A review on the use of drones for precision agriculture

Daponte, P.; Vito, Luca De; Glielmo, Luigi; Iannelli, Luigi; Liuzza, Davide; Picariello, Francesco; Silano, Giuseppe

doi:10.1088/1755-1315/275/1/012022

Cited by 112 publications

(68 citation statements)

References 16 publications

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“…Specifically, low-cost light UAVs dotted with RGB cameras are the most preferred Unmanned Aerial System (UAS) by many users, farmers among others. Whereas RGB cameras are limited by the development of spectral products, beyond scouting and monitoring, DSMs avoid artefacts that appear often in the UAV imagery, such as shadows projected from objects in the surface or clouds, light differences during the flight time or different colours caused by soil moisture or wet vegetation [38][39][40]. These challenges can be addressed with deep learning, but they need a large labelled database to feed the algorithm [41].…”

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confidence: 99%

Crop Loss Evaluation Using Digital Surface Models from Unmanned Aerial Vehicles Data

Millán

Rankine²,

Sánchez‐Azofeifa

2020

Remote Sensing

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Precision agriculture and Unmanned Aerial Vehicles (UAV) are revolutionizing agriculture management methods. Remote sensing data, image analysis and Digital Surface Models derived from Structure from Motion and Multi-View Stereopsis offer new and fast methods to detect the needs of crops, greatly improving crops efficiency. In this study, we present a tool to detect and estimate crop damage after a disturbance (i.e., weather event, wildlife attacks or fires). The types of damage that are addressed in this study affect crop structure (i.e., plants are bent or gone), in the shape of depressions in the crop canopy. The aim of this study was to evaluate the performance of four unsupervised methods based on terrain analyses, for the detection of damaged crops in UAV 3D models: slope detection, variance analysis, geomorphology classification and cloth simulation filter. A full workflow was designed and described in this article that involves the postprocessing of the raw results from the terrain analyses, for a refinement in the detection of damages. Our results show that all four methods performed similarly well after postprocessing-reaching an accuracy above to 90%-in the detection of severe crop damage, without the need of training data. The results of this study suggest that the used methods are effective and independent of the crop type, crop damage and growth stage. However, only severe damages were detected with this workflow. Other factors such as data volume, processing time, number of processing steps and spatial distribution of targets and errors are discussed in this article for the selection of the most appropriate method. Among the four tested methods, slope analysis involves less processing steps, generates the smallest data volume, is the fastest of methods and resulted in best spatial distribution of matches. Thus, it was selected as the most efficient method for crop damage detection. Under many climate change scenarios, many authors point out that human systems developed during the Holocene (such as agriculture) are under risk of disappearing or at least be compromised in the Anthropocene era that we have entered, and it is expected that this situation might become worse in the coming years [6][7][8]. While the total world precipitation has not significantly changed in the last decade, the distribution of rainfall across the year and space has changed, implying relevant effects in biomes and agriculture lands [9].Farmers and agronomists are aware of the risk of their business, with an eye on possible changes on climate. Thus, in the literature we found many studies related to alternative management practices to face climate change [10,11]. Some effects, unfortunately, cannot be prevented, such as floods, storms and fires. For this reason, an accurate evaluation and prediction of damages is of major importance for farmers and stakeholders in the agriculture business, including insurance companies. Many farmers and agriculture corporations invest in insurance in case they lose part or the totality of t...

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confidence: 99%

Crop Loss Evaluation Using Digital Surface Models from Unmanned Aerial Vehicles Data

Millán

Rankine²,

Sánchez‐Azofeifa

2020

Remote Sensing

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“…UAVs have become a common tool in precision agriculture [66,67]. Thanks to their affordability, user-friendliness and versatility, UAVs are often the primary choice for fast and precise in situ remote sensing or survey operations.…”

Section: Uavs Remote Sensing Techniques and Sensorsmentioning

confidence: 99%

Drone and sensor technology for sustainable weed management: a review

Esposito

Crimaldi

Cirillo

et al. 2021

Chem. Biol. Technol. Agric.

137

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Weeds are amongst the most impacting abiotic factors in agriculture, causing important yield loss worldwide. Integrated Weed Management coupled with the use of Unmanned Aerial Vehicles (drones), allows for Site-Specific Weed Management, which is a highly efficient methodology as well as beneficial to the environment. The identification of weed patches in a cultivated field can be achieved by combining image acquisition by drones and further processing by machine learning techniques. Specific algorithms can be trained to manage weeds removal by Autonomous Weeding Robot systems via herbicide spray or mechanical procedures. However, scientific and technical understanding of the specific goals and available technology is necessary to rapidly advance in this field. In this review, we provide an overview of precision weed control with a focus on the potential and practical use of the most advanced sensors available in the market. Much effort is needed to fully understand weed population dynamics and their competition with crops so as to implement this approach in real agricultural contexts.

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“…In a review study by Mogili [ 8 ], the researchers reported that drones can be used in pesticide and fertilizer application, so that humans do not come in contact with the some of these pesticides, which are harmful, and are gradually being phased out. Drones can also function as water sprinkling systems [ 8 , 36 ].…”

Section: Related Literaturementioning

confidence: 99%

Smart and Climate-Smart Agricultural Trends as Core Aspects of Smart Village Functions

Adesipo

Fadeyi

Kuča

et al. 2020

Sensors

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Attention has shifted to the development of villages in Europe and other parts of the world with the goal of combating rural–urban migration, and moving toward self-sufficiency in rural areas. This situation has birthed the smart village idea. Smart village initiatives such as those of the European Union is motivating global efforts aimed at improving the live and livelihood of rural dwellers. These initiatives are focused on improving agricultural productivity, among other things, since most of the food we eat are grown in rural areas around the world. Nevertheless, a major challenge faced by proponents of the smart village concept is how to provide a framework for the development of the term, so that this development is tailored towards sustainability. The current work examines the level of progress of climate smart agriculture, and tries to borrow from its ideals, to develop a framework for smart village development. Given the advances in technology, agricultural development that encompasses reduction of farming losses, optimization of agricultural processes for increased yield, as well as prevention, monitoring, and early detection of plant and animal diseases, has now embraced varieties of smart sensor technologies. The implication is that the studies and results generated around the concept of climate smart agriculture can be adopted in planning of villages, and transforming them into smart villages. Hence, we argue that for effective development of the smart village framework, smart agricultural techniques must be prioritized, viz-a-viz other developmental practicalities.

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A review on the use of drones for precision agriculture

Cited by 112 publications

References 16 publications

Crop Loss Evaluation Using Digital Surface Models from Unmanned Aerial Vehicles Data

Crop Loss Evaluation Using Digital Surface Models from Unmanned Aerial Vehicles Data

Drone and sensor technology for sustainable weed management: a review

Smart and Climate-Smart Agricultural Trends as Core Aspects of Smart Village Functions

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