Abstract
Technology is changing the ways, techniques, and approaches many old and tiresome chores are conducted, to the point where thinking about the lack of these current technology a few decades ago might make one's mood worse. Intelligence system has enhanced nearly every part of human existence, and given the key roles it has begun to play in agriculture, researchers and scientists are looking for new ways to improve and ensure the optimal performance of the established systems. This research examines is on the design and implementation of intelligence weeding system, it examine the use of Intelligence system as the most technologically efficient method for weed detection. The researcher reviewied related scholars work. Based on their degree of relevance to the investigation, a streamlined classification and categorization was undertaken. The adopted methodology, nature of inputs, processing methods, and results from numerous studies were all taken into account. The roles of intelligent systems in agricultural operations were highlighted in order to extend reasoning and channel future research toward producing better and more technologically efficient intelligent systems to aid in agricultural activities. According to research findings, technological advancements in the introduction and use of Artificial Intelligence (AI) systems will result in a more technologically efficient way for weed detection in agriculture.
CHAPTER ONE
INTRODUCTION
1.1 Background of Study
Information is of crucial significant in empowering farmers to improve their livelihood. This implies that essential information such as weather information, storage information, sowing, improving soils, on the lookout for the finest cost of produce, pest control all empower farmers and influences their decision-making. This is because inadequate information on weather conditions, soil erosion, floods, droughts, pests and outbreak of diseases make decision making difficult for farmers (Lokeswari, 2016). However, the timeliness of such information and its relevance to farmers’ specific field needs is an uphill task in the face of an increasing shortage of extension staff and other physical and policies related challenges bedevilling extension service delivery. As a result of these problems that are encountered by small-scale farmers, the emergence of Information and Communication Technology (ICT) becomes timely (World Bank, 2011). Nonetheless, farmers’ ability to efficiently use these information communication technology platforms in accessing extension services remains very sacrosanct to maximizing the gains of this service delivery option. Furthermore, a forum that provides farmers with the opportunity to reach new markets and power to bargain by interacting with trader and government agencies through information communication technology is lacking (Srivastava, 2018).
In agriculture ICT tools are utilized to disseminate recent information and to enhance the usage of the existing ones. In Many developing countries, different technologies are used for agricultural and economic development (Chhachhar et al., 2014). Kabir (2015) asserted that information and skills gap inhibit the adoption of new technologies by farmers and reduces their technical efficiency. This implies that improved productivity by farmers demands that farmers get relevant information at the right time. However, little success has been accomplished hence broad utilization of contemporary information technologies has to be encouraged and implemented as information is fundamental for encouraging agrarian, rustic advancement and bringing around social and financial changes (Oladele, 2015).
Several efforts have been put in place by agricultural extension agencies, especially the public extension services which have used various approaches, strategies as well as programmes to make sure that farmers adopt advanced technologies, for instance, the Department of Agriculture Forestry and Fisheries (DAFF) in an endeavour to rejuvenate the outlook of extension service in South Africa launched an Extension Recovery Plan (ERP) in 2011, the programme hinged on 5 principles which included; to guarantee responsibility and detectable quality of extension , to ensure competence and improve the outlook of extension and the involvement of other stakeholders, selection and training of extension officers, re- skilling and re-orientation of extension and to make available ICT infrastructure such as laptops, mobile phones, digital pen and Extension Suite Online (Liebenberg, 2015) .
Consequently, the integration of ICT in agriculture as embedded in the program has rapidly changed the way agricultural technologies are transferred. This therefore, resulted in a transformation in agricultural practices due to farmers improved access to timely and relevant information and sharing knowledge (Agha et al., 2018). Furthermore, the favourable attitude of farmers is required to achieve the benefits of ICT in extension program planning (Raghuprasad et al., 2012). Attitude is described as an intricate group of beliefs, values, feelings and dispositions, which are personified by the way, we reason or feel about certain people, situation or thing, which are a product of a person’s life experience (Aiden and McCarthy, 2014). Otherwise stated, attitude is a predisposition to act in one way or another toward an object, situation or person (Johnston, 2011).
According to Shiro (2008), rural dwellers have a positive attitude towards ICT and accept any ICT developments in their communities. Nevertheless, ICT usage amongst this group of people (farmers) is at minimal due to lack of ICT knowledge.
Besides the technological evolutions, robotics is a fast growing field; research and development of producing new robots provide enormous practical grounds whether home like, economically or aggressively. Applying automatic technology to agriculture has a several improvements to the industry which help the farmers to save money and time. In flora, the management of weeds depends on manual weeding and herbicides. In order to control the weeds, herbicide is not a good choice. To vanquish these effects many methods were suggested for removing the weeds that is chemical weed removing, electrical weed removing and mechanical weed removing. One of the three methods is the desired method that is mechanical removing which has a three main techniques which is used most widely in burying weeds and cutting weeds. (Cloutier et al., 2007).N.D.Tillett et al. (2008) developed an experimental machine for inter row and within row weed removal. A hydraulic driven disc which is used to remove weeds within the row and moreover it had an interior section to avoid damaging of crops.
The application of technological gadgets embedded with Artificial Intelligence (AI) in agriculture is currently yielding significant results in weed detection and improving crop yield, thus necessitating the need to consider the technological roles of Artificial Intelligence in Weed detection. This paper describes the detection and removal of weeds based on machine vision in the inter rows and within rows in agriculture field.
1.2 Statement of Problem
Presence of weeds increases the cost of agriculture and hinders the progress of work. It increases the irrigation requirement. They reduce the value of produce or otherwise adds the cost of cleaning. ny other pest (Akobundu, 1987). Weed problems are also reflected in the costs of hiring labour to carry out land preparation and weeding (Doll et a!', 1977). Weeding is time-consuming. According to Harsch (2004), out ofthe total labour input of African women in rice production, 40-60% is spent on weeding. According to Le Bourgeois & Marnotte (2002) about 60% of the time in farming is spent on the first clearing ofthe farm and on weeding, representing 140-190 man-days per ha. The detrimental effects ofweeds in Africa far exceed the world average. It is estimated that in Africa yield losses range from 25% to total crop failure, depending on many factors among which weed pressure, availability ofimproved weed control technology, cost of weed control and level ofweed management practised by farmers (Akobundu, 1987; Van Rijn, 2000). The majority of farmers in Nigeria identified weeding as the main constraint in their farming system, with a major effect on yields (Amanor, 1994). In Benin, investigations carried out in the different agro-ecological zones revealed that weeds are a serious constraint on crop production (Carsky et a!', 1994; 2003; Gbehounou, 1998; Chikoye et a!', 1999; 2002; Ahanchede, 2000; Gbehounou & Adango, 2003). Spear grass (Imperata cylindrical interference can cause crop yield losses as high as 80% in cassava and 50% in maize (Koch et a!', 1990; Chikoye et a!', 2001). Striga caused total crop losses on over 15,000 ha and was present on about 20,000 ha of fallow land, parasitizing wild hosts (Favi, 1986). Only in newly opened land is weed infestation limited and one weeding is enough to get a good yield. Weed problems have been aggravated and have become particularly acute as a result of population pressure, ofshortening or eliminating fallow periods, of scarcity oflabour, and of the collapse of commodity prices, particularly of cotton. This listing makes clear that weed problems need to be understood in the context of both the biophysical (soil, crops) and the socio-economic and political environment. In Benin, weeding is one of the most difficult and stressful farm operations. The drudgery associated with weed control is due to hand weeding, which is the method used by the majority of farmers. Family labour is seriously stretched on large farms and has to be deployed continuously for weeding, as the first weeded plots are re-infested by the time the last plots are cleaned. Farmers have no rest and sometimes have to give priority to other farm (and non-farm) activities based on opportunity cost (P.V. Vissoh, personal observation).
Though automated weed control solutions are now available, they have yet to be broadly adopted. Current systems are simply unable to differentiate weeds from crops at a sufficient ground speed to support wide-scale adoption.It is on the premises of this background that the study seeks to investigate the design and implementation of intelligence weeding system.
1.3 Research Questions
The following are the research questions that propelled the study thus;
- What are the available weeding system accessible to small- scale-farmers?
- What are the small-scale farmers’ knowledge of the prominent information communication technologies for weeding system?
- What are the constraints in the adoptio of intelligence weeding system by farmers?
1.4 Objective of the study
The main objective of this study is to design and implement an intelligence weeding system to assist farmers.
The specific objectives are:
- To develop o an autonomous system for broadacre agriculture capable of undertaking a range of agricultural tasks, with a focus on weed management
- To Propose a K-means clustering method for weed detection using inter-row and inter-plant technique to detect and control weeds
- To detected weeds by combining image processing technique using intelligence system
1.5 Scope of the study
The study is restricted to the Design and implementation of intelligence weeding system. The proposed solution involves the use of a tool that is designed with the specific aim of detecting weeds in farmland.
1.6 Significance of the Study
ICT is an important tool in the dissemination of agricultural information to farmers. Through ICT the gap between agricultural extension agents and farmers could be bridged easily as farmers can have access to adequate information in a timely manner. The study is therefore expected to contribute to the advancement of knowledge to farmers for the adoption of better farming practices and weeding detection and control mechanism. Adequate development and implementations of intelligence weeding system were examined. The study therefore should accelerate small-scale farmers’ adoption of agricultural technology by the findings. This will be ensured by improving dissemination and access of ICT agricultural based information amongst the rural communities in the study which would assist in developing small-scale farmers towards food security in the form of optimal production and profit making.
1.7 Definition of terms
Information Communication Technology (ICT): Theses can be described as devices, tools or application that supports the exchange or collection of data through interaction or transmission.
Small-scale farmers: A group of individuals who reside mostly in the rural areas and engage in agricultural production of crops and livestock on a small piece of land without using advanced and expensive technologies.
Farm Management: these are activities carried out by a farmer in the on-going management of his or her farm and for which advice may be available from professional specialist and extension agents. It is the science (and art) of optimizing the use of resources in the farm component of farm-households.
Extension service: An organisation established by the Department of Agriculture to support and educate farmers on best agricultural practices and technologies for optimal production and profit making to improve rural livelihood.
Intelligence system:Any formal or informal system to manage data gathering, to obtain and process the data, to interpret the data, and to provide reasoned judgments to decision makers as a basis for action.
Weed: A plant that is growing in an area where it is not wanted. The complicated definition is: Weeds are plants which are undesirable, persistent, damaging and interfere with growth of other crop plants thus affecting human activities, agriculture, natural processes and economy of the country.
