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Crop Disease and Pest Identification
What is a Crop Disease and Pest Identification System?
Correctly detecting and identifying diseases and pests can save large proportions of crop yields every year. With artificial intelligence models, however, farmers no longer need to rely on expert human evaluations to perform accurate crop disease and pest identification.
These systems come in the form of phone-based apps or in drones and robots.
Why Is It Important to Have a Good Crop Disease and Pest Identification System?
Crop disease and pest identification saves, on average, about a quarter of yearly crop yields. These AI systems, then, save livelihoods and contribute to local and global food security. Further, they do this faster than traditional methods.
Accurate identification systems also decrease the costs and deleterious effects of disease and pest misidentification.
What Internal Data Should I Have for a Good Crop Disease and Pest Identification System?
An effective crop disease and pest identification system uses the farmer’s crop and property boundary data. This information ensures, first, that desired crops are protected and, second, helps identify local disease strains and pests. Additionally, this boundary data can ensure that remote identification systems do not trespass into neighboring farms.
These AI systems also include the farm’s crop disease and pest history, especially fertilizers and herbicides previously used and currently in stock. The identification system primarily uses this information to suggest pest and disease management solutions. However, this information also suggests alternate disease and pest identification. Some disease strains, for example, are chemical resistant, and the systems should tailor their recommended management solutions to this information.
Employee identification data should also be included so that unauthorized individuals cannot breach the farm’s database.
What External Data Is Essential for a Good Identification System?
Good crop disease and pest identification systems incorporate a large amount of continuously updating external data on diseases, pests, and management solutions. Diseases and pests may be further divided into common and uncommon varieties.
Other important external data include news and weather history and forecasts. Weather, obviously, can make plants vulnerable to certain diseases. The news, meanwhile, can warn farmers and their AI systems that, for example, researchers identified a mutant disease strain impacting the crops they grow.
What External Data May Prove Useful for a Good Identification System?
Additional external data may include other agricultural data, such as soil quality. A good system can use this kind of data measured by IoT sensors and incorporate it into an inclusive IoT farm ecosystem.
Industry data is another good resource, especially for farmers who may want to upgrade their crop disease and pest identification systems.
What Are the Main Challenges of this Use Case?
The main challenges of building and maintaining a strong crop disease and pest identification system include price, ease of use, and the need to constantly manage and update the system. Obviously, new plant diseases mutate all the time, and rodents and insects move into different parts of the world due to climate change, deforestation, and so on. Price and ease of use, however, are major roadblocks to adopting this technology, especially among subsistence farmers in many parts of the world. Making this technology available through mobile phone apps, however, can help these farmers.
Security is also a concern, especially for larger farms which use IoT-connected robots to track plant health.
Interesting Case Studies and Blogs to Look Into
ICRISAT: Artificial Intelligence to Track Pests and Diseases in India
NCBI: IoT-Based Strawberry Disease Prediction System for Smart Farming
Tangible Examples of Impact
Currently, the removal of infected trees is carried out after the visual confirmation of symptoms of HLB by trained inspectors teams. However, visual inspection takes a long time and is not sufficiently accurate. It is expensive and tedious.
VetorGEO’s research team has been developing a new detection method utilizing Agrowing’s high resolution 4 and 10 band multispectral sensors.
Connected Categories
Connected Datasets
LANDFIRE.US_120FBFM13
The LANDFIRE fuel data describe the composition and characteristics of both surface fuel and canopy fuel. Specific products include fire behavior fuel models, canopy bulk density (CBD), canopy base height (CBH), canopy cover (CC), canopy height (CH), and fuel loading models (FLMs). These data may be implemented within models to predict the behavior and effects of wildland fire. These data are useful for strategic fuel treatment prioritization and tactical assessment of fire behavior and effects. DATA SUMMARY: Thirteen typical surface fuel arrangements or “collections of fuel properties” (Anderson 1982) were described to serve as input for Rothermel’s mathematical surface fire behavior and spread model (Rothermel 1972). These fire behavior fuel models represent distinct distributions of fuel loadings found among surface fuel components (live and dead),
LANDFIRE.AK_130CFFDRS
The LANDFIRE fuel data describe the composition and characteristics of both surface fuel and canopy fuel. Specific products include fire behavior fuel models, canopy bulk density (CBD), canopy base height (CBH), canopy cover (CC), canopy height (CH), and fuel loading models (FLMs). These data may be implemented within models to predict the behavior and effects of wildland fire. These data are useful for strategic fuel treatment prioritization and tactical assessment of fire behavior and effects. DATA SUMMARY: These fuel types have been defined “as an identifiable association of fuel elements of distinctive species, form, size, arrangement, and continuity that will exhibit characteristic fire behavior under defined burning conditions” (Pyne, Andrews and Laven, 1996). The Canadian Forest Fire Danger Rating System (CFFDRS) arranges fuel types into
LANDFIRE.US_130CC
The LANDFIRE fuel data describe the composition and characteristics of both surface fuel and canopy fuel. Specific products include fire behavior fuel models, canopy bulk density (CBD), canopy base height (CBH), canopy cover (CC), canopy height (CH), and fuel loading models (FLMs). These data may be implemented within models to predict the behavior and effects of wildland fire. These data are useful for strategic fuel treatment prioritization and tactical assessment of fire behavior and effects. DATA SUMMARY: Canopy cover (CC) describes percent cover of tree canopy in a stand. A spatially-explicit map of canopy cover supplies information for fire behavior models such as FARSITE (Finney 1998) to determine surface fuel shading for calculating dead fuel moisture and for calculating wind reductions. In FARSITE, canopy characteristics
LANDFIRE.US_110PRS
Broad-scale alterations of historical fire regimes and vegetation dynamics have occurred in many landscapes in the U.S. through the combined influence of land management practices, fire exclusion, ungulate herbivory, insect and disease outbreaks, climate change, and invasion of non-native plant species. The LANDFIRE Project produces maps of historical fire regimes and vegetation conditions using the disturbance dynamics model VDDT. The LANDFIRE Project also produces maps of current vegetation and measurements of current vegetation departure from simulated historical reference conditions. These maps support fire and landscape management planning outlined in the goals of the National Fire Plan, Federal Wildland Fire Management Policy, and the Healthy Forests Restoration Act. Data Summary: The Historical Percent of Replacement Severity Fires data layer quantifies the percent of all fires that
Modeled potential presence of Ceratocystis luhuohia across Hawaiian Islands
Hawai’i’s most widespread native tree, ‘ohi’a lehua (Metrosideros polymorpha), has been dying across large areas of Hawai’i Island mainly due to two fungal pathogens (Ceratocystis lukuohia and Ceratocystis huliohia) that cause a disease collectively known as Rapid ‘Ohi’a Death (ROD). Here we examine patterns of positive detections of C. lukuohia as it has been linked to the larger mortality events across Hawai’i Island. Our analysis compares the environmental range of C. lukuohia and its spread over time through the known climatic range and distribution of ‘ohi’a. This data set is a georeferenced raster file, containing the projected potential presence of C.lukuohia across the main Hawaiian Islands using climatic variables that varied consistently with C. lukuohia prevalence (Mean annual precipication and minimum temperature of coldest month)