Comparative Study of Artificial Intelligence Techniques for the Diagnosis of Chronic Nerve Diseases

Farming is essential to the long-term viability of any economy. It differs in each country, but it is essential for long-term economic success. Only a few of the agricultural industry’s issues include a lack of suitable irrigation systems, weeds, and plant monitoring concerns as a consequence of efficient management in distinct open and closed zones for crop and plant treatment. The objective of this work is to carry out a study on the use of artificial intelligence and computer vision methods for diagnosis of diseases in agro sectors in the context of agribusiness, demonstrating the feasibility of using these techniques as tools to support automation and obtain productivity gains in this sector. During the literary analysis, it was determined that technology could improve efficiency, hence decreasing these types of concerns. Given the consequences of a wrong diagnosis, diagnosis is work that requires a high level of precision. Fuzzy cognitive maps were shown to be the most efficient method of utilizing bibliographically reviewed preferences, which led to the consideration of neural networks as a second option because this technique is the most robust in terms of the qualifying criteria of the data stored in databases.

Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors

Environmental conditions are very changeable; fluctuations in temperature, precipitation, illumination intensity, and other factors can decrease a plant productivity and crop. The remote sensing of plants under these conditions is the basis for the protection of plants and increases their survivability. This problem can be solved through measurements of plant reflectance and calculation of reflectance indices. Reflectance indices are related to the vegetation biomass, specific physiological processes, and biochemical compositions in plants; the indices can be used for both short-term and long-term plant monitoring. In our review, we considered the applications of reflectance indices in plant remote sensing. In Optical Methods and Platforms of Remote Sensing of Plants, we briefly discussed multi- and hyperspectral imaging, including descriptions of multispectral and hyperspectral cameras with different principles and their efficiency for the remote sensing of plants. In Main Reflectance Indices, we described the main reflectance indices, including vegetation, water, and pigment reflectance indices, as well as the photochemical reflectance index and its modifications. We focused on the relationships of leaf reflectance and reflectance indices to plant biomass, development, and physiological and biochemical characteristics. In Problems of Measurement and Analysis of Reflectance Indices, we discussed the methods of the correction of the reflectance indices that can be used for decreasing the influence of environmental conditions (mainly illumination, air, and soil) and plant characteristics (orientation of leaves, their thickness, and others) on their measurements and the analysis of the plant remote sensing. Additionally, the variability of plants was also considered as an important factor that influences the results of measurement and analysis.

Efficient Drone-Based Rare Plant Monitoring Using a Species Distribution Model and AI-Based Object Detection

Monitoring rare plant species is used to confirm presence, assess health, and verify population trends. Unmanned aerial systems (UAS) are ideal tools for monitoring rare plants because they can efficiently collect data without impacting the plant or endangering personnel. However, UAS flight planning can be subjective, resulting in ineffective use of flight time and overcollection of imagery. This study used a Maxent machine-learning predictive model to create targeted flight areas to monitor Geum radiatum, an endangered plant endemic to the Blue Ridge Mountains in North Carolina. The Maxent model was developed with ten environmental layers as predictors and known plant locations as training data. UAS flight areas were derived from the resulting probability raster as isolines delineated from a probability threshold based on flight parameters. Visual analysis of UAS imagery verified the locations of 33 known plants and discovered four previously undocumented occurrences. Semi-automated detection of plant species was explored using a neural network object detector. Although the approach was successful in detecting plants in on-ground images, no plants were identified in the UAS aerial imagery, indicating that further improvements are needed in both data acquisition and computer vision techniques. Despite this limitation, the presented research provides a data-driven approach to plan targeted UAS flight areas from predictive modeling, improving UAS data collection for rare plant monitoring.

Problematic plant monitoring in Hopewell Culture National Historical Park: 2008–2019

Managers are challenged with the impact of problematic plants, including exotic, invasive, and pest plant species. Information on the cover and frequency of these plants is essential for developing risk-based approaches to managing these species. Based on surveys conducted in 2008, 2011, 2015, and 2019, Heartland Network staff and contractors identified a cumulative total of 51 potentially problematic plant species in Hopewell Culture National Historical Park. Of the 37 species found in 2019, we characterized 7 as very low frequency, 9 as low frequency, 17 as medium frequency, and 4 as high frequency. Of these, midpoint cover estimates of 2 medium frequency and 2 high frequency species exceeded the 10-acre threshold. Because of the number, extent, and cover of problematic plants in the park and the small park size, control efforts should focus on treating high priority species across the entire park. High priority species may include plant species capable of rapid spread, species at low population levels, and species which can effectively be controlled.