Evaluating Reflected GPS Signal as a Potential Tool for Cotton Irrigation Scheduling

Accurate soil moisture content measurements are vital to precision irrigation management. Remote sensing using the microwave spectrum (such as GPS signals) has been used for measuring large area soil moisture contents. In our previous work, we estimated surface soil moisture contents for bare soil using a GPS Delay Mapping Receiver (DMR) developed by NASA. However, the effect of vegetation was not considered in these studies. Hence the objectives of this study were to: 1) investigate the feasibility of using DMR to determine soil moisture content in cotton production fields; 2) evaluate the attenuation effect of vegetation (cotton) on reflected GPS signal. Field experiments were conducted during the 2013 and 2014 growing seasons in South Carolina. GPS antennas were mounted at three heights (1.6, 2.7, and 4.2 m) over cotton fields to measure reflected GPS signals (DMR readings). DMR readings, soil core samples, and plant measurements were collected about once a week and attenuation effect of plant canopy was calculated. Results showed that DMR was able to detect soil moisture changes within one week after precipitation events that were larger than 25 mm per day. However, the DMR readings were poorly correlated with soil volumetric water content during dry periods. Attenuation effect of plant canopy was not significant. For irrigation purpose, the results suggested that the sensitivity of reflected GPS signals to soil moisture changes needed to be further studied before this technology could be utilized for irrigation scheduling in cotton production. Refinement of this technology will expand the use of advanced remote sensing technology for site-specific and timely irrigation scheduling. This would eliminate the need to install moisture sensors in production fields, which can interfere with farming operations and increase production costs

Relationship of Soil Moisture and Reflected GPS Signal Strength

Many agricultural fields across the country have a high degree of variability in soil type and water holding capacity that affects irrigation management. One way to overcome problems associated with the field variability for improving irrigation management is to utilize a site-specific irrigation system. This system applies water to match the needs of individual management zones within a field. A real-time continuous soil moisture measurement is essential for the success of site-specific irrigation systems. Recently the National Aeronautics and Space Administration (NASA) developed sensor technology that records the global positioning system (GPS) signal reflected from the surface of Earth, which estimates the dielectric properties of soil and can be used to estimate soil moisture contents. The overall objective of this study was to determine the feasibility of utilizing GPS-based technology developed by NASA for soil moisture measurements and to determine the influence of soil type, soil compaction, and ground cover on the measurements. The results showed strong positive correlations between soil moisture and reflected signals. Other factors (soil compaction and soil type), were not significantly related to reflectivity and did not significantly change the relationship between reflectivity and soil moisture contents. In addition, ground cover (rye crop) did not significantly reduce reflectivity. Therefore, this system could be used as a real-time and continuous nonintrusive soil moisture sensor for site-specific irrigation scheduling and watershed management

Controlling irrigation in a container nursery using IoT

The demand for water has increased while the supply has been stagnant. This may be attributed to population growth, land-use dynamics and climatic factors. The availability of a sustainable source of water for food production is forecast as a critical issue facing agriculture. The recent EPA Strategic Plan emphasized the need to ensure waters are clean through improved water infrastructure and sustainably manage programs to support the different uses of water. This plan though broad dictates the need to develop new technologies that can help optimize the use of water via sensor-based irrigation. The goal of this project was to design a prototype irrigation controller using the internet of things (IoT). The controller is a closed-loop design which uses the soil moisture data to turn on and off the irrigation system based on specified thresholds. The data and control was hosted in an online IoT platform. The IoT platform provides real-time monitoring and control via a simplified online Graphical User Interface (GUI). The system was developed at the Sensor and Automation Lab of Edisto-REC, Clemson University and then field tested at McCorkle Nurseries in Dearing, GA during summer 2017. During the four month field test a number of challenges were identified including signal transmission and hardwire connections although overall, the prototype achieved the six objectives established for the system

UAVs for Vegetation Monitoring: Overview and Recent Scientific Contributions

This paper reviewed a set of twenty-one original and innovative papers included in a special issue on UAVs for vegetation monitoring, which proposed new methods and techniques applied to diverse agricultural and forestry scenarios. Three general categories were considered: (1) sensors and vegetation indices used, (2) technological goals pursued, and (3) agroforestry applications. Some investigations focused on issues related to UAV flight operations, spatial resolution requirements, and computation and data analytics, while others studied the ability of UAVs for characterizing relevant vegetation features (mainly canopy cover and crop height) or for detecting different plant/crop stressors, such as nutrient content/deficiencies, water needs, weeds, and diseases. The general goal was proposing UAV-based technological solutions for a better use of agricultural and forestry resources and more efficient production with relevant economic and environmental benefits

Huanglongbing (citrus greening) detection using visible, near infrared and thermal imaging techniques

This study demonstrates the applicability of visible-near infrared and thermal imaging for detection of Huanglongbing (HLB) disease in citrus trees. Visible-near infrared (440-900 nm) and thermal infrared spectral reflectance data were collected from individual healthy and HLB-infected trees. Data analysis revealed that the average reflectance values of the healthy trees in the visible region were lower than those in the near infrared region, while the opposite was the case for HLB-infected trees. Moreover, 560 nm, 710 nm, and thermal band showed maximum class separability between healthy and HLB-infected groups among the evaluated visible-infrared bands. Similarly, analysis of several vegetation indices indicated that the normalized difference vegetation index (NDVI), Vogelmann red-edge index (VOG) and modified red-edge simple ratio (mSR) demonstrated good class separability between the two groups. Classification studies using average spectral reflectance values from the visible, near infrared, and thermal bands (13 spectral features) as input features indicated that an average overall classification accuracy of about 87%, with 89% specificity and 85% sensitivity could be achieved with classification models such as support vector machine for trees with symptomatic leaves

Feasibility of Using Computer Vision and Artificial Intelligence Techniques in Detection of Some Apple Pests and Diseases

There are many methods to detect plant pests and diseases, but they are primarily time-consuming and costly. Computer vision techniques can recognize the pest- and disease-damaged fruits and provide clues to identify and treat the diseases and pests in their early stages. This study aimed to identify common pests, including the apple capsid (Plesiocoris rugicollis)/AC, apple codling moth (Cydia pomonella)/ACM, Pear lace bug (Stephanitis pyri)/PLB, and one physiological disease-apple russeting/AR in two cultivars, Golden Delicious and Red Delicious, using the digital image processing and sparse coding method. The Sparse coding method is used to reduce the storage of the elements of images so that the matrix can be processed faster. There have been numerous studies on the identification of apple fruit diseases and pests. However, most of the previous studies focused only on diagnosing a pest or disease, not on computational volume reduction and rapid detection. This research focused on the comprehensive study on identifying pests and diseases of apple fruit using sparse coding. The sparse coding algorithm in this work was designed using Matlab software. The apple pest and disease detection were performed based on 11 characteristics: R, G, B, L, a, b, H, S, V, Sift, and Harris. The class detection accuracy using the sparse coding method was obtained for 10 classes with three views of apple for S. pyri of red apple as 81%, S. pyri of golden apple as 88%, golden apple russeting as 85%, S. pyri and russeting of red apple as 100%, S. pyri and russeting of golden apple as 80%, codling moth of red apple as 86%, codling moth of golden apple as 72%, S. pyri of red apple as 83%, S. pyri of golden apple as 90%, codling moth and S. pyri of red apple as 80%, and codling moth and S. pyri of golden apple as 67%. The total processing time for developing the dictionary was 220 s. Once the dictionary was developed, pest and disease detection took only 0.175 s. The results of this study can be useful in developing automatic devices for the early detection of common pests and diseases of apples. Although the study was focused on apple diseases, results for this work have huge potential for other crops