NOVEMBER 2011: Agri-electronics is an emerging and multidisciplinary frontier of advanced research that takes convergent and holistic look at all the related areas in the agricultural sector. It ensures optimal induction of IT and electronics for improvement in crop productivity, quality and value.
R&D in agri-electronics is a potential vehicle for digitisation of green revolution. It promises a catalytic role in national agricultural development in terms of diversification, enhancing productivity, adding value, capturing markets, mixing farm and off farm income, entering and creating marketing chains, improving food quality and safety, and balancing ecological interests.
Scope and challenges of agri-electronics
The latest practice adopted worldwide is precision agriculture. It implies knowledge based agriculture employing the latest techniques of science and technology bringing together diverse fields agronomy, plant science, soil science, entomology, metrology, weed science, plant pathology, ecology, water technology, environment and economics to deal with complex issues of production, distribution, pricing, marketing, etc.
Fig. 1 shows the scope of agri-electronics. To deal with cross-disciplinary complexities, multitudes of technologies like bio-electronics, sensors, software, embedded systems and soft computing need to be harnessed to develop efficient agri-electronics solutions. Management of new technologies for agri-electronics research is one of the stiffest challenges. Agri-electronics research must be carefully planned and modulated to take account of present and future trends in agriculture, bio-technology, and information and communication technology (ICT).
Partnerships and networking are essential for a multidisciplinary field like agri-electronics. It is imperative that agri-electronics research be conducted through alliances. Stakeholders and researchers from different organisations need to collaborate to identify the needs for specific kinds of knowledge, to implement research projects, and to disseminate results.
Bringing different interest groups together (such as policy-makers, scientists, traders, farmers, producers, governments, industries and consumers) is the key to increasing the contribution of ICT research in agricultural development.
Agri-electronics activities at the national level may be divided into two major categories: Electronics and instrumentation for agriculture, and ICT for agriculture.
While very few national R&D laboratories are involved in the field of electronics and instrumentation, government agencies, NGOs and ICT companies in the private sector are involved in developing and deploying e-solutions mainly focused on information dissemination services. Such ICT services include advisory information provision over the Internet or mobile devices to the farmers on weather forecasts, price information, etc.
IT and electronics in agriculture. Table I shows the R&D labs in India that are engaged in agri-electronics research to develop novel and smart instruments.
Agri information dissemination systems. In India, there are 46 Web portals and 39 IT based Web enabled applications already operational. All these portals and applications provide information transaction services to the government departments, agro marketers at all levels, farmers and all the stakeholders in agricultural domain.
Though a number of these Web based portals have very friendly user interfaces and presentations in vernacular languages, these are not yet used regularly by the small and medium farmers due to a number of reasons including lack of infrastructure, lack of IT literacy and inadequate content. Of course, the portals find good use in government to government transactions.
A comprehensive farmers’ portal is under development by the Ministry of Agriculture & Cooperation, government of India. Mobile governance is also making its way to the e-governance arena, and a number of solutions using mobile-based agro advisory services are being developed by a number of companies. A brief synopsis of such e-agriculture initiatives is given in Table II.
The following conclusions can be drawn from the above discussion:
1. In India, more stress is in deploying eagriculture solutions over the Internet or mobile devices by multiutility Web portals.
2. No focus is directed towards development of smarter electronics and instrumentation systems in India.
3. Even the deployed ICT projects in India provide very few success stories, and there is no widespread outcome and impact at the grassroot level.
In view of the above, there exists an emergent need for focused and integrated endeavour towards development of smart devices for the agricultural sector and coupling of the smart devices to the intelligent software through advanced telecommunication (Internet or mobile devices).
A few innovative solutions have been developed for quality measurement, monitoring and management of Indian crops. These innovations are detailed in the box.
1. Electronic nose (e-nose) for aroma characterisation of black tea
An emergent need for some physical, fast and lowcost methodology for measurement of physical quality attributes like smell has been felt by the food processing industries, especially black tea manufacturing industries in India. The e-nose system proposes a pragmatic methodology for measuring the aroma of black tea by fusion of advanced sensor technologies backed up with intelligent self-learning-type software algorithms.
The e-nose-based aroma indices innovated through sustained experimentations in black tea fermentation process enable online smell monitoring and detection of optimal fermentation time for producing best flavour components in the manufactured black tea. The neural network-based self-learning computational models built into the e-nose software system permit fast declaration of quality scores of finished black tea, coherent with the organoleptic appraisals of the tea-tasters. The entire system is software-driven, field customisable and requires minimal operator intervention.
E-nose detects and discriminates among complex odours using a sensor array. The sensor array consists of non-specific sensors that are treated with a variety of chemical materials. The sensor array is exposed to the volatile molecules and smellprint (or fingerprint) is generated from it. Patterns from known odours are used to construct the database and train a pattern recognition system so that unknown odours can be classified and identified.
Fig. 2 shows the block diagram of e-nose system. The e-nose instrument comprises three elements—sample handling system, detection system and data processing system.
Sample handling is a critical step affecting the analysis by e-nose. To introduce the volatile compounds present in the head space (HS) of the sample into the detection system of e-nose, several sampling techniques are used:
1. Static head space (SHS) technique
2. Purge and trap technique
3. Solid-phase micro extraction technique
4. Stir bar sorptive extraction technique
5. Inside-needle dynamic extraction technique
6. Membrane introduction mass spectrometry technique Out of these, SHS technique has been observed to be useful for food and agro applications.
The most complicated part of electronic olfaction process is odour capture and the sensor technology deployed for it. Any sensor that responds reversibly to a chemical in gaseous or vapour phase has the potential to be developed in an e-nose format. Early e-nose used either polished wires in contact with porous rods saturated with different electrolytes, or thermistors coated with materials such as gelatin, fats or polymers. In the 1980s, advances were made with the appearance of chemically-sensitive sensors and developments in electronics and computing.
Some of the essential or desirable properties of the chemical micro-sensors to be used in e-nose are selectivity, sensitivity, speed of response, reproducibility, reversibility and portability.
Table III shows sensor types and associated measuring principles used for e-nose. Out of these sensors, conducting polymer, metal-oxide semiconductor and bulk acoustic devices are most commonly used in commercial e-noses.
Advanced signal processing. In a machine olfaction system, two major building blocks are the sensors and the pattern classification engine. Interfacing electronics, signal conditioning and data acquisition circuits provide the crucial link between these two blocks.
Olfaction sensors have a wide range of transduction mechanisms. But at the output of these sensors, an electrical signal (say, voltage or current) proportional to the gas/liquid exposure on the sensor surface is required. Interface circuits convert the sensor output parameters into an electrical signal for further processing.
In chemo resistor sensors a change in resistance is obtained on exposure to the odour particles. A voltage divider or a Wheatstone bridge circuit can be used as the basic interface circuit. Interfacing circuits of quartz crystal microbalance or surface acoustic wave devices are required to measure shift in the resonance frequency.
The interfacing circuits are followed by the signal-conditioning block, which is basically analogue conditioning of the electrical signal through five sequential stages—buffering, amplification, filtering, conversion and compensation.
Data acquisition involves gathering signals from measurement sources, i.e., sensors, and digitising the signal for storage, analysis and presentation on a personal computer (PC). Data acquisition systems are available in many different PC technologies for flexibility and application-specific customisation. Various options include peripheral component interconnect (PCI), PCI eXtensions for instrumentation (PXI), PCI Express, PXI Express, Personal Computer Memory Card International Association (PCMCIA), USB, IEEE 1394, parallel or serial ports for data acquisition in test, measurement and automation applications.
Signal pre-processing. Signal pre-processing means extraction of relevant information from the responses obtained and preparation of this information for multivariate pattern analysis. The major aspects of pre-processing are baseline identification and manipulation/determination, compression and normalisation or final preprocessing.
Baseline is the sensor response to a reference analyte and proper accommodation of the same into the actual sensor response is expected to provide compensation due to drift, scaling and enhancement of contrast and better identification. Three different techniques of baseline manipulation are differential, relative and fractional.
Compression vector or a fingerprint is extracted by reducing the number of descriptors. Normalisation is the final leg of pre-processing where techniques are applied to operate on the sensor signal to compensate for sample-to-sample variations due to change in analyte concentration and drift in the sensors. Alternatively, the entire database of a single sensor can be operated upon and scaling of sensors effected. The former technique is known as local method and the latter as global method.
The responses generated by an array of olfaction sensors are processed using a variety of techniques. The pattern recognition engine may be developed using both parametric and non parametric methods. Parametric methods are commonly referred to as statistical approaches and are based on the assumption that the sensor data set may be represented as a probability distribution function. The non-parametric methods do not assume any probability distribution function and deal with biologically inspired techniques, viz, artificial neural networks and expert systems.
A MOS-based electronic nose has been developed for tea aroma characterisation at C-DAC, Kolkata. The e-nose setup is shown in Fig.3.
The following e-noses are available in the market: FOX 2000/3000/4000, PEN 3, GDA 2, i-PEN, Gemini, Cybernose, ZNose 4200/4300/7100, Astree, Smart Nose 2000 and LibraNose 2.1.
2. Electronic vision for tea
Vision is the most advanced human sense and images play an important role in human perception. Digital image processing is one of the emerging frontiers of advanced research. It deals with the process of digital camera-based image capturing, conditioning and measuring the captured images by advanced soft-computing algorithm so that important information and features may be extracted from the acquired images.
Colour of tea leaves (both finished as well as in-process) play an important role in optimisation of the length of the processes, estimation of quality and gradation of finished tea. The advances of digital image processing techniques may be gainfully employed for objective assessment of tea-quality.
A number of image processing-based solutions (block diagram shown in Fig. 5) have been developed: End-point detection of fermentation by monitoring tea-leaf colour, mimicking visual inspection of tea-taster by electronic means and quality estimation (tea gradation) of manufactured tea at drier mouth.
End-point detection of fermentation by monitoring tea-leaf colour. Determination of optimal point in fermentation process is crucial for quality in manufactured black tea. Fermentation is an oxidation process wherein various enzymatic reactions and chemical changes occur progressively leading to change in the colour of cut tea leaves from green to coppery-brown. Over- or under-fermentation may lead to significant quality degradation.
Traditionally, the fermentation end-point is determined by two physical parameters—observing the odour (apple-type flavour) and simultaneously monitoring the leaf colour (coppery-brown). There is also a wet colorimetric test to determine the fermentation end-point.
An image processing-based e-vision system has been developed to detect the end-point of fermentation using a suitable colour-matching algorithm backed up by soft computing technique. During software training, a colour-palette/image database is created with colour images of the leaf at various stages of fermentation process. This is called a standard image database.
During the actual fermentation process, any leaf image at any stage of the fermentation process can be compared against those of the standard database to determine an estimation of the remaining time for fermentation. Being physical, this colour comparison is a convenient tool to determine the end-point of fermentation instantly with a high degree of accuracy and repeatability with respect to finished tea quality.
The software framework enables tea planters to train the system with fermentation colour data of their own garden. Additionally, user-friendly software (Fig. 6(a)) has been designed to display dynamic colour profile with respect to time (Fig. 6(b)) for comparison against any previously stored profile. The framework enables data logging, audio-visual alarm annunciation, etc.
Finished tea classification by e-vision. Traditionally, the quality of processed tea is measured by measuring its grain size, appearance, liquor colour, infusion and flavour in a subjective manner (on a 1-10 scale). An e-vision system captures the images of various tea samples for analysis using colour matching/soft computing technique to provide a colour index value (like tea tasters’ score) more precisely and reliably. It also finds a suitable match from the previously-created image database.
Monitoring is based on the grain-size, dry leaf texture, dry leaf appearance, liquor colour (without milk), liquor colour (with milk), etc. Colour-image is analysed using hue, saturation and intensity (HSI) model because human perception is closely matched with this classification system. A suitable colour-matching algorithm with soft computing technique is utilised to determine the nearest match from this image database. Ultimately, this colour-indexing may also be correlated with the tea tastes’ grading.
The image processing software has been developed such that the tea tasters (or planters) can train the system with their own grading. The system estimates/predicts the score/colour index value of any unknown sample against the created matrices. The user-friendly software has been developed to store the tea taster’s results after embossing date-time stamping, tasting ID and pointer to the corresponding image in the image database for future reference.
Granular gradation of tea by e-vision. Instant estimation of manufactured tea grade at drier-mouth is one of the desirable requirements for quality tea production. It is very difficult to find out the percentage of tea grade at the drier output at any moment of time. An innovative image processing-based solution has been developed to determine the percentage of various tea grades at drier-mouth output as an estimation of consistency in quality tea production (Figs 7(a) and 7(b)).
3. Electronic tongue for taste comparison
Electronic tongue (e-tongue) is an instrument that measures and compares tastes. In a human tongue, chemical compounds responsible for taste are perceived by human taste receptors. In an e-tongue, sensors detect the dissolved compounds. Like human receptors, each sensor has a spectrum of reactions different from the other. The information given by each sensor is complementary and the combination of all sensor results generates a unique fingerprint.
Most of the detection thresholds of sensors are similar to or better than human receptors’. In the biological mechanism, taste signals are transducted by nerves in the brain into electric signals. E-tongue sensors’ process is similar. These generate electric signals as potentiometric variations. Taste-quality perception and recognition is based on building or recognition of activated sensory nerve patterns by the brain on the taste fingerprint of the product. This step is achieved by the e-tongue’s statistical software which interprets the sensor data into taste patterns.
The type of taste that is generated is divided into five categories—sour, salty, bitter, sweet and umami (delicious). Sourness, which includes hydrochloric acid, acetic acid and citric acid, is created by hydrogen ions; saltiness is registered as sodium chloride; sweetness by sugars; bitterness, which includes chemicals such as quinine and caffeine, is detected through magnesium chloride; and umami by monosodium glumate from seaweed and disodium in meat, fish and mushrooms.
The mechanisms for taste recognition in human and electronic tongues have the same three levels:
1. Receptor level: Taste buds in humans and lipid membrane or novel metal sensors in the e-tongue
2. Circuit level: Neural transmission in humans and transducers in the e-tongue
3. Perceptual level: Cognition in the thalamus in humans and statistical analysis by software in the e-tongue
Receptor level. To detect dissolved organic and inorganic compounds, the sensor-probe assembly is used by the e-tongue at the receptor level. Each probe shows cross-sensitivity and selectivity so that each sensor could concurrently contribute to the detection of most substances found in the liquid matrix. These sensors are composed of an organic coating sensitive to the species to analyse the samples and a transducer that allows conversion of the membrane response into signals that will be analysed
Circuit level. At the circuit level, the sample is quantified and digitised, and results recorded. Electro-analyical methods can be used for measuring the result. These include both active and passive to study an analyte through the observation of potential and/or current.
Potentiometry measures the potential of a solution between two electrodes passively, without affecting the solution much in the process. Voltammetry applies a constant and/or varying potential at an electrode surface and measures the resulting current with a three-electrode system. Coulometry uses applied current or potential to completely convert an analyte from one oxidation state into another. In this experiment, the total current passed is measured directly or indirectly to determine the number of electrons passed
Perceptual level. Perception is done in the computer using the e-tongue system. Depending upon the application for which it is applied, the data analysis can produce a variety of information. Exploratory data analysis can be performed using different statistical methods such as principal component analysis and linear discriminant analysis on the data set to correlate the collected data.
Tea tasters usually evaluate tea quality and conventionally assign scores on a scale of 1 to 10 depending on the major quality attributes of the tea sample. The major quality attributes of black tea are briskness/strength, flavour, aroma and colour.
At C-DAC Kolkata, e-tongue for determining the briskness/strength of the black tea liquor has been developed. The virtual instrumentation-based e-tongue uses voltammetry technique for correlating the briskness/strength of tea liquor with the ‘tea taster’ mark. In this work, an array of selected electrodes was immersed into black tea liquor-samples for analysing the tea samples. A computational model was developed to correlate the measurements with the tea taster’s briskness/strength scores. The developed e-tongue structure is shown in Fig. 8.
4. GSM-based remote operation of agricultural equipment
Bengaluru based Rural Bridges’ Kisan Shakti is an easy-to-use and economical communication device which enables a farmer to operate his motor pump set from anywhere through his mobile or landline. The GSM-based controller can be fitted to all the existing motor starters and used with mobiles or landlines of any operator.
How it works? A farmer simply calls Kisan Shakti and selects the options provided by the interactive voice response system (IVRS) menu in the local language to switch on/off of the motor. He gets the confirmation of the operation performed along with running status of the motor and also if water is being pumped or not immediately from the IVRS menu. Advanced technological features of the device ensure instant communication to the farmers whenever power is available, and also in cases of faulty power supply, lack of water in the well/bore and attempt of device theft. The device raises an alarm and makes a phone call to the farmer if anyone tries to steal it or any device connected to it. There are different modes of operation to suit the needs of the farmers—auto, manual and timer—indicated by three LED indicators. The device is adaptive to any motor configuration up to 20 HP.
5. ePest surveillance system
Infronics Systems in association with the Food & Agriculture Organisation (FAO) and Ministry of Agriculture, government of India, has developed an ePest surveillance system called Fieldman. It offers a uniform and standardised process for pest surveillance and data collection from the agricultural fields to monitor and ascertain the health of the crop, and issue timely advice to farmers.
The system is an amalgamation of embedded and communication technologies on a single platform. There are plug-in sensors for automatic data collection. The GPS module enables collection of geo-referenced data and the GPRS/GSM module ensures connectivity through the telecom network. The data collected from the fields can be uploaded to a central location for analysis by experts.
The ePest surveillance system also includes an extensive suite of software applications—both on the Fieldman handheld system and back-end server—to offer uniform surveillance protocol for field data collection about pests.
6. Plantation management using wireless sensor network
For developing an efficient system of plantation management, the foremost input is the availability of accurate data. This data includes soil properties, agronomic data, physicochemical parameters, atmospheric data, etc, preferably on a day-to-day basis or even hourly basis. Normal laboratory analysis of these parameters and manual decision-making take a long time even with the most sophisticated analytical techniques.
Most of the sampling procedures are not in-situ and samples have to be brought from the field to laboratories for analysis, a lot of time. By the time the results are available and decisions taken, the farm conditions might change making the decision inappropriate. Quick and quality decision-making at the farm level can enhance agricultural productivity and quality manifold.
Computer-aided decision-making process can handle and analyse several input parameters at the same time involving large databases.Monitoring of physical and environmental parameters including soil moisture, soil temperature, soil pH, leaf temperature, relative humidity, air temperature, rainfall, vapour pressure and sunshine hours is done through a wireless sensor network (WSN).
WSN comprises spatially distributed sensors to monitor physical or environmental conditions. It is a comprehensive system that integrates sensing, wireless and processing technologies and is capable of spatially and temporally sensing different physical parameters without loss in the sensing accuracy. The parameters are processed and wirelessly transmitted to a centralised data storage system through a gateway from where they may be remotely accessed and analysed by the user.
The system architecture of a WSN-based system consists of different sensors interfaced to electronic hardware with data processing capabilities. The electronic hardware is also equipped with wireless communication modules allowing the sensed data to be processed and transmitted according to a select protocol. These hardware nodes are called motes in WSN terminology. Each of the motes is interfaced with a set of sensors depending on the application domain. The sensors may be programmed to sense at specific intervals or periodically in a day.
WSN in agriculture. WSN technology can broadly be applied into three areas of agriculture: fertiliser control, irrigation management and pest management.
The sensors that can be interfaced to the mote are temperature, relative humidity, solar radiation, rainfall, wind speed and direction, soil moisture and temperature, leaf wetness, electrical conductivity and soil pH sensors. These sensor-readings can be integrated with a decision support system that aids the management of resources to the crop. Given below is a brief note on automated drip irrigation system.
Drip irrigation automation. Conventional irrigation methods like overhead sprinklers and flood-type feeding systems usually wet the lower leaves and stem of the plants. The entire soil surface is saturated and often stays wet long after irrigation is completed. Such a condition promotes infections by leaf mold fungi. Flood-type methods consume a large amount of water, but the area between crop rows remains dry and receives moisture only from the incidental rainfall.
The drip or trickle irrigation technique slowly applies a small amount of water to the plant’s root zone. Water is supplied frequently, often daily, to maintain favourable soil moisture condition and prevent moisture-stress in the plant with proper use of water resources.
WSN-based drip irrigation system is a real-time feedback control system which monitors and controls all the activities of the drip irrigation system. A typical system includes a delivery system, filters, pressure regulators, valves or gauges, chemical injectors, measuring sensors/instruments and controllers.
WSN framework installed in the field may gather various physical parameters related to irrigation. These include ambient temperature, ambient humidity, soil temperature, drip water temperature, soil moisture, soil pH, water pressure, flow rate, amount of water, energy calculation (power), sunlight condition, chemical concentration and water level.
The data is sent to the central server wirelessly through the motes and gateways. Based on the data ranges, the central server generates necessary control actions, which are routed to the respective controllers through control buses enabling implementation of closed-loop automation of the drip irrigation system.
The basic feature of the product is to enable switching on and off of the motor remotely. The device ensures that all the fault conditions are checked and only then the motor is started.
The author is associate director at Centre for Development of Advanced Computing (C-DAC), Kolkata