Automatic Detection of Regions in Spinach Canopies Responding to Soil Moisture Deficit Using Combined Visible and Thermal Imagery

Abstract

Thermal imaging has been used in the past for remote detection of regions of canopy showing symptoms of stress, including water deficit stress. Stress indices derived from thermal images have been used as an indicator of canopy water status, but these depend on the choice of reference surfaces and environmental conditions and can be confounded by variations in complex canopy structure. Therefore, in this work, instead of using stress indices, information from thermal and visible light imagery was combined along with machine learning techniques to identify regions of canopy showing a response to soil water deficit. Thermal and visible light images of a spinach canopy with different levels of soil moisture were captured. Statistical measurements from these images were extracted and used to classify between canopies growing in well-watered soil or under soil moisture deficit using Support Vector Machines (SVM) and Gaussian Processes Classifier (GPC) and a combination of both the classifiers. The classification results show a high correlation with soil moisture. We demonstrate that regions of a spinach crop responding to soil water deficit can be identified by using machine learning techniques with a high accuracy of 97%. This method could, in principle, be applied to any crop at a range of scales.

Optimizing the Processing of UAV-Based Thermal Imagery

“…In this case, the images can be processed as multispectral images and can be aligned based on the RGB information. Otherwise, if an RGB and thermal camera have fixed positions relative to each other and are triggered to take images at the same moment, the RGB and thermal data can be co-registered based on a fixed transformation of one image pair [35], and thermal data can also be aligned along with and based on the RGB images. In most applications, however, thermal cameras are not equipped with an integrated RGB camera, or the simultaneous triggering of the RGB and thermal camera is difficult or practically not feasible.…”

A real-time phenotyping framework using machine learning for plant stress severity rating in soybean

“…There has been recent work in this regard to design, develop and deploy high efficiency methods/tools to quantify leaf surface damage [12] as well as plants response to pathogens [13]. Additionally, a number of approaches using imaging methods for phenotyping, such as fluorescence and spectroscopic imaging have been successful for stress-based phenotyping [14], high throughput machine vision systems that use image analysis for phenotyping Arabidopsis thaliana seedlings [15] and barley [16], hyperspectral imaging for drought stress identification in cereal [17], and a combination of digital and thermal imaging for detecting regions in spinach canopies that respond to soil moisture deficit [18] which have proven to be successful. However, a simple, user friendly framework is unavailable for the public to phenotype for IDC in soybean plants.…”

Significant Water Savings Can Be Made In Commercial Spinach Cropping Without Adverse Impacts on Crop Yield or Shelf Life

“…The use of thermal imaging has shown potential as a tool for estimating plant water status through measuring plant temperature, which is an indicator of stomatal aperture [12]. Stomatal responses occur prior to any change in plant water status making stomatal conductance a sensitive pre-symptomatic indicator of soil water deficit [13,14] and non-transpiring plants can be up to 4ºC hotter than their transpiring counterparts [15].…”

“…Stomatal responses occur prior to any change in plant water status making stomatal conductance a sensitive pre-symptomatic indicator of soil water deficit [13,14] and non-transpiring plants can be up to 4ºC hotter than their transpiring counterparts [15]. Although there has been a lot of work on the use of thermal imaging to identify water stress in a variety of crops [12,14], this technique has not, to our knowledge, been applied to leafy salad crops in the context of improving crop quality. The focus for this research is spinach, which has a large global market both as a salad and vegetable crop, with 867,728 Ha produced in 2011 [16].…”