from factual to quantitative approaches. The ideas help to link real devices that are equipped with sensors, actuators, and computing power, allowing them to collaborate on a task while staying connected to the Internet, dubbed the “Internet of Things” (IoT). According to the World Telecommunication Union’s Worldwide Guidelines Operation, the Internet of Things (IoT) is a set of sensors, computers, software, and other devices that are connected to the Internet. The paper is highly susceptible to the consequences of its smart agriculture breakthrough.


2021 ◽  
Vol 937 (2) ◽  
pp. 022047
Author(s):  
A Belyaev ◽  
S Kramarov ◽  
O Mityasova ◽  
O Popov ◽  
V Khramov

Abstract Decarbonization issues are one of the main strategic directions of modern environmental development today. New technologies of agricultural use of soils are used to fix carbon in the soil in the form of humus, which ultimately helps to reduce the greenhouse effect and actively affects the amount of carbon entering the atmosphere. The use of open data of remote sensing of the Earth from space (further - RSE) together with the data of satellite monitoring of the Normalized Difference Vegetation Index (NDVI) can allow us to obtain new methods of carbonation analysis. In this paper, we consider the possibilities of such use of standard NDVI data together with a more accurate definition of specific boundaries of agricultural fields in order to increase the accuracy of research results. This article shows the results of processing data and images obtained using open crop monitoring data. The proposed technology is proposed by us as an additional tool for monitoring changes in the ecosystems of the regions.


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