Active optical sensor assessment of spider mite damage on greenhouse beans and cotton

Automated imaging coupled with AI-powered analysis accelerates the assessment of plant resistance to Tetranychus urticae

The two-spotted spider mite (TSSM), Tetranychus urticae, is among the most destructive piercing-sucking herbivores, infesting more than 1100 plant species, including numerous greenhouse and open-field crops of significant economic importance. Its prolific fecundity and short life cycle contribute to the development of resistance to pesticides. However, effective resistance loci in plants are still unknown. To advance research on plant-mite interactions and identify genes contributing to plant immunity against TSSM, efficient methods are required to screen large, genetically diverse populations. In this study, we propose an analytical pipeline utilizing high-resolution imaging of infested leaves and an artificial intelligence-based computer program, MITESPOTTER, for the precise analysis of plant susceptibility. Our system accurately identifies and quantifies eggs, feces and damaged areas on leaves without expert intervention. Evaluation of 14 TSSM-infested Arabidopsis thaliana ecotypes originating from diverse global locations revealed significant variations in symptom quantity and distribution across leaf surfaces. This analytical pipeline can be adapted to various pest and host species, facilitating diverse experiments with large specimen numbers, including screening mutagenized plant populations or phenotyping polymorphic plant populations for genetic association studies. We anticipate that such methods will expedite the identification of loci crucial for breeding TSSM-resistant plants.

Unmanned Aerial Vehicles (UAV) in Precision Agriculture: Applications and Challenges

Agriculture is the primary source of income in developing countries like India. Agriculture accounts for 17 percent of India’s total GDP, with almost 60 percent of the people directly or indirectly employed. While researchers and planters focus on a variety of elements to boost productivity, crop loss due to disease is one of the most serious issues they confront. Crop growth monitoring and early detection of pest infestations are still a problem. With the expansion of cultivation to wider fields, manual intervention to monitor and diagnose insect and pest infestations is becoming increasingly difficult. Failure to apply on time fertilizers and pesticides results in more crop loss and so lower output. Farmers are putting in greater effort to conserve crops, but they are failing most of the time because they are unable to adequately monitor the crops when they are infected by pests and insects. Pest infestation is also difficult to predict because it is not evenly distributed. In the recent past, modern equipment, tools, and approaches have been used to replace manual involvement. Unmanned aerial vehicles serve a critical role in crop disease surveillance and early detection in this setting. This research attempts to give a review of the most successful techniques to have precision-based crop monitoring and pest management in agriculture fields utilizing unmanned aerial vehicles (UAVs) or unmanned aircraft. The researchers’ reports on the various types of UAVs and their applications to early detection of agricultural diseases are rigorously assessed and compared. This paper also discusses the deployment of aerial, satellite, and other remote sensing technologies for disease detection, as well as their Quality of Service (QoS).

Verification of Fractional Vegetation Coverage and NDVI of Desert Vegetation via UAVRS Technology

Desertification control and scientific evaluation of desert ecosystem sustainability are important issues for countries along the Silk Road Economic Belt. Fractional vegetation coverage (FVC) is used as a quantitative indicator to describe the vegetation coverage of desert ecosystems. Although satellite remote sensing technology has been widely used to retrieve FVC at the regional and global scale, the authenticity evaluation of the inversion results has been flawed. To gain insight into the composition, structure and changes of desert vegetation, it is important to assess the accuracy of FVC and explore the relationship between FVC and meteorological factors. Therefore, we adopted unmanned aerial vehicle remote sensing (UAVRS) technology to verify the inversion results and analyse the practicability of MODIS-NDVI (where NDVI = normalized difference vegetation index) products in desert areas. To provide a new method for the estimation of vegetation coverage in the natural state, the relationships between vegetation coverage and four meteorological factors, namely, land surface temperature, temperature, precipitation and evaporation were analysed. The results showed that using the original MODIS-NDVI data product with a spatial resolution of 250 m to invert vegetation coverage is practical in desert areas (coefficient of determination (R2) = 0.83, root mean square error (RMSE) = 0.052, normalized root mean square error (NRMSE) = 42.94%, mean absolute error (MAE) = 0.007) but underestimates vegetation coverage in the study area. MODIS-NDVI data products are different from the real NDVI in the study area. Correcting MODIS-NDVI data products can effectively improve the accuracy of the inversion. When extracting vegetation coverage in this area, the scale has little effect on the results. There is a significant correlation between precipitation, evaporation and FVC in the area, but the interaction of temperature and land surface temperature with precipitation and evaporation also has a considerable impact on FVC, and evaporation has a substantial impact on FVC values inverted from MODIS-NDVI data (FVCM), When exploring the relationship between vegetation coverage and meteorological elements, if vegetation coverage is retrieved from MODIS-NDVI data products or MODIS-NDVI data, when considering temperature and precipitation, the effect of evaporation should also be considered. In addition, meteorological factors can be used to predict FVC (R2 = 0.7364, RMSE = 0.0623), which provides a new method for estimating FVC in areas with less manual intervention.

Drones: Innovative Technology for Use in Precision Pest Management

Arthropod pest outbreaks are unpredictable and not uniformly distributed within fields. Early outbreak detection and treatment application are inherent to effective pest management, allowing management decisions to be implemented before pests are well-established and crop losses accrue. Pest monitoring is time-consuming and may be hampered by lack of reliable or cost-effective sampling techniques. Thus, we argue that an important research challenge associated with enhanced sustainability of pest management in modern agriculture is developing and promoting improved crop monitoring procedures. Biotic stress, such as herbivory by arthropod pests, elicits physiological defense responses in plants, leading to changes in leaf reflectance. Advanced imaging technologies can detect such changes, and can, therefore, be used as noninvasive crop monitoring methods. Furthermore, novel methods of treatment precision application are required. Both sensing and actuation technologies can be mounted on equipment moving through fields (e.g., irrigation equipment), on (un)manned driving vehicles, and on small drones. In this review, we focus specifically on use of small unmanned aerial robots, or small drones, in agricultural systems. Acquired and processed canopy reflectance data obtained with sensing drones could potentially be transmitted as a digital map to guide a second type of drone, actuation drones, to deliver solutions to the identified pest hotspots, such as precision releases of natural enemies and/or precision-sprays of pesticides. We emphasize how sustainable pest management in 21st-century agriculture will depend heavily on novel technologies, and how this trend will lead to a growing need for multi-disciplinary research collaborations between agronomists, ecologists, software programmers, and engineers.