A drone-based platform coupled with digital, multispectral, and/or hyperspectral sensors presents a multitude of opportunities and challenges for monitoring, investigating, and mapping cereal crops at the farm level. Some issues like complex drone regulations and unavailability of insurance policy for drones, however, are debatable and actual conditions may differ country by country [19
]. Similarly, privacy concerns may be relaxed as the agriculture area generally lies in remote places with no nearby settlements. Additionally, an endurance of around 2 h with a fixed-wing drone will be fine for agriculture scouting at the farm level. A list of opportunities and challenges for cereal crop investigation and modeling are summarized in Table 7
and each of them is reviewed and discussed.
The flying height of small drones is usually in the range of tens of meters to a few hundred meters. This allows the platform to provide ultra-high-resolution images [49
]. Spatial resolution smaller than 10 cm is generally obtained, though 1 cm spatial resolution [131
] has become quite common in recent times. The available resolution is very much suitable for monitoring and mapping of smaller farms in low-income countries [13
]. Further, the ultra-high spatial resolution images not only allow to get individual crop level information but also can permit the information on soil characteristics, e.g., soil moisture [89
Due to the short lifespan of cereal crops, monitoring, and intervention of the farms with the extremely high temporal resolution is of paramount importance. Monitoring cereal crop characteristics at each/particular phenological stage is crucial. Moreover, the revisit time of very high resolution (VHR) satellite images may not align with the strict crop monitoring schedule. However, a drone can be flown at the strict monitoring schedule. Furthermore, this platform also permits to fly at a chosen time of the day which will enable to reduce the shadowing effect. Additionally, the lower operational cost of drones also supports capturing data with high temporal resolution.
Though optical satellite images were common in the past, cloud cover [14
] is a major hindrance to their applications for cereal crop investigation, especially during the monsoon and the winter. Cloud cover is prevalent in the monsoon when paddy rice is grown in the larger area of Asia. Similarly, fog and haze are quite common during the winter wheat growing season. Hence, these images limit the investigation of cereal crops. Satellite-based active sensors (e.g., SAR) can be an alternative data source, however, the spatial resolution of such images currently available is unsuitable for the smallholders’ plot-level farm investigation. Hence, drone images prove to be the most promising platform considering the operational flying height of small drones that permits the acquisition of cloud-free ultra-high spatial resolution images.
Three-dimensional point could is crucial for estimating plant height, crop phenology, and biomass [135
]. A better estimation of these parameters can be achieved with a high-density point cloud obtainable from drone images and LiDAR than aerial imageries and stereo satellite images. Though the accuracy of the point cloud from LiDAR is tremendous, its cost is far greater than that from the drone images [174
]. Moreover, monitoring smaller farms with manned aircraft and satellite images is not practical due to associated cost and necessity for high temporal resolution.
Planning and operating a consumer drone have become straight forward because of the high level of automation in planning, takeoff, and landing. With high penetration of mobile networks [175
], smartphones-based freely available flight planning software have made planning a drone flight reachable to almost all, and are tremendously easy as they now offer intuitive graphical user interface (GUI). With some kind of training provided to the farmers, they could download and process images with freely available GUI based software, e.g., PrecisionMapper (https://www.precisionhawk.com/precisionanalytics
) and WebODM (https://www.opendronemap.org/webodm/
Cereal crop monitoring, investigation, and yield estimation using satellite-based EO images are challenging due to smaller farm sizes in low-income countries [13
] and a short cereal crop lifespan. Whilst the spatial and temporal resolutions of freely available satellite images prohibit their applicability at the farm level, the cost of very high-resolution (VHR) satellite images bars temporal monitoring of such crops [176
]. Moreover, the farmers lack the resources to use VHR remote sensing images, images from piloted aircraft, or any sophisticated and expensive systems like LiDAR. Further, the cost of operation for consumer drones are low, are easy to operate, and provide ultra-high spatial and temporal resolution images.
Cooperative farming is becoming popular [177
]. This practice not only helps to cut the cost of inputs and gets government subsidies but also support sharing resources, knowledge, and find marketing their product much appropriate. As a result of the low operational cost and easy to operate the system for monitoring, mapping, and yield estimation with consumer drones, the cooperatives can manage to pay for and employ the technology to scout their cereal farms, make informed decisions, and get enhanced productivity. Henceforth, the drone-based system is scalable with a cooperative farming system. In one of our research, plant height measurements were performed by farmers’ cooperative members [137
]. Thus, the farmers’ cooperatives can appropriately employ the drones and operate it themselves as well, if proper training is prearranged.
The drone data solution represents the five stages of the data lifecycle i.e., acquisition, analysis, storage, sharing, and visualization. The advancements of sensor technologies (e.g., Table 3
) have enabled drones to acquire a large amount of data (e.g., image, video, radio signals, emission gases, etc.) with very high spatial and temporal resolution. At the same time, IoT-based low-cost sensors continuously present agronomic and environmental parameters, which can be assimilated with the drone-platforms. The advancement of big data processing environments (e.g., Microsoft Azure, Google Earth Engine (GEE) and machine learning methods (e.g., Table 6
) offer a unique opportunity to process these datasets in real-time for better decision making [178
Natural disasters (such as floods, drought, cyclone, etc.), pandemic and zoonotic diseases (such as COVID-19, bacteria, parasites, and fungi) and occasional but disruptive insects like Desert Locust (which appears at the beginning of 2020 in Africa and entered Nepal in July 2020) can damage the crop and reduce the yield. Precise and accurate information is mandatory for both the farmers and the insurance companies for a fair and accurate payout. Traditional surveying methods like images from an expensive aircraft or an inaccurate measurement produced by “eyeballing”, are no longer practical in these conditions. On the contrary, a single drone flight can provide a rapid, easy, and accurate assessment for crop insurance adjustment procedures and payout.
Further, crop simulation models require very comprehensive information on crop fields to accurately predict the produce. With drone’s technology, the crop insurance companies can get supplementary information like tentative sowing date, planting density, row spacing, management regimes, precise growth, and health among other information at the plot-level. Farmers plant/sow different crops than what had been committed to crop insurance companies at the time of issuance of the insurance policy. With the application of drone’s technology, geographic information system (GIS), and digital cadastral data, it is now possible to detect fraudulent claims by the farmers, measure discrepancies and validate the compliance level stated/agreed in the policy [180
]. Hence, a drone-based crop scouting system can be a great solution for fair and accurate payout to the insured farmers, by the crop insurance companies and this would help the companies to stay competitive and increase operational efficiency.
Consumer drones have limited payload capacity. Thus, they cannot afford to accommodate heavy professional quality sensors. Innovations in sensor technology have led to the development of lightweight sensors that can fit on consumer drones (Table 3
). However, the cost of such sensors is still out of reach for many users, especially in low-income countries. Nonetheless, the cost of light-weight sensors can be anticipated to drop in the coming years as did the price of the consumer drones.
The high cost of high spectral resolution (hyperspectral) sensors is a major limiting factor to the widespread use of drones in the agriculture sector. Thus, many users were limited to use a consumer-grade digital camera for many applications [66
]. Consumer digital cameras suffer from overlapping spectral bands [108
], pixel interpolation, and high vignetting effect [109
]. Thus, these sensors may find limited applications, for example, in disease detection, crop health monitoring, and weeds identification. The use of multispectral sensors could bridge the gap for many, as a cost point of view.
Drone images are free from the cloud cover issues. However, other atmospheric phenomena like haze, fog, precipitation, and strong winds may negatively impact its usage [181
]. These issues can, however, be tackled with proper flight planning and management, and to some extent with the help of image pre-processing techniques.
Flight endurance of multi-rotor drones which present flexibility in sensor installation, is still a major challenge for monitoring larger acreage. However, the lager acreage can be inspected and mapped with the help of fixed-wing drones. Nonetheless, only light-weight sensors can be installed on them.
Though consumer drones are cheaper, they come with consumer-grade optical sensors, which are unsuitable for many agricultural applications. A hyperspectral sensor offers tremendous opportunities for cereal crop analysis. However, they are unaffordable to many. Additionally, the weight of such sensors is still on the heavier side which requires larger carrier drones, increasing the ownership cost. Thus, the initial cost of ownership is still a major hurdle in the wider application of drones. We can expect to drop the cost as well as the size and the weight of the sensors in the future.
For proper utilization of the technologies, confidence and knowledge of the farmers need to be boosted. Motivating and capacity building of wide diversity of farmers may be the main challenge towards real applications of drones in agriculture. While younger generations are selecting farming as an occupation, the job can be completed comparatively with ease. Nonetheless, training for implementing the entire workflow is of paramount importance.
Though consumer drones are easy to operate, their repair and maintenance require technical knowledge and experience. Further, the unavailability of parts (for replacement in case of damage) may be another issue at many places in the world.