Green Technology Approaches for Management of Ganoderma Diseases in Oil Palm.

EXECUTIVE SUMMARY

ViGGor EB1 is a liquid formulation contains specific beneficial non-pathogenic and endophytic bacteria, Pseudomonas ganoEB1 strain. The bio-technology formulations  was developed through MPOB TT No. 443, June 2010 and the research was led by Dr Idris Abu Seman, Head of Ganoderma and Disease Research for Oil Palm or known as GanoDROP, MPOB aiming on Increasing Plant Health and Growth while managing Ganoderma infections.   GanoDROP won the 3rd prize of the National Intellectual Property Award 2014 for the Patent Category for its environmentally-friendly inventions of Compositions for Controlling Ganoderma Disease in Plants and recently given an appreciation by winning MPOB Gold Medal Award 2017. 

The effectiveness of P. ganoEB1 in suppressing BSR development in oil palm seedlings shows a significant difference in % Disease Incidents (%DI), Severity Foliar Symptom (%SFS) and Dead Seedlings (%DS). The reduction of %DI, %SFS and %DS is respectively was noted as 68%, 24% and 74%

ViGGoR-EB1 contains of:

Pseudomonas ganoEB1 with 1 x 109 CFU per ml.

Packaging: 1.0 Liter, 

4.0 Liter and

20.0 Liter.

Mode of Action 

P. ganoEB1 will colonize the roots of plant and produce enzyme and this is endophytic bacteria meaning this bacteria can penetrate and live in the plant.  This unique behavior give long and good duration of disease management and at the same time increase immunity of the plant from others incoming diseases.

P. ganoEB1 will antagonize direct with the pathogen and destroy the pathogen.  This method also apply to fungus and also attack and destroy fungus system and also spore.

P. ganoEB1 will not kill pathogen but the unique system which the antibiotic produce and the enzyme will strengthen the plant and this make the pathogen difficult to develop in the plants.

Thus, P. ganoEB1 will provide

1. Typically give a positive result to the oxidase and catalase tests
2. The genus demonstrates a great deal of metabolic diversity and consequently are able to colonies a wide range of niches.
3. The bacteria induce systemic resistance in the host plant, so it can be better resist attack by a pathogen, or bacteria might out compete other pathogenic soil microbes, by siderophores giving a competitive advantage at scavenging for iron or the bacteria might produce compounds antagonistic to other soil microbes, such as phenazine-type antibiotics or hydrogen cyanide. 
4. Growth hormone for better plant growth
5. Enzyme for fighting diseases and as antibiotic
6. Produce enzyme too boost plant immunity versus disease
7. Able to fight disease based on Bacteria, Virus and Fungus.
8. Will act as a plant booster to enhance plant growth.
9. Able to increase soils health for the all crops.
10. Increase plant immunity to diseases and extreme environment
11. Increase crop yield and quality. 
12. Cost effective and long duration of effectiveness.
13. Safe to users and environment.

Further, 

a. Preventive Treatment.

Highly recommended for historically ganoderma area whereby, early treatment are crucial for better protection.

b. Infected Palm.

ViGGoR-EB1 will prolong the life of infected palm  and salvage more yield.  It is compulsory to conduct treatments on surrounding palm to avoid new infection and manage ganoderma disease before spreading.

This unique behavior give long and good duration of disease control and at the same time increase immunity of the plant from others incoming diseases such as ganoderma in oil palm plant and promote plant growth.

Advantages of ViGGoR-EB1 Oil Palm Industries.

P. ganoEB1 will interact with plant host and will symbiosis with the plant for better growth and healthy indirectly provide insurance to the planters;

a. Suitable for IPM for the management of Ganoderma infection.

ViGGoR-EB1 is suitable for IPM ganoderma management in oil palm plantation and study has been conducted more than 10 years by reputable bodies.  ViGGoR EB1 can be used at any stages of oil palm growth and for better control, ViGGoR EB1 need to be used at early as possible.

b. Promote Plant Growth.

ViGGoR-EB1 contains endophytic bacteria which show good result on plant growth, which microbe will communicate with plant for better plant growth and resistant to diseases.

c. Cost effective and long duration of effectiveness.

One of the most cost effective way to combat or prevent ganoderma attack in oil palm plantation compare to others microbes based products and also conventional practices.

d. Long Duration of Control.

P.ganoEB1 is host specific (Oil Palm), after it’s inoculate into oil palm roots the P.ganoEB1 will remain in the oil palm three.  This will give long duration of protection from ganoderma diseases.  

e. Safe to users, Soil Health and environment.

This product was tested and it’s safe to the users, environment and increase soils health. Hence will promote better oil palm growth.

f. Simple application methods.

ViGGoR EB1 does not need special equipment to apply compared to other products which need to do pocket methodology.  This will safe on cost and speed up the application progress.

g. Protection is BETTER than Cure.

ViGGoR EB1 act as insurance to the planters for their valuable investment by providing protection on the investment.  This small investment will give long term protection against ganoderma infection and at the same time promoting plant growth hence produce better yield.

Dosages and Recommendation.

For a better result of ViGGoR-EB1 application the treatment must be conducted at three years consecutive rounds.  

 

Dilution.

Mix ViGGoR-EB1 with water at a ratio of 1:100

Apply ViGGoR-EB1 solutions in according to dosages 

Recommendation.

a. Prevention.

Nursery Stages and Field Planting.

Treat all palm seedling at nursery stages as early as possible and ensure the best oil palm seedling will be planted in the field. 

b. For Infected Palm.

Planted and Matured Oil Palm.

ViGGoR EB1 will protect the healthy roots and this will prolong the life of the infected palm.  It is compulsory to treat the surrounding palm with ViGGoR-EB1 for protection against ganoderma diseases.

Application Methods.

ViGGoR-EB1 application is simple that do not use extra and expensive method and equipment.  ViGGoR-EB1 applications are by soil drenching or spraying at palm based depending on the palm age at the given dosages.

CONCLUSION.

This is a unique products designed aiming for Ganoderma management and has gone through years of research by MPOB.  To maintain the efficacy and quality, MPOB has taken proactive measure such as maintaining the ViGGoR-EB1 culture at the best place.  ViGGoR-EB1 is the affordable and reliable product to combat ganoderma disease while benefited at all stake in the oil palm industries.

#PROTECTYYOURINVESTMENT

#PREVENTIONISBETTERTHANCURE

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Thermal Remote Sensing in Pest Management and the Possibility of Implementing in Oil Palm Plantation

Introduction

The oil palm plantation in Malaysia is one amongst the largest producer of crude palm oil in the world. It is constitutes a key pillar of the Malaysian National Economy. The industry is well regulated, comprising government, land schemes and independent smallholdings, as well as those engage in downstream industries such as milling, processing, manufacturing and trading. The export revenues of palm oil are up to more than RM 67.6 billion in 2016. Oil palm was planted in more than 5.74 million hectares (ha) in 2016 and utilising the largest agricultural land area of more than 60% (Khusairi, et.al. 2017). 

Despite the rapid growth of the industry, the oil palm remains prone to the threat by a variety of pests such as insects, vertebrates and diseases.  With the fact that there are various techniques in in controlling such as cultural, mechanical, biological, genetic and chemical control are became the primary means of solving pest problem in plantation. Often the strategy of controlling at the late stages of pest outbreak are not satisfactory due to impact to human health and environment impact which are not comply with MSPO and RSPO. 

Precise and efficient pest early detection and warning are key strategic important in handling and managing pest outbreak in the plantation. Meteorological data for example were used to forecast pest outbreaks based on knowledge of the biology and ecology of the pest. Ibrahim et.al. 2013, demonstrated that the effect of temperature on the development and survival of bagworm species conformed to the insect’s trend to increasing temperature until the optimum was reached. 

The used of spatial technology, with comprises and Global Positioning System or GPS benefited agriculture through applications of crop nutrient and pest and disease status monitoring (Ahmadi et.al. 2017). The presence of remote sensing tools offer rapid, harmless and cost-effective means to obtain necessary information on the triggering factor of pest outbreaks such as temperature, relative humidity and their natural enemies.

What is Thermal Remote Sensing System or Thermography?

Thermal remote sensing technology or thermography is a non-destructive technique used to determine thermal properties of any objects of interest. The principle of thermal remote sensing is the invisible radiation patterns of objects converted into visible images and these images are called thermal images. These images can be acquired using portable, handheld or thermal sensors that are coupled with optical systems mounted on an airplane or satellite. 

The potential use of thermal remote sensing in agriculture includes nursery and greenhouse monitoring, irrigation scheduling, plant disease detection, estimating fruit yield, evaluating the maturity of fruits and bruise detection in fruits and vegetables. 

However, in recent years, the usage of thermal imaging is gaining popularity in pest detection due to the reductions in the cost of the equipment and simple operating procedure. 

Concept and advantages of Thermography

Thermal remote sensing or Thermography is one amongst techniques in remote sensing systems. Others are such as Virtual, Optical Microwave, Radar and Synthetic Radar. Thermography however is a non-contact techniques to determine the temperature distribution of any object in a short period of time. It is deal with the acquisition, processing and interpretation of data acquired primary in the thermal portion of the electromagnetic spectrum (Ishimwe et.al. 2014). 

The principle of thermal remote sensing is, it collects the thermal infrared regions within the infrared radiation from 8 to 12 um which emitted from the Earth's surface by thermal sensors or cameras into an image (Ishimwe et.al. 2014).  The thermal remote sensing, radiations emitted by ground objects are measured for temperature estimation. These measurements give the radiant temperature of a body which depends on two factors; kinetic temperature and emissivity (Prakash. 2000).

Advantages of the thermal cameras are easy to handle and highly accurate temperature measurements are possible (Lloyd. 2013). Further, the thermal imaging, it is possible to obtain temperature mapping of any particular region of interest with fast response times, which is not possible with thermo-couples or other temperature sensors. In addition, the repeat- ability of temperature measurements is high in thermal imaging. 

Furthermore, previous models of thermal camera’s required cryogenically cooled sensors to obtain temperature resolution of 0.1°C whereas recent day cameras can operate at room temperature, making these camera users friendly and promoting an increase in the use of thermal imaging in various fields (Ishimwe et.al. 2014; Lillesand & Chipman. 2014).

Detector and Lenses 

The principle used by thermal imaging is heat from incoming IR radiation increases temperature and are used to measure temperature changes by ant temperature dependent mechanism such as thermo-electric voltage, resistance or pyro-electric voltage (Rogalski, 2003). The detectors used in thermal cameras may be broadly classified into three categories: classic semi-conductors, novel semi-conductors, and thermal detectors (Holst. 2000). 

The lenses for thermal cameras are usually made of silicon (Si) or germanium (Ge) materials. In general, Si is used for MWIR cameras and Ge is used in LWIR cameras. Both materials have good mechanical properties (non-hygroscopic and do not break easily). While making proper design, infrared camera lenses can transmit close to 100% of incident radiation (Holst. 2000).

Thermal Imaging camera

A Thermo-graphic Camera (Infrared camera or Thermal Imaging Camera) is a device that forms an image using infrared radiation that similar to an image forms by a common camera using visible light. Thermal camera is designed to detect radiation emitted from a sample in a specified waveband into an electrical signal which is then processed into an image. Radiation in this part of the electromagnetic spectrum is referred to as infrared, or commonly IR, which is just beyond what the human eye can see (Lloyd. 2013; Prakash. 2000). On the other hand, camera sensors can be built to detect and make use of this type of radiation. A so-called day-and-night camera uses an IR-cut filter during daytime to filter out IR-light so it will not distort the colors of images as perceived by the human eye. When the camera is in night mode, the IR-cut filter is removed.

Since the human eye is unable to see infrared light the camera displays the image in black and white. Near infrared light also requires some kind of light source, either natural, such as moonlight, or man-made, such as street lights or a dedicated IR-lamp. With advancement in electronics and instrumentation technology, there are several thermal camera models available in the market at wide price ranges (Lloyd. 2013).

Case Study 1: Finding Termites with Thermal Imaging

Termites has been a huge problems in housing areas and urban buildings. Owners quite often are unaware of the present of any termite problem in their house. Pest Control Company are usually over-charged due to in ability to identify the location of the source of the problems. 

Oil palm plantation especially in an area of peat are attack by subterranean termites. The primary termites capable of killing oil palm was Coptotermes curvignathus (Cheng S., Kirton, L.G. and Gurmit, S., 2008). The occurrences of termite attack in oil palm was reported first in 1927 in Malaya. In immature oil palm, termites attack palms as early as 7 to 8 months after planting and infestations of immature plantings could reach 8-9 per cent with 3-5 per cent killed per year if not quickly treated. In mature oil palm, termites gain entry into the central frond column to feed on spear and new frond bases and then the growing point; the palm is finally killed (Fee C.G., 2017). 

When termite invade house and buildings, the normal heat pattern of the walls floors and roof are changes. Thermal imaging camera records changes in heat pattern and indicates the exact location of termite’s infestation. It was noted that termites are “ectothermic”, animal body temperature is determine by taking advantage of external condition and also called as cold-blooded animal whereby heat are not produced by the body. However, Termites are hosts to bacteria, which live in their gut and the bacteria helps to break down and digest cellulose, the main component of wood. The digestion and chemical reaction generates heat. 

   

Figure 1: Termite Queen Figure 2: Termite Queen in Thermal Image

Figure credit to Ken J., and David R., 2002

Figure 1 and 2 are an example of end product of thermal imaging camera for Termite queen. Figure 3 and 4 is an example of how thermal image can be used as a tool to locate termites whereas conventional method fail to locate the infestation of termites. 

Figure 3: Thermal imaging are capable to locate termite infestation Figure 4: Conventional investigation are not capable to find evidence of termite infestation

Figure credit to Ken J. and David R., 2002

Case Study 2: Remote Sensing for Monitoring Bagworm Infestation

Bagworm which classified in family Lepidoptera: Psychidea are categorised as leaf eating caterpillars characterised based on their bag, built from pieces of dried plant material i.e. leaves and small twigs (Barlow, 1982). The outbreak are very common in Malaysia with some states experiences a severe attack especially in west coast and having negative effect on economic due to reduction of oil palm yield.

According to Wood et.al. (1973), the yield decline over the next two years is caused by 50% of canopy damages and may up to 43% reduction. Perhaps more precise and early detection of bagworm infestation becomes critical part and may help in providing solution for oil palm plantation. 

The census was conducted by MPOB in June 2012, at Teluk Intan Research Center. However, the study was using a spectral reflectance measures from field spectrometer instead of using thermal imaging. The data collection was based of three types of foliar damage and its respective spectral measurement i.e. light, medium and serious damage (Figure 5)

Figure 5: Spectral reflectance characteristics on each level of foliar damage. 

Based on the obtained data, plotting of bagworm location infestation are possible to be plotted and displayed in a map mode (Figure 6). It can be translated into on ground action, indeed after minimum activity of on ground census. Decision can be made by planters and manager to use appropriate tools and method of controls the outbreak of bagworm.

Figure 6: Distribution of sampling points over layered in SPOT 5 satelite image of MPOB Research Station, Teluk Intan, Perak. (MPOB TT No 502) 

Case Study 3: Thermal Imagery for Animal Detection 

The large-scale of deforestation due to opening of new land for oil palm plantation creates crisis between human needs and the idealism of conservationist i.e. preserve the forest. Thus, RSPO and MSPO has a goal of transforming the industry in collaboration with the global supply chain. One amongst the important element in RSPO and MPSO are the High Conservation Value (HCV) and monitoring the wildlife are crucial. Similarly, information on animal pest such as wild boar, unattended cattle given a negative impact on overall plantation management. 

The conventional techniques are often fail to give an actual number of animal life in the designated area. Traditional ground-based survey are such as direct counts or records or observation on transect counts, trapping, tagging and sign may give a predicted number of population. The accuracy in using traditional methods comes with high costs in both labour and other limitation. Field expedition often consists of laborious, intensive sampling over long periods of time.

The animal thermal scanner was recently used on a study basis in three experimental research two of which were on ground basis and through airborne. The observation scoring method and on image analysis were developed for quantification of the thermal images in the vegetation component. 

Digital high resolution using remote sending has been used in combination with airborne platform perhaps may give a more detail data as well as more competitive costs and credible data. Thermal infrared sensors may give a more precise outcomes. Sensors measure the thermal radiant energy of materials within a scene. Most large animals are having a high radiant temperature in comparison with their current backgrounds.  Therefore thermal sensors provide a potential means for counting and studying the distribution of animals at night (Barrett & Curtis 1992).  

Thermal imagery has been recognised as a means of detecting animals since the late 1960s (Croon et. al. 1968). The technique has gained increasing recognition with the introduction and advance of new hardware technology, yet the method still remains at an experimental phase; few thermal aerial operational surveys are conducted. Inadequacies such as thermal sensor limitations, equipment availability, high costs and thermal imaging procedures can contribute to a non-viable operation.

Case Study 4: Red Palm Weevil

Rhynchophorus ferrugineus or better known as the red palm weevil (RPW) considered to be the world’s worst pest of palm trees (Abbas, 2010). According to DOA, 2007 the attack of RPW was first detected in Terengganu and had spread to 58 localities in all seven district of the states of Terengganu (DOA, 2011). In 2016 has been found in Perlis, Kedah, Pulau Pinang, Terengganu and Kelantan indicated drastic increase of RPW weevil (DOA, 2016). 

The weevil is believed to be introduce by date palm trees which brought in across the border either for the date palm plantation or landscaping purposes without proper quarantine several years back (Wahizatul et. al., 2013). The weevil is a conceal tissue borer that attacks more than 26 palm species worldwide belonging to 16 genera, including coconut, oil palm and sago palm (EPPO, 2007) 

Accessing and visual detection of the weevil infestation is difficult and almost impossible thus, an alternative have been evaluated. Soroker, 2013 has deployed a system to detect RPW larvae, the canopy temperature based on aerial thermal images using semi-automated procedures used to map potential infestation of RPW larvae caused water stress, which was reflected by both higher canopy temperature extracted from thermal images and lower stomata conductance compared with healthy trees. 

The water stress was detected 25 days after infestation, three weeks before visual symptoms were observed (Figure 7). The thermal system is used in green house to collect thermal images, while Golomb (2015), applied thermal system in the field, the main goal of the study was to examine the ability to detect infected trees using thermal images. By measurements, imaging and analysing of infected and uninfected trees over multi-year experiments in quarantine and commercial orchards, results was (partially showed that the RPW creates water stress and affects canopy temperature. Analysis of the aerial thermal image above date palm plantation successfully detected the infected trees, which was similar to Soroker (2013) results.

 Figure 7: Thermal images of palm trees infected and uninfected by RPW. 

   

Figure 8: Thermal system for RPW detection in the Field Figure 9: Thermal system for RPW detection in the Green House

Discussion and Conclusion 

The application of thermal remote sensing application was developed and introduced in 1960’s, mainly to monitor thermal bridging in the buildings and overheating processes such as engine and electronic devices and the energy industries. Due to reduction of costs of the equipment and simple application techniques, it creates an opportunities in area of agriculture and currently have been used in precision agricultures (Prakash, 2000). 

Thermal imaging was a better alternative tools for many application in agriculture, starting from pre-nursery, nursery, irrigation scheduling (Jones, 1999) yield forecasting (Stajnko et.al., 2004) harvesting, green house, termite attack, farm machinery and post-harvest operations and  bruise detection (Varith et. al. 2003). Several utilisations in metrology with pest management may be conducted efficiently. 

However, in contrary with earlier statement with the used of thermal imaging in reality not getting much attention by researcher as well as agriculturist due to reasons which led reduce in using. The system was seen as an expensive due to camera price and limits the application only to laboratory settings and high value target analysis.  Further weather condition in the field limits the automation of thermal data and often imaging sensor calibration and atmospherics correction are a must. Thermal imaging need to be improved in time and more research need to be conducted. 

Infra-red or IR imaging may be an option or perhaps the combination of both thermal and IR application in some cases may be a solution. With the invention of enhanced thermal sensor cameras will bring more interest and challenge in this relatively less explores field. There are a definite need to get to more understanding thermal data by scientific and application. The development should be focuses on fundamental and principle of thermal remote sensing, laboratory measurement and spectral response of natural materials in the thermal infrared region which lead to a more precise and sophisticated sensor technology. 

Review on case study reveals that thermal imaging will be appropriate with present in large animal such as wildlife and large insect especially termites. Study by MOPB for monitoring or census on the infestation of bagworm were on infested palm leaves whereby when monitoring was conducted it has already perhaps at the late stages. However a combination of conventional methods such as observation and IR images may be a good practices if plantation staff and personal aware of bag worm present at early stages. IR imaging method may be used to quantify the infestation stages. 

 

References

Abbas, M.S.T. (2010). IPM of the Red Palm Weevil, Rhynchophorus ferrugineus. Plant Protection Research Institute, Cairo, Egypt.

Ahmadi, P., F.M. Muharam, K. Ahmad, S. Mansor and I. Abu Seman, 2017. Early detection of Ganoderma basal stem rot of oil palms using artificial neural network spectral analysis Plant Dis., 101:1009-1016. 

Barlow, H.S., 1982, An Introduction to the moths of South East Asia. Art Printing Work Sdn Bhd Kuala Lumpur. P. 305

Barrett, E.C. and Curtis, L.F. (1992). Introduction to environmental remote sensing. Chapman and Hall, London.

Cheng, S., Kirton, L.G., and Gurmit, S., 2008. Termite attack on oil palm grown on Peat Soil: Identification of Pest Species and Factors Contributing to the problem. The Planter, Kuala Lumpur, 84m (991): 200-210. 

Croon, G.W., McCullough, D.R., Olson, C.E. and Queal, L.M. (1968). Infrared scanning techniques for big game censusing. Journal of Wildlife Management, 32(4): 751-759.

DOA, 2011. Report on current status of attack of the Red Palm Weevil, Rhynchophorus ferrugineus in Terengganu. Biosecurity Division, Department of Agriculture, Malaysia.

DOA 2016. Report on current status of attack of the Red Palm Weevil, Rhynchophorus ferrugineus in Terengganu. Biosecurity Division, Department of Agriculture, Malaysia.

EPPO (European and Mediterranean Plant Protection Organisation 2007. Rhynchoporous ferrugininues and R. palmarum. European and Mediterranean Plant Protection Organisation Bulletin 37:571-579. 

Fee Chung Gait, 2017. Review on Major Pets Management in Oil Palm. The Planter. Vol 93 No 1090 pp. 29-47.

Golomb, O., Alchanatis, V., Cohen, Y., Levin, N., & Soroker, V. (2015). Detection of red palm weevil infected trees using thermal imaging. Precision agriculture 15 (pp. 322-337) Wageningen Academic Publishers.

Ibrahim, Y., H.C. Tuck and KK Chong 2013, Effect of temperature on the development and survival of the bagworms Pteroma pendula and Metisa plana (Lepidoptera: Psychidae). J. Oil Palm Res., 25: 1-8. 

Holst, G. C. (2000). Common sense approach to thermal imaging SPIE Optical Engineering Press Washington, DC, USA:.

Ishimwe, Roselyne & Abutaleb, Khaled & Ahmed, Faruk. (2014). Applications of Thermal Imaging in Agriculture - A Review. Advances in Remote Sensing. 3. 128-140. 10.4236/ars.2014.33011.

Jones, H.G. 1999. Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling. Agricultural and Forest Meteorology 95:139-149.

Ken J, and David R., 2002. Finding termites with thermal imaging. ImfraMation 2002 ITC 035 A 2002-08-01

Kushairi, A., Soh Kheang Loh, Azman I, Elina Hishamuddin, Melina Ong-Abdullah, Zainalbidin Mohd Noor Izuddin, Razmah G., Shamala Sundram, and Ghulam Kadir Ahmad Parveez, 2018. Oil Palm Economic Performance in Malaysia and R & D Process in 2017. Jurnal of Oil Palm Research Vol 30 (2). 

Lloyd, J. M. (2013). Thermal imaging systems Springer Science & Business Media.

Lillesand, T., Kiefer, R. W., & Chipman, J. (2014). Remote sensing and image interpretation John Wiley & Sons.

Nordiana Abd Aziz, Wahid Omar, Rohani Kassim and Norman Kamarudin, 2012. Remote Sensing Measurement for detection of bagwork infestation in oil palm plantation. MPOB Information Series ISSN 1511-7871, June. 

Prakash, A. (2000). Thermal remote sensing: Concepts, issues and applications. International Archives of Photogrammetry and Remote Sensing, 33 (B1; PART 1), 239-243.

Rogalski, A. (2003). Infrared detectors: Status and trends. Progress in Quantum Electronics, 27(2), 59-210.

Soroker, V., Suma, P., Pergola, A. l., Cohen, Y., Alchanatis, V., Golomb, O., et al. (2013). Early detection and monitoring of red palm weevil: Approaches and challenges. Colloque Méditerranéen Sur Les Ravageurs Des Palmiers, Nice, France, 16-18 Janvier 2013,

Stajnko, D., M. Lakota and M. Hocevar. 2004. Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging. Computer and Electronics in Agriculture 42(1):31-42

Wahidzatul, A.A.; Zazali, C., Abdul Rahman A.R. and Nurul Izzah, A.G (2013). A new invasive Coconut Pest in Malaysia, The Red Palm Weevil (Curculionidae: Rhynchophorus fer

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Question and Answer on Precision Agriculture

Question 1: Discuss three possible outcomes of Precision Agriculture (PA) with regard to crop yields. For each outcome, state a logical time frame.

Precision Agriculture (PA) that define by United States Department of Agriculture or better known as USDA as a management system that is information and technology based, is site specific and uses one or more of the following sources of data: soils, crops, nutrients, pests, moisture or yield, for optimum profitability, sustainability and protection of the environment. Like any system that is designed to provide improvement, there is a cycle of events which should take place to monitor its effectiveness and helps to understand some of the definition.

Thus, 3 possible outcomes of PA with regard to crop yields are as follows, 

1. Higher yield, similar cost.

The most challenging task by farmers and planters today, with due respect to the increase human population are the increase demand of food sources with limited area available to be planted. Thus, among the strategies are to increase production with same level of agriculture input such as total acreage of land and same level of fertiliser as well as pest and disease control practises.  To adapt, planters are looking for a better management practises and tools whereby PM are being exploited in such a way that yield will be increased, waste will be reduced and mitigate the economic and security risks faced by agriculture industry.

The aim of PA is looking at the increased efficiencies of agriculture inputs after taking into an accounts of understanding and the implementation that will be in line with natural variability exist in the field. Further, yield is not a goal but to better manage and distribute inputs on site-specific basis to better optimised long term cost and benefits.

Survey conducted in a paddy field shows that even the same level of inputs such as seeds, fertiliser and pesticide at the same location or place may not produce high yield due to differences in soil fertility, water management and human factor. For example, with the implementation of PA, soil fertility maps may be produced and decision can be made on the amount of fertiliser apply with priority made on the less fertile soil area. More fertilizer will be applied in less fertile and less at fertile zone. However, total fertilizer consumption will be the same if looking on big scale of land available. 

2. Same yield, lower cost

Increase application of agriculture inputs such as fertilizer may not always end up with increase in yield, but simply hold them constant whilst reducing input costs. PA enable planters to reap increasing profits through better management and the application of more appropriate or reduce chemical treatments also helps to preserve the environment.

PA such as remote sensing is used to detect soil fertility, crops health and provide tools to detect pest and diseases (P&D) as early as possible whereby early measures can be implemented before major outbreak and planters may experiencing lowering losses and profits will be optimised. Applying the same level of pesticide across the entire field may no longer the best policy however, information obtained from PA by using Global Positioning System (GPS), Remote Sensing, Variable Rate Application (VBA), Neural Network and Decision Support System are utilised to decide the best solution. 

For example, the level infestation of bagworm can be detected at early stage and major outbreak can be avoided when the application of pesticide can be conducted during instar 1 to 4 of the life cycle of bagworm. The data obtained may be a tool to decide on action to be taken in the field and ensure at the right rate, right time, right place and right type of chemical used. Thus, early detection may promote less pesticide used in the plantation. The yield will be ensured with the proper management of P&D in the field. 

3. Higher yield, Lower Cost

The application of PA, may be leveraged on the combination use of sensors, robots, GPS, mapping tools and data-analytic software to customised plant care without jeopardising labour costs. Images on plants individually produced by stationary or robot-mounted sensors and camera equipped drones wirelessly may provide information on stem size, leaf shape and soil moisture content. The information may be used to monitor on plant health and enable planters to decide to plant or harvest crops. Further, PA can improve time management, optimising water, chemical and produce healthier crops with result of better and higher yields. 

In a related development, seed producers are applying technology to improve plant “phenotyping.” By following individual plants over time and analysing which ones flourish in different conditions, companies can correlate the plants’ response to their environments with their genomics. That information, in turn, allows the companies to produce seed varieties that will thrive in specific soil and weather conditions. Advanced phenotyping may also help to generate crops with enhanced nutrition.

A survey on soybean growers is US, reveal that 

• Growers report an average savings of about 15% on several crop inputs such as seed, fertilizer and chemicals.
• Savings on inputs often pays for the technology within a year for a large cropping operation and two to three years for smaller operations.
• Growers are increasingly using precision tools to conduct their own comparisons on their own ground.
• Findings show that most growers, particularly those with more than 500 total acres, are using several precision farming technologies.
• The larger the acreage increase, the more likely the farmer is to use multiple precision farming technologies.

For example, seed is an input where costs have risen dramatically. Avoiding overlap with new technology not only reduces total input costs, but it also improves yields on acres that used to have poor production because of too much seed.  In any given season, growers often find themselves ordering extra seed to cover their acres, but growers who use automatic clutches often return the extra seed, using exactly as much as their acres call for. The savings go directly into their pocket. While growers who do not use precision technology believe that they can save money other ways, like buying at a discount, the combination of wise buying and precision application can save even more.

Question 2: Remote Sensing (RS) is a key enabler of precision agriculture (PA). Discuss how remote sensing can be used for crop stress detection as a function of time (temporal variability).

Remote sensing can be describe as a technology to acquire and extract the information about the earth surface or any phenomenon without making any physical contact with the object. Common understanding of remote sensing is a satellite technology that capture the picture of the earth including earth surface, atmosphere and ocean. Nowadays, remote sensing is widely used in many applications such as geology, oceanography, cartography, land survey, military and also agriculture. Remote sensing can be divided into two category, active and passive remote sensing. Passive remote sensing is the sensor capture the reflectance of sunlight from the objects. Example of remote sensing satellite such as LandSat-TM, SPOT-5, IKONOS and the latest is WorldView-IV with high-spatial resolution imagery. Meanwhile, active remote sensing required the sensor to transmit the signal to the object, and emit the reflected signal or back-scatter from the object. Example of active remote sensing system such as Radar-Sat and LiDAR.

Remote Sensing used for Crop Stress Detection.

There are four characteristics of remotely sense data, spatial resolution, spectral resolution, temporal and radiometric resolution. Spatial resolution represents by value of pixel size of the imagery. Spectral resolution represents by number of bands of the imagery. Temporal resolution represents the duration of satellite to visit the same location at certain time. Meanwhile, radiometric resolution is representing the value or size of information that contain in the satellite imagery. For agriculture application, spectral and spatial resolution is very important to fulfil the requirement of analysis. Using multi-spectral or maybe hyper-spectral remote sensing data, we can identify the vegetation or crop behaviour. The reflectance capture by sensor come in several type of wavelength. Common sensor or normal camera only captured the visible wavelength. However, some of satellite sensor or camera can capture infra-red wavelength and consider as multispectral sensor. 

With multi-spectral remote sensing data, we can analyse the crop throudh vegetation indices method. Mostly used vegetation indices is Normalize Difference Vegetation Index (NDVI). In order to analyse the crop using vegetation indices, the sensor must consist of visible wavelength (Red, Green and Blue) and Near Infra-Red (NIR) wavelength. Near Infra-Red wavelength is very sensitive with chlorophyll, the lower value of reflectance or Digital Number (DN) from the object represent the lower chlorophyll content. In general, low chlorophyll content represents the stress crop. Due to limitation of multi-spectral satellite imagery availability cause of low temporal resolution and cloud contamination in the imagery, crop stress report from satellite imagery is not really significant and applicable compare to daily agriculture practice.

For example, using high-resolution Worldview-II satellite imagery for crop stress study at paddy field. This satellite platform carries multispectral sensor consist of 8 different wavelength (band).  However, the crop stress study can only be made when the satellite imagery is available (free cloud cover) at the study area with most recent time.  Mostly, the crop stress study happens for the previous time, not in real time. Using drone technology with multi-spectral sensor, crop stress monitoring can be emphasis in daily agriculture practice in almost real time and crop rehabilitation is more efficient.

Question 3: Discuss the key differences in precision agriculture implementation between annual cropping system and perennial cropping system (specify your choice of crops)

Precision agriculture (PA) can be defined as management of spatial and temporal variability in fields using information and communications technologies. Temporal changes within or between years have been addressed in good agricultural practise by means of laboratory analyses of example spots, while spatial patterns of plant growth, which have also been known for a long time, have been quantified in large scale with the assistance of PA. PA is, therefore, also referred to as site-specific management. This approach considers a management system for farms that aims to increase yield or sustainability. PA can assist farmers, because it permits precise and optimized use of inputs adapted to the apparent plant status, consequently leading to reduced costs and environmental impact. Because the practise provides record trail, enhanced traceability of farm activities can be obtained that consumers and administration increasingly require.

Precision horticulture targets individual trees or zones of tree blocks adaptively to its apparent status that shall trim down environmental footprint of fruit and vegetables production through enhanced resource efficiency and improved production performance. In horticulture, quality analysis of the product is more important than in any other crop. The field size is frequently smaller compared to arable production. The planting density is lower and even single plants may be treated individually adapted to the spatial or temporal pattern. The plant architecture is more complex with planting systems of single rows and missing trees in rows may occur 

Horticultural crops are divided into annual and perennial crops. In the latter, the planting system remains stable over years, while morphological adaptation of canopy and root develops according to the environment. Temporal data over more than one season are important, since historical plant data potentially provide valuable information on the status of endogenous growth factors, e.g., the status of phyto-hormone sand assimilates. Horticultural products are the result of many manual operations and hand harvesting. In perennial fruit trees, even additional production measures are requested, e.g., thinning of flowers and fruits, pruning. In orchards, structures for irrigation, hail net or frost protection are limiting the use of methods for soil mapping, e.g., for electromagnetic measurements, which are disturbed by iron installations.

Annual cropping 

Applications in mechanically harvested vegetables have also been presented: Pelletier and Upadhyaya (1999) developed a yield monitor for processed tomato using load cells under the conveying chains of the machine. Hofstee and Molema (2002) presented vision system for potato yield mapping. A colour line scan camera above the conveyor belt captured 2D pictures of the potatoes. Correlation between potato size and weight was established and used for estimation of potato flow in the machine. Yield estimated by the sensor compared to yield weighed on the platforms showed good precision between 3.5 and 4.6%. Yield mapping systems for potatoes based on load cells have shown similar good results of approximately 5% measuring uncertainty (Rawlins et al., 1995).

Most horticultural crops are not mechanically harvested and therefore many customised approaches for specific horticultural crops have been tested for yield mapping. In Florida citrus plantations, Schueller et. al. (1999) used a system to weigh palette bins where oranges were collected. Each worker got picking bags to collect fruits picked manually. After filling, bags were emptied in nearby tubs or pallet bins placed between trees (Whitney et al., 1999). Bins were removed by hydraulic lift, which used load cells for weighing, and GPS to record the position of the bin. It was assumed that each bin represented yield of surrounding trees.

A reasonable assumption since workers would empty their bags into the nearest bin. Yield was estimated by dividing weight by area covered by each bin. Position and yield were used to prepare yield maps. Spatial variability of yield was observed in a 3.6 ha orange orchard. Results were confirmed in Mediterranean growing regions in grapefruit (Peeters et al., 2015). 

For apple orchards, Aggelopoulou et. al. (2010) mapped yield, where apples were handpicked and placed in 20-kg plastic bins along rows of spindle-formed trees. Each bin was weighed and geo-referenced using DGPS. The bins, corresponding to 5 or 10 trees, were grouped to represent their yield. The estimation of yield of each tree was not possible due to spindle formation, where branches of adjacent trees were coinciding. The system facilitated workers, who picked fruits continuously, and yield mapping did not interfere with their work. The same procedure for yield mapping wasalso performed for pears in a small field of less than 1 ha by Vatsanidou et. al. (2015). 

For palm trees, Mazloumzadeh et al. (2010) created yield maps as follows: a few days before harvesting the dates, locations of trees were surveyed and plotted as x-y co-ordinates, fixed at the south-western corner of the grove. Numbers were allocated to all trees located in the grove and, during harvesting, yield of each tree was recorded. In plum, hand-picking was carried out in bins that were transported to the laboratory for single fruit analyses. Spatial pattern of yield and soil ECa was found in an orchard of 180 trees capturing 0.37 ha. Results pointed to low correlation of elevation, soil ECa and generative plant growth (Käthner and Zude-Sasse, 2015). Konopatzki et al. (2015) mapped yield in pear orchard of 5 ha size. They performed selective (n=3) harvests of 36 trees and recorded fruit mass, length and diameter, and soil properties. Results showing high variability of yield with coefficient of variation = 77%, and generally low correlations with soil properties. Perry et al. (2010) carried out yield mapping of pears by weighing total fruit mass picked per tree. They found that yield was strongly spatially clustered, suggesting possible management by zones.

Pozdnyakova et al. (2005) analysed spatial variability of yield in a cranberry plantation. They used 0.3 x 0.3 m frames to measure the number of fruits before harvesting. Using mean berry mass, they estimated the yield. High spatial variability was also observed here.

References 

Aggelopoulou, K.D., Wulfsohn, D., Fountas, S., Gemtos, T.A., Nanos, G.D., and Blackmore, S. (2010). Spatial variation in yield and quality in a small apple orchard. Precision Agriculture 11, 538–556. http://dx.doi.org/10.1007/s11119-009-9146-9.

Hofstee, J.W., and Molema, G.J. (2002). Machine vision based yield mapping of potatoes. Paper No. 02-1200 (St. Joseph, MI, USA: ASAE). Perennial Cropping

Mazloumzadeh, S.M., Shamsi, M., and Nezamabadi-pour, H. (2010). Fuzzy logic to classify date palm trees based on some physical properties related to precision agriculture. Precision Agriculture 11, 258–273. http://dx.doi.org/10.1007/s11119-009-9132-2.

Peeters, A., Ben-Gal, A., Gebbers, R., Hetzroni, A., Zude, M., et al. (2015). Getis-Ord’s hot- and cold-spot statistics as a basis for multivariate spatial clustering of tree-based data. Computers and Electronics in Agriculture 111, 140–150. http://dx.doi. org/10.1016/j.compag.2014.12.011.

Pozdnyakova, L., Giménez, D., and Oudemans, P.V. (2005). Spatial analysis of cranberry yield at three scales. Agronomy Journal 97, 49–57. http://dx.doi.org/10.2134/agronj2005.0049.

Rawlins, S.L., Campbell, G.S., Campbell, R.H., and Hess, J.R. (1995). Yield mapping of potato. In Proceedings of Site-Specific Management for Agricultural Systems, P.C. Robert, R.H. Rust, and W.E. Larson, eds. (Madison, WI, USA: ASA, CSA, SSSA). pp. 59–68.

Schueller, J.K., Whitney, J.D., Wheaton, T.A., Miller, W.M., and Turner, A.E. (1999). Low-cost automatic yield mapping in hand-harvested citrus. Computers and Electronics in Agriculture 23, 145–153. http://dx.doi.org/10.1016/S0168-1699 (99)00028-9.

Vatsanidou, A., Fountas, S., Nanos, G., and Gemtos, T. (2014). Variable Rate Application of nitrogen fertilizer in a commercial pear orchard. Fork to Farm: International Journal of Innovative Research and Practice 1(1).

Whitney, J.D., Miller, W.M., Wheaton, T.A., Salyoni, M., and Schueller, J.K. (1999). Precision farming applications in Florida citrus. Applied Engineering in Agriculture 15, 399–403. http://dx.doi.org/10.13031/2013.5795.

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