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UAV Platform for Precision Agriculture of Oil Palm

Introduction

Precision Agriculture of oil palm plantation has faced a breakthrough since rising of unmanned aerial vehicles (UAV). There is an increasing need for timely, accurate and detailed monitoring of oil palm growth, health assessment, fertilizer application, and inventory management. One of the most significant benefits of UAV technology is its flexibility that has filled the gap between satellite-based and ground-based monitoring and georeferencing system. Crop scouting and assessment is the discipline that deals with the observation and management of crops or to facilitate economical production. This involves constant sensing and monitoring of crop, environment, climate, soil, and pest/weed/disease.  

Problem statement:

Modern management of oil palm plantations encompasses regular inspection of the palms, mainly for nutrient content estimation, disease infestation, and yield monitoring. To conduct proper monitoring, growers take samples manually or employ remote sensing techniques. Several aspects of precision agriculture such as inventory management or quantification of the oil palm fresh fruit bunches are intensive labor tasks that are either ignored in large-scale plantations or are carried out manually by the use of hand counters. Plantation managers require a fast, accurate, and reliable tool to replace with the conventional inspection methods. 

Solution:

Unmanned Aerial Vehicles (UAV) can improve oil palm plantation management by providing aerial camera shots, multispectral measurement and remote sensing equipment in a single package. UAVs have versatile payload platforms, can cover large surfaces, are easy to operate and provide hard copy data that can be inputted instantaneously.  As a result, UAVs can improve the quality of oil palm production processes whilst lowering the crop management costs.

 

 

Project Objectives

The project "UAV Platform for Precision Agriculture of Oil Palm" was defined by Dr. Redmond Ramin Shamshiri at the Smart Farming Technology Research Center (SFTRC) of the Universiti Putra Malaysia with the following main objectives:

  • To support precision agriculture research of oil palms with a flexible remote sensing platform dedicated to timely, accurate and detailed data collection
  • To provide oil palm stakeholders with an affordable UAV-based yield monitoring and inventory management system
  • To provide plantation managers with a reliable tool for early detection and prevention of Ganoderma disease spread in oil palms

 

 Significant of the research

Compared to conventional remote sensing platforms such as satellite or ground based, the added value of the UAV-based remote sensing is in more dedicated research experiments. This includes, for example, higher frequent observations in time (e.g., diurnal observations), observations of an object under different observation angles for characterization of specific features such as vegetation indices, plam's height and crown diameter estimation,  more accurate palm census, improved disease detection, and more importantly the flexibility in use of camera’s and sensors types. In this way, laboratory type of set ups can be tested in a field situation and effects of up-scaling can be tested.

 


Greenhouse Technology

Modern closed-field plant production requires innovative methods for shifting from conventional greenhouses to smart controlled environments that benefit from natural resources for eliminating deleterious external conditions. The ultimate objective in this regard would be achieving high-yield and high-quality fruits at minimum possible cost.

Researchers at the smart farming laboratories of Universiti Putra Malaysia are engaged in developing innovative methods for shifting from fully-closed controlled environments to smart greenhouse systems (Shamshiri and Ismail, 2012; Shamshiri and Ismail, 2013; Shamshiri, 2014; Shamshiri and Ismail, 2014a; Shamshiri and Ismail, 2014b; Shamshiri et al., 2014a; Shamshiri et al., 2014b; Shamshiri et al., 2014c; Shamshiri et al., 2015; Wan Ismail et al., 2015). The concept of smart farming (Zheng et al., 2011) and adaptive management framework for plant production systems (Shamshiri, 2014) are new research approaches that can contribute to achieving ideal year-round microclimate growth condition. It is expected that the new design will be capable of eliminating the detrimental external condition, while at the same time benefiting from potential of natural climate to peruse the ultimate objective of achieving high-yield and high-quality production at the minimum possible cost. Face-to-face questionnaires with local growers indicated a common belief that greenhouse environments can be totally independent of external climatic conditions. In fact local business demands for designing a flexible closed-field production system which can cost-effectively maximize production of the candidate crops, regardless of the external climate.  This is obviously not practical since microclimate condition inside a greenhouse is affected by outside disturbances, i.e., climate variations, rains, wind speed and direction, solar radiation, temperature, humidity, and carbon dioxide. Disturbance variables also affect the controlled environment that cannot be manipulated, or they were not even considered in the control algorithm, i.e., plant transpiration at different growth stages (GS). Improving closed-field controlled environments for increasing potential yields and fruit quality while reducing production cost requires an in-depth assessment of the outside environment. The main objective should be providing each specific plant with the optimal condition at its different growth stages (GS). It is, therefore, necessary to begin the design by determining how close each variable is to the optimal growth condition before arriving at the conclusion about greenhouse structure, size, covering materials, site-location, growing season and even the correct crop to plant. 

This paper begins with a brief literature review of the light condition and intercepted solar radiation effects on tomato growth responses. In the next section, a computer application for performing light-based analysis of microclimate data is introduced. This program was implemented as one of the 6 toolboxes that were developed for an adaptive analysis framework (Shamshiri, 2014). Demonstration of the outputs are presented by sample results for two groups of data collected from an open-field and three different closed-field environments in tropical lowlands of Malaysia. For the purpose of this paper, only three main light condition levels including sun, cloud and night, have been studied, however, the original framework is capable of simulating all possible scenarios with respect to the crop being produced and priority levels (i.e., whether the tomato is for fresh market or processing industry). These priorities can lead to more proper decisions such as site location, greenhouse shape, covering materials and growing season.

 

Greenhouse tomato production

Tomato is warm season and will grow best with adequate sunlight and warm temperature. In spite of cold regions where greenhouses are generally used for year-round and off-season production, closed-field environments in hot and humid environments such as tropical lowlands of Malaysia are practiced mainly for protecting plants from extreme sunlight, temperature, rain hills, winds, insects, and possibly providing CO2 enrichment for attaining optimal plant growing conditions. Commercial growers are willing to produce maximum possible yields at the highest quality with minimum cost, however, the complexity of interactions between internal and external elements surrounding a closed-field environment beside other miscellaneous disturbance variables have burdened achieving these goals. It is therefore essential to have an in-depth understanding of the fundamental requirements for highly-demanded horticultural species (i.e., tomato, pepper, etc.) and their adaptation to local condition. Among these requirements is the plant’s need for minimum global radiation of 8.5 MJ/m^2/day, an equivalent of 2.34 kWh/m^2/day (Jones, 2007). According to Anderson (1996), light levels less than 15% of summer light will significantly reduce fruit yield in fall or winter crops in greenhouses. Gosselin (1996) reported that utilizing additional lighting supplements with PPFD of 150 micromoles m^(-2)  s^(-1) in low light hours improved plant growth and fruit yield. According to the same study, marketable yield increased by 4.1 kg/m^2 when supplemental lighting increased from 50 to 150PPF. Adegoroye and Jolliffe, (1987) found that greenhouse tomato growth and yields can be increased by applying supplemental lights. In addition, fruits developed under adequate light have had better appearance quality. In the other hand, high intensity of light combined with high air temperature can be destructive for tomato and lead to sunscald injury and uneven ripening syndromes in the growth development and visual appearance (Dorais et al., 2001). Growth competition between plants and fruits increases in low light hours due to the lower photosynthetic availability, which ultimately can result in reduced marketable fruit.

Artificial lights, shading screens, and plant density are the main methods of controlling greenhouse light and intercepted radiations. Little information is available about the effects of different microclimate condition on the quality of the tomato fruit due to different covering materials. Jarquín et al. (2013) studied the effects of double layer plastic and flat glass cover on the lycopene accumulation and color index during tomato fruit ripening. They concluded that lycopene biosynthesis in tomato fruits was increased by the amount of light after the beginning of ripening growth stage. Experimental studies show that air temperature and relative humidity in open-field are more favored by the plants compared to traditional greenhouses with pad-and-fan systems (Shamshiri et al., 2014a). In the other words, traditional greenhouses create an adverse situation for crop growth microenvironment, which needs the energy to return it back to the comfortable level that was already available by outside environment. Reports indicate that while outside temperature was around 28-33°C, the temperature inside a polyethylene film covered greenhouse without environment control reached to 68-70°C, leading to air vapor pressure deficit (VPD) of 4kPa (Shamshiri et al., 2014a). This causes high transpiration rates with significantly increases in evapotranspiration (ET) demands and stomatal closure. In this regard, insect-proof net screen covered greenhouses have become more popular in the lowlands of Malaysia. In a study by the Malaysian Agricultural Research and Development Institute (MARDI), average yield per unit area under rain shelters is about 2 to 4 times higher than open-field production (Illias and Rezuwan 1997). The structural design of tropical greenhouse has a significant role in the natural ventilation and the quality of inside air (Aldrich and Bartok 1994). Both of the two main processes in the plant growth, the photosynthesis and transpiration, can be enhanced by controlled environments (Heijnen 1996).Shading nets ease the natural ventilation process and can protect plants from excessive sunlight, wind, and heavy rains. Lorenzo et al. (2003) reported that movable shade under intense sunlight in Spain caused 10% increase in the marketable yield of greenhouse tomato. Net-screen greenhouses have gained more popularity in tropical regions due to the likely potential of air temperature and relative humidity that are naturally close to plants desired levels. These structures are generally used to reduce insect migration on the crop and subsequent crop damage, reduce risk of damaged by high rainfall, extreme solar radiation and high wind speeds. A comparison between different greenhouse covering materials, including polyethylene film, photo-selective red colored film, and insect-proof net, for tomato cultivation during summer was carried out by Arcidiacono et al. (2006). 

 

Shading cover nets changing incoming solar intensity affect carotene level and biosynthesis of lycopene in tomato fruits. In hot climates, total market yields can be increased with color shaded greenhouse, however, their application for greenhouse use in cold and cloudy regions with limited sun-light hours can be deleterious. Cockshull et al. (1992) reported that cover with 23% shade reduced greenhouse yield by 20%. Ilić et al. (2012) observed significantly higher lycopene content using red shade netting technologies in greenhouse tomato; however, these fruits had lower carotene content. The percentages of shading for highest tomato production are reported under 40% and 35% by El-Aidy and El-Afry, (1983) and El-Gizawy et al. (1992) respectively. According to El-Gizawy et al. (1992), a 51% increase in tomato production was resulted by increasing shading intensity. Management of planting spacing can maximize light interception and its efficient use by the crop. It is therefore important to have correct spacing between plant for providing uniform and adequate light distribution in the canopy. Higher density in tomato plants lessens radiation at the soil surface; however, plants absorbing more radiant energy will ultimately cause the soil surface to lose its moisture faster (Agele et al., 1999). In fact high plant densities are best used in situations with high light or where fruit size is not of great concern. Selecting proper planting density can optimize light interception and increase crop water productivity. Qiu et al. (2013) investigated the planting density effect on the evapotranspiration (ET) and yield of greenhouse grown tomato. Higher density increases total yield to a certain level. Several studies reported that parameters such as single fruit weight, fruit set per plant and numbers of flowering were all lower with too high planting density, consequently resulting in lower yields per plant (Agele et al., 1999; Amundson et al., 2012; Kirimi et al., 2012).

 


What is precision Agriculture?

Author: Redmond R. Shamshiri, PhD. Biosystem Simulation and Modelling 

  • Precision Ag is managing each crop production input (fertilizer, limestone, herbicide, seed, insecticide, etc.) on a site specific basis to reduce waste, increase profit and maintain the quality of the environment.
  • Precision Ag is carefully tailoring soil and crop management to fit the different conditions found in each field.

Introduction

There was a time when growers invested on expensive farm equipment for the sake of high technology and low energy consumption. Management assumed that sophisticated machinery were always more efficient. While this could be true to some extent, cutting-edge technologies such as precision agriculture and smart farming have opened new doors for cost-conscious farmers to apply modern management tools and to reduce the use of consumable field supplies and improve profit. Recent developments in electronic and computer has led to the invention of faster and lower-cost microprocessors that made possible manufacturing of smaller global position system (GPS) instruments and mobile based geographic information system (GIS) applications, both having  great influence in precision agriculture, with significant contribution to farm management and mechanization. GPS is a satellite based navigation system that defines position, velocity and time, (PVT), under any climate condition 24 hours a day anywhere in the world, for free. Originally developed for the military, the USA owns GPS technology and the Department of Defense maintains it. GPS has made a great evolution in different aspects of our today’s modern life as well as in agriculture section. Today, a growing number of crop producers are using GPS and other modern electronic and computer equipment and practice precision agriculture. The purpose of this article is to provide a quick review of GPS concepts such as coordinate systems and NMEA standards, and to highlight some of the applications in precision agriculture. 


Disease dettection

Reflectance spectroscopy provides non-destructive, rapid, reliable and precise analysis approach for plant’s health condition assessment. Currently, there are three main remote sensing platforms for this purpose, including close-range platforms such as ground based or handheld sensors, middle-range platforms such as drone-based sensors or imaging devices mounted on autonomous unmanned aerial vehicles (UAVs), and far-range platforms such as piloted airplanes or satellite-based sensors. Multi-spectral images contain monochrome images taken from sensors with four or more channels, each representing reflectance values for the objects in the image corresponding to a specific wavelength. Hyperspectral images can be acquired by ground-based, airborne or spaceborne hyperspectral cameras, with each image pixel containing hundreds or thousands wavelength bands (i.e., from 350 to 2500 nm). The spectral data of a pixel in a hyperspectral image is a combination of the reflected light from the entire features (i.e., moisture content and chemical elements) that exist in that pixel area. For example, a typical hyperspectral image in the size of  256 by 320 pixels that is acquired with an 118-channels hyperspectral camera will have 81920 spectra (or reflectance signal), each with a length of 118. The main difference between hyperspectral imaging and spectroradiometer devices is that data from hyperspectral camera includes spectral and spatial information, whereas a spectroradiometer only provides reflectance spectra. In the other words, a pixel in a hyperspectral image includes reflected radiation for the piece of object in that pixel over hundreds of wavelength bands. Indeed, the spectral data for that pixel has a similar shape as data produced by a spectroradiometer. An image from hyperspectral camera contains spatial axis, corresponding to a line of pixels, and spectral axis, corresponding to reflectance data distribution. 

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