Remote Sensing Data to Detect Hessian Fly Infestation in Commercial Wheat Fields

Toward integrated pest management in bivalve aquaculture

Pests of bivalve aquaculture are a challenging problem that can reduce productivity, profitability and sustainability. A range of pest management approaches have been developed for bivalve aquaculture, but a general absence of guiding frameworks has limited the scale and permanency of implementation. Applying principles of 'integrated pest management' (IPM) could change this paradigm to improve economic and environmental outcomes. We reviewed existing research and tools for pest management in bivalve aquaculture, with studies grouped under five pillars of IPM: pest ecology (25 studies), bioeconomic cost-benefits (4 studies), continual monitoring (17 studies), proactive prevention (32 studies) and reactive control (65 studies). This body of knowledge, along with insights from terrestrial agriculture, provide a strong foundation for developing and implementing IPM in bivalve aquaculture. For example, IPM principles have been applied by a regional collective of oyster farmers in the US Pacific Northwest to optimize pesticide application and search for other options to control problematic burrowing shrimps. However, IPM has not yet been broadly applied in aquaculture, and data gaps and barriers to implementation need to be addressed. Priorities include establishing meaningful pest-crop bioeconomic relationships for various bivalve farming systems and improving the efficacy and operational scale of treatment approaches. An IPM framework also could guide potential step-change improvements through directing selective breeding for resistance to pests, development of bespoke chemical control agents, applying emerging technologies for remote surveillance and farm management, and regional alignment of management interventions. © 2022 Society of Chemical Industry.

A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping

During the last decade, there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant. High throughput phenotyping (HTP) involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing. Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain. More specifically, several root phenotyping tools are discussed to study the plant's hidden half and an area long neglected. However, the use of these HTP technologies produces big data sets that impede the inference from those datasets. Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions. These are interdisciplinary approaches for data analysis using probability, statistics, classification, regression, decision theory, data visualization, and neural networks to relate information extracted with the phenotypes obtained. These techniques use feature extraction, identification, classification, and prediction criteria to identify pertinent data for use in plant breeding and pathology activities. This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms. We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls. Overall, this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions. Finally, we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.

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).

Ecological phylogeography and coalescent models suggest a linear population expansion of Anastrepha fraterculus (Diptera: Tephritidae) in southern South America

This work is a first approach to an integrated view of the genetics, ecology and dispersion patterns of Anastrepha fraterculus in southern South America. We studied the association of genetic variation with geographical patterns and environmental variables to provide insight into the crucial factors that drive the structure and dynamics of fly populations. Data from a 417 bp mitochondrial COII gene fragment from seven Argentinian populations and one South Brazilian population (from five ecoregions grouped in three biomes) were used to identify population clusters using a model-based Bayesian phylogeographical and ecological clustering approach. The sequences were also analysed under a coalescent model to evaluate historical demographic changes. We identified 19 different haplotypes and two clusters differing in all the environmental covariables. The assumption of neutral evolution and constant population size was rejected, and the population growth parameters suggested a linear population expansion starting 2500 years before present. The most likely ancestral location is Posadas, from where A. fraterculus would have expanded southwards and westwards in Argentina. This result is consistent with Holocene changes and anthropic factors related to the expansion of the Tupí–Guaraní culture, 3000–1500 years before present.

Phyllobilins from Senescence-Associated Chlorophyll Breakdown in the Leaves of Basil (Ocimum basilicum) Show Increased Abundance upon Herbivore Attack

In view of the common use of the herb basil (Ocimum basilicum) in nutrition and in phytomedicine, the contents of its leaves are of obvious interest. In extracts of fresh yellowish-green basil leaves, phyllobilins (PBs), which are bilin-type catabolites of chlorophyll (Chl), were detected using high-performance liquid chromatography (HPLC). Two such PBs, provisionally named Ob-nonfluorescent chlorophyll catabolite (NCC)-40 and Ob-YCC-45, exhibited previously unknown structures that were delineated by a thorough spectroscopic characterization. When basil leaves were infested with aphids or thrips or underwent fungal infections, areas with chlorosis were observed. HPLC analyses of the infested parts of leaves compared to those of the healthy parts showed a significant accumulation of PBs in the infested areas, demonstrating that the senescence-associated pheophorbide a oxygenase/phyllobilin (PAO/PB) pathway is activated by herbivore feeding and fungal infection.

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

The use of satellites to monitor crops and support their management is gathering increasing attention. The improved temporal, spatial, and spectral resolution of the European Space Agency (ESA) launched Sentinel-2 A + B twin platform is paving the way to their popularization in precision agriculture. Besides the Sentinel-2 A + B constellation technical features the open-access nature of the information they generate, and the available support software are a significant improvement for agricultural monitoring. This paper was motivated by the challenges faced by researchers and agrarian institutions entering this field; it aims to frame remote sensing principles and Sentinel-2 applications in agriculture. Thus, we reviewed the features and uses of Sentinel-2 in precision agriculture, including abiotic and biotic stress detection, and agricultural management. We also compared the panoply of satellites currently in use for land remote sensing that are relevant for agriculture to the Sentinel-2 A + B constellation features. Contrasted with previous satellite image systems, the Sentinel-2 A + B twin platform has dramatically increased the capabilities for agricultural monitoring and crop management worldwide. Regarding crop stress monitoring, Sentinel-2 capacities for abiotic and biotic stresses detection represent a great step forward in many ways though not without its limitations; therefore, combinations of field data and different remote sensing techniques may still be needed. We conclude that Sentinel-2 has a wide range of useful applications in agriculture, yet still with room for further improvements. Current and future ways that Sentinel-2 can be utilized are also discussed.

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.