MACHINE VISION EXTRACTED PLANT MOVEMENT FOR EARLY DETECTION OF PLANT WATER STRESS

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations.

Experimental Validation of an Adaptive Controller for Manipulators on a Dynamic Platform

This paper reports on laboratory and field experimental results for controlled robotic manipulators operating on moving platforms with unmodeled dynamics. The aim is to validate theoretical predictions for the dependence on control parameters of an adaptive control strategy. In addition, the results provide insight into different discretizations of the continuous-time formulation, suggesting the most suitable discretization scheme for hardware implementation. The second set of experimental results, obtained from an implementation of the control framework for synchronization and consensus in networks of robotic manipulators, similarly validate theoretical predictions on the sensitivity to network communication delays.

A Phenotype-Based Approach for the Substrate Water Status Forecast of Greenhouse Netted Muskmelon

Cultivation substrate water status is of great importance to the production of netted muskmelon (Cucumis melo L. var. reticulatus Naud.). A prediction model for the substrate water status would be beneficial in irrigation schedule guidance. In this study, the machine learning random forest model was used to forecast plant substrate water status given the phenotypic traits throughout the muskmelon growing season. Here, two varieties of netted muskmelon, "Wanglu" and "Arus", were planted in a greenhouse under four substrate water treatments and their phenotypic traits were measured by taking the images within the visible and near-infrared spectrums, respectively. Results showed that a simplified model outperformed the original model in forecasting speed, while it only uses the top five most significant contribution traits. The forecast accuracy reached up to 77.60%, 94.37%, and 90.01% for seedling, vine elongation, and fruit growth stages, respectively. Combining the imaging phenotypic traits and machine learning technique would provide a robust forecast of water status around the plant root zones.

Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques

Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.

A Hybrid Approach for Improving Image Segmentation: Application to Phenotyping of Wheat Leaves

In this article we propose a novel tool that takes an initial segmented image and returns a more accurate segmentation that accurately captures sharp features such as leaf tips, twists and axils. Our algorithm utilizes basic a-priori information about the shape of plant leaves and local image orientations to fit active contour models to important plant features that have been missed during the initial segmentation. We compare the performance of our approach with three state-of-the-art segmentation techniques, using three error metrics. The results show that leaf tips are detected with roughly one half of the original error, segmentation accuracy is almost always improved and more than half of the leaf breakages are corrected.

Monitoring and screening plant populations with combined thermal and chlorophyll fluorescence imaging

Thermal and chlorophyll fluorescence imaging are powerful tools for the study of spatial and temporal heterogeneity of leaf transpiration and photosynthetic performance. The relative advantages and disadvantages of these techniques are discussed. When combined, they can highlight pre-symptomatic responses not yet apparent in visual spectrum images and provide specific signatures for diagnosis of distinct diseases and abiotic stresses. In addition, their use for diagnosis and for selection for stomatal or photosynthetic mutants, these techniques can be applied for stress tolerance screening. For example, rapid screening for stomatal responses can be achieved by thermal imaging, while, combined with fluorescence imaging to study photosynthesis, they can potentially be used to derive leaf water use efficiency as a screening parameter. A particular advantage of imaging is that it allows continuous automated monitoring of dynamic spatial variation. Examples of applications include the study of growth and development of plant lines differing in stress resistance, yield, circadian clock-controlled responses, and the possible interactions between these parameters. In the future, such dual-imaging systems could be extended with complementary techniques such as hyperspectral and blue-green fluorescence imaging. This would result in an increased number of quantified parameters which will increase the power of stress diagnosis and the potential for screening of stress-tolerant genotypes.

Tuning the pores: towards engineering plants for improved water use efficiency

The management of limited fresh water resources is a major challenge facing society in the 21st century. The agricultural sector accounts for more than two-thirds of human water withdrawal and is therefore a prime area to implement a more rational water use. Environmental stresses are a major factor limiting stable food production. Given the growing shortage of available water for crops this will be an emerging factor in international agricultural economy. The most environmentally friendly and durable solution to the problem of water shortage is to complement more efficient irrigation approaches with crops with optimal water use efficiency, achieved either through genetic engineering or conventional breeding, combined with high yields.