Farmers ’ perspectives on the adoption of smart farming technology to support food farming in Aceh Province, Indonesia

: The possible future challenge for food agricul - ture development led to the transition from conventional to modern agricultural management using smart farming technology ( SFT ) . Some factors detaining the use of SFT for food commodities, speci ﬁ cally in small farmers ’ com - munities, are global climate change, low - quality human resources of farmers, and extension workers. Small farmers generally have relatively small land, limited access to capital and farming input, and grow di ﬀ erent kinds of commodities according to seasons. This research examined the adoption readiness in using SFT for three food commodities in Aceh Province, Indonesia, namely rice, maize, and potatoes. The sample comprises 70 farmers per commodity and 48 exten - sion workers, obtained through the quota sampling method, culminating in 258 respondents. The readiness measurement for SFT adoption was conducted by introducing various models, images, videos, and RITX applications. The col - lected data were investigated and analyzed using the Mann – Whitney and Kruskal – Wallis for two or more cate - gories. The result showed that both farmers and extension workers have a positive perception of the application of SFT. However, in terms of readiness, farmers have a rela - tively lower readiness level than the extension workers due to their low capacity. This means it is essential to focus on the economic and capacity building of farmers by pro - viding them with appropriate SFT devices to overcome the high investment cost and provide the technical skill for its application to overcome this situation.


Introduction
Population growth, changes in global climate, and lifestyles, such as the people's consumption patterns, affect a country's food security [1].Therefore, there is a need for significant alterations in the management of agri-food businesses to boost competition in terms of productivity, cost and labor efficiency, product processing, prices, and marketing [2].This gradually transitioned from conventional agricultural management to using smart farming technology (SFT) [3].SFT has already been implemented in Indonesia, although on a limited scale.Therefore, its adoption at farmers' level becomes necessary [4].
SFT is applied as an innovation in the input, processing, and output sections.It also involves improved methods and tools that boost agricultural development, such as drone technology, sensor, online applications, robots, e-commerce, and digital extension [5].Preliminary research related to the use of SFT focused not only on developed countries but also on developing ones [5].It has also been implemented in Indonesia, although on a limited scale [6].This condition is caused by several factors, such as restricted access to technology, unavailability of infrastructure, smallholder human resource capacity, and financial support from the government [6].
Developed countries, such as Canada, Australia, and Sweden, have employed SFT in the farming business, although its absorption is still low (approximately 35%) [7].Its implementation in Indonesia is still low and far behind other Asian countries, such as China and Thailand.The majority of farmers have been introduced to SFT through programs organized by the government.
Recently, the implementation of SFT in Aceh was realized through government assistance and the private sector.Among the technology used is an android-based application that functions as a monitoring tool, weather and soil fertility level sensors, plant pests and diseases detection, as well as market information on prices of commodities and communication forums for the optimization of small farmers.Consequently, this research aimed to achieve the following objectives: 1.What are the perceptions of farmers and agricultural extension workers in Aceh concerning the potential benefits of adopting SFT? 2. Does the difference in the size of agricultural land owned, level of education, gender, age, experience, commodities, nonagricultural income, land ownership status, and capital loan or credit significantly affect the potential benefits of SFT? 3. What is preventing Aceh farmers and extension workers from adopting this modern procedure?4. How prepared are they? 5. What types of equipment do the food farmers (rice, corn, and potatoes) prefer?
6. What strategies need to be employed to enable them to adapt to SFT?
This research starts with an introductory section, followed by sections.

Literature review
Various online platforms are available, and users can subscribe to diverse Internet of Things (IoT) applications.
Selecting the most suitable one is quite challenging.Mashal et al. [8] stated that users usually prefer the application of the three criteria, including smart objects and providers.Specifically, privacy, reliability, and availability are essential IoT criteria.In relation to agriculture, several researches have been carried out on smart farming and risk adaptation strategies.Information and Communication Technology (ICT) has an enormous impact on agriculture.As shown in Figure 1, its utilization with various technological tools has resulted in increased productivity, better use of resources, and reduced time required for agricultural management, marketing, logistics, and quality assurance [9].
The research carried out by ref. [10] and [11] in Punjab, Pakistan, and Ghana has revealed certain obstacles that are often encountered in the bid to apply "smart farming" technology.These include the low readiness of human resources, specifically extension workers and farmers, poor capital, lack of information and communication technology infrastructure, weak government budget allocation policy.On the other hand, the application of SFT has been proven to increase the competitive power of agriculture as well as have a positive impact on efforts to boost productivity, input efficiency, production costs, product processing, prices, and marketing that aids improve farmers' welfare and food security [12].Introducing this modern technique to small farmers is essential because it allows them to maximize yields with the least quantity of water, fertilizer, and seeds [13].Using sensors to map the fields, they tend to understand their crops on a micro-scale, conserve resources, and reduce environmental impacts for sustainable agriculture [14].
Many factors affect rice yields and its consumption, such as rainfall, humidity, and people's lifestyle [15].Therefore, due to its complexity, it is necessary to design a platform that predicts future harvests and demands.Muthusinghe et al. [16] introduced a smart one targeted at farmers familiar with the modules concept of rice harvest and price prediction.Ronaghi and Forouharfar [17] investigated their desire to use IoT technology as a necessary and fundamental prerequisite for implementing smart agriculture in Middle Eastern countries.It was stated that a good information technology (IoT) plan must consider its users' wishes.Agricultural policymakers need to set the stage for promoting this procedure by considering and justifying farmers' benefits in terms of optimal performance, minimal effort, ease of access, and usability, even in remote areas.
Various constraints, perceptions, and desires relate to using SFT.In accordance with certain preliminary research carried out in France, Germany, Greece, the Netherlands, Serbia, Spain, and the UK, it was reported that there are diverse perceptions of SFT in terms of boosting employment and the impact of agriculture on the environment [18].Generally, farmers across Europe stated that the barriers encountered during SFT implementation include its cost, incompatible devices, and the transformation of acquired data.Meanwhile, farmer-to-farmer communication is one of the most important sources of information.
Indonesian farmers are different compared to those from Europe and the Middle East.They are generally smallholders with average land ownership of fewer than 0.5 hectares and aged 52 years old.The majority only completed elementary and middle schools and earned an average income of USD 55.61 per month.Of course, farmers, including those in Aceh, find it difficult to use the SFT.However, it is important to the Acehnese considering its benefit, particularly in boosting farming practices and predicting the weather conditions.The majority are also not aware of the type of pests found in this area, as well as the needs of community members, thereby making it paramount to exchange ideas with various foreign experts [19].All these issues are expected to be resolved using SFT.Several types of equipment are in the markets but are ineffectual when Acehnese farmers cannot use it due to limited knowledge and skill.Based on that reason, this research tries to analyze how farmers and extension workers perceive the potential of SFT, specifically in terms of boosting food commodities, the various types, obstacles encountered during usage, and their readiness to take advantage of this modern procedure.
3 Materials and methods

Research sites
Aceh is a province located in the western part of Indonesia with a population of 5.33 million and a density ratio of 95 people/km 2 .It has abundant natural resources, such as forest products, plantations, agriculture, fisheries, and mining.This sector has been able to contribute approximately 10.83% of oil and gas commodities.In addition, the community's economic activities dominate the agricultural, plantation, and forestry sectors.The agricultural sector is business inclined and absorbs the majority of the labor while playing a major role in terms of boosting Aceh's economic growth in accordance with the annual contribution to gross regional domestic product, reaching relatively 30%.This is one of the provinces that received the "Gold Pin" for exceptional rice production performance and was ranked 8th nationally by the Ministry of Agriculture in 2019.It has a harvested an area of 310.012 ha, while producing 1714.438tons or 982.570 tons of dried rice [19].The utilization of SFT in agricultural operations is still low [6].At the same time, its usage in food commodities is predicted to increase its security and improve the economy.
As many as 36.13% of Acehnese work in the agriculture, forestry, and fishery sectors.Majority of farmers in Aceh Province, as well as in other regions, own small-scale businesses based on seasonal commodities.Due to its tropical nature, the cultivation process is not restricted by climate, and farmers are able to plant diverse commodities at different seasons.This affects their perspective of SFT because it tends to be risky to their businesses.Therefore, it is necessary to carry out further research to improve the adoption readiness in terms of utilizing SFT, specifically in food commodities, according to the potential needs of farmers in Indonesia.

Sampling
In Indonesia, there are generally small farmers with limited financial capacity and the ability to utilize technology.Extension workers play an important role in agricultural activities.In terms of implementing SFT, it is necessary to analyze their technological needs, interests, readiness, and perceptions.The acquired sample comprises of 48 agricultural extension workers, from each region that excessively produces food commodities.Extension workers for each commodity were those whose working area covers the location of farmers.Those whose work focused on the respective commodities were selected.This survey was designed and carried out in three districts in Aceh, using the quota sampling method.Generally, Acehnese farmers are not familiar with SFT; therefore, the selection of samples was expected to provide an overview of the comparison of their perceptions concerning the constraints of the commodity groups.Although it is not as accurate as random sampling, quota is reliable and safe in certain circumstances as long as some boundaries are clearly defined [20].Sampling procedure comprised of as many as 210 farmers, where 70 of them represented rice, corn, and potato commodities.The selected farmers own paddy, corn, and potato fields, and this grouping was carried out to distinguish technological needs, ideas, interests, readiness, and their perceptions of SFT.
Informed consent: Informed consent has been obtained from all individuals included in this study.

Ethical approval:
The conducted research is not related to either human or animal use.

Data collection
The questionnaire consisted of 63 questions for farmers and 64 for extension workers.These focused on whether they were familiar with or knew about SFT, their perceptions, barriers encountered, supporting facilities and infrastructure, ability to access information technology, and suggestions regarding usage [10,21].Farmers were asked to either agree or disagree with statements representing different points of view (using a Likert scale strongly disagree -1 to strongly agree -5) and answer and respond to multiple-choice and several open-ended questions.For example, how much is the household expenditure per month?how many hectares of land are cultivated?how many years have you been farming?how much is your monthly income from farming? how much is your monthly income from other sources?There are four types of SFT with examples of its tools circulating in the Indonesian market as referred to in the earlier mentioned questions.To ensure the clarity of the technology being inquired about, pictures of each SFT types were shown and explained to each farmer and extension worker before and during the distribution of questionnaires.The survey of farmers and agricultural extension workers was carried out by directly visiting their fields and offices in the three districts (Figure 2).Individual interviews with farmers lasted for approximately 30-90 min, while agricultural extension officers were for relatively 6 h.This comprised of introducing SFT through video presentations and pictures, descriptions of its benefits, discussions about certain opportunities, possible obstacles encountered by farmers that apply it, and methods of adoption.

Data analysis
All data obtained both from farmers (n = 210) and the extension workers (n = 48) were stored in the lime survey software, then downloaded for evaluation using descriptive analysis to provide an overview of perceptions, interest, and the readiness to use smart farming.The Mann-Whitney and Kruskal-Wallis methods were used to analyzing the factors that differentiate perceptions [22], such as land size, level of education, gender, farming experience, the dominant type of commodity cultivated, nonagricultural income, land ownership, access to credit, and farm income level.The Mann-Whitney U or Wilcoxon rank-sum test is used to evaluate the difference between two groups on one ordinal variable [23].Meanwhile, the Kruskal-Wallis is a non-parametric statistical test that assesses the difference between three or more independent sample groups on a single continuous variable that is abnormally distributed [24].
Statistical analysis was further carried out using SPSS.In terms of analyzing farmers' perceptions of SFT, all the statements were ordered according to the average Likert score.Furthermore, the scores and the associated standard deviation were used to identify the relationship between the strength of the agreement (i.e., the mean score, and its deviation).Farmers and extension workers' perception of SFT potentials is measured using the Likert value from the lowest (1) to the highest (5), in accordance with the following scale: strongly disagree (1), disagree (2), undecided (3), agree (4), and strongly agree (5).The results obtained are analyzed and classified into five classes, as shown in Table 2.

Perception of SFT adoption
Perception is an important variable that affects the speed of the adopted innovation [25].In this research, farmers' perceptions of SFT were assessed and measured by asking them to estimate its potential in overcoming certain challenges related to the need to increase the productivity and quality of food commodities cultivated.In addition, the perceptions of farmers and extension workers were assessed to evaluate the benefits of this modern technology according to the location and food commodities cultivated.Regarding the potentials (Table 1), both parties were asked for their responses from "strongly disagree" to "strongly agree." Table 3 shows the result of farmers and extension workers perception on SFT potentials based on the categories in Table 2.
The average value obtained from Table 3 is the total value of the respondents divided by the number of respondents.average   3 show that the average value of the perception of the sample farmers and extension workers is greater than 3, which reflects all sample farmers and extension workers agree that all the potential for SFT will be felt if it is adopted.The perception between farmers and between extension workers is also not far from the average rating where the standard deviation values are all <1.
Based on the SFT descriptions and the respondents' responses, farmers and extension workers tend to have a relatively similar perception, as indicated in the aggregate results (Table 3).Generally, both parties have good and very good perception, and this is indicated by index values between 3.4 and 4.63.It seems that extension workers have a higher expectation than farmers, and this is indicated by an aggregate index value, that is greater than 3.4 for all SFT potentials in the questions except for the "better than previous equipment" (index value less than 3.4).On the other hand, farmers perceived SFT as being less potential in terms of "usefulness," "increasing work comfort," and "better than previous equipment."A possible explanation to this tendency is based on the fact that the described SFT samples were not directly utilized by farmers but only introduced to them through videos during surveys and interviews.To improve the situation, a direct experience of its utilization is recommended.When carried out in a model area through farmers' group approach, they are bound to have a chance to directly observe the results and assess the relative advantages of this innovation, thereby resulting in a better perception and adaptability to SFT potential [24].The interval in each category is determined by a minimum value = 1, maximum value = 5, range = 5 − 1 = 4, class = 5, class length = 4/5 = 0.8.
Meanwhile, when viewed from the different perception test results with factors that influence them, two factors, namely land area and access to fund, were proven to have a significant influence (Table 4).This follows the perception of the potential utilization of SFT, namely the problem of input costs and productivity (Table 3).
Based on the analysis shown in Table 4, it is evident that farmers' perceptions regarding the scale of land ownership and access to funds or loans have a significance level of 5 and 10%, respectively.Judging from the land area factor, those who own more lands believe that SFT is useful compared to those who own smaller hectares (Figure 3).This finding is similar to Pivoto and colleagues [26] that the main influence that motivates farmers to adopt smart farming software for agricultural production, technical, and financial management activities is the size of the farm they own.Large farmers usually demand for more information tools to meet the complexities of their agricultural organization.In contrast, small and medium farmers do not need to control their land, rather they tend to manage it informally.This is slightly different from the findings of Pivoto and his colleagues [26] that the smallscale mean rank value of 93.40 was highly different from the medium-scale mean rank of 119.42.These results showed that the perception of farmers with medium-scale land is better and significantly different from those with small-scale land.The mean rank value of farmers who own small scale is lower than that of those who own large scale, although indicating insignificantly different.It is also evident that the mean rank value of medium scale is higher than that of large scale.This indicates that the perception of medium-scale farmers is better than that of large scale, although it is insignificant.
Meanwhile, based on the factor concerning access to credit funds loans, it was perceived that those who do not have access to credit fund loans greatly prefer to use SFT compared to those who have greater access (Figure 4).This implies that the existence of a loan or capital debt burden reduces the potential of farmers to apply SFT in terms of cultivating food crops.This is in contrast to the findings of Giné and Yang [27] that there is a positive correlation between rich farmers and the adoption of new technologies.Majority of the wealthy ones are purchasing new technologies to reduce agricultural risks.This difference in behavior is due to the dissimilar priorities between farmers in Aceh and Malawi.Generally, Acehnese farmers usually apply for loans to increase the size of their agricultural businesses.They are not aware of new technological support such as SFT; therefore, their interest in its adoption is low.
The perceptions of farmers with low, medium, and high levels of education are similar.The same is also applicable to that between men and women, young, adult, and old, inexperienced, experienced, and highly experienced farmers.There is also no difference in the perceptions of rice, corn, and potato farmers.In totality, there is no significant difference in farmers' perceptions of SFT based on the age category, experience, type of food commodity, income from outside the farm, land ownership status, and the amount of income generated from agriculture.
Based on the results shown in Figures 3 and 4, farmers owning a wider scale of land highly benefited from smart farming compared to those who own a smaller scale.This is possibly due to the fact that SFT utilization is more effective and efficient on a wider scale.However, farmers who have access to credit fund loans feel that the potential for using this modern technology is less compared to those who do not, as shown in Figure 4.This is due to the prioritized credit funds usage, and farmers tend to use it to obtain agricultural inputs such as seeds and fertilizers rather than invest in the usage of SFT for agricultural activities.

The factors inhibiting SFT adoption
Poor literacy of food farmers toward SFT causes them to encounter certain obstacles during its adoption.The condition of smallholder agriculture with low human resources causes them to be left behind in agricultural development [28].However, the obstacles encountered in adopting SFTs are not solely caused by farmers rather they are also caused by external factors [29].In this research, the factors that inhibit food farmers from adopting SFT based on their perception of its samples were introduced through dissemination and demonstration, as shown in Figure 5.We ask that respondents agree on the barriers to adopting smart farming.Respondents expressed their agreement by choosing the options strongly disagree, disagree, neutral, agree, and strongly agree on some of the obstacles listed in the questionnaire.We count the number of respondents agree and strongly agree with the given statement and express it in percent for each statement.
Figure 5 shows that the respondents (farmers and extension workers) perceived that the main obstacle encountered in the utilization of SFT in the cultivation of food commodities is high investment cost and lack of access to demonstrations.These are defined as inappropriate technology, complexity, inability to operate equipment, and difficult interpretation of data.Moreover, such inhibiting factors were observed among farmers in Pakistan and Ghana [10,11]."Lack of access to demonstration" is perceived as an accumulation of four other inhibiting factors related to farmers' inability to discern the advantages of SFT perceived as complicated, difficult to operate, and unclear usefulness of the data.
An entirely different observation of the inhibiting factors is shown in [30].Some research stated that the major  Exploring the smallholder farmers' adaption on smart farming technology  865 inhibiting factors in the adoption of artificial intelligence in Australia is the lack of organizational and leadership support.However, it was not only targeted at farmers but also at the organizational level.This implied that inhibition of SFT application tends to occur not only at the individual level (farmers) but also at the group or organizational level.
The unclear added value of the profit factor is the least inhibiting attribute that needs to be considered in the adoption of SFT.This finding is slightly similar to the observation made by [31], which reported the costs and financing of this process (costs and finance) have a negative significant impact (ß = −0.554,p < 0.01) on the eco-innovation.

SFT adoption readiness
The research development on adoption preparedness is generally related to climate change and disasters [32].However, the readiness to adopt SFT by small farmers in Indonesia is an important issue that needs to be studied because of the limited land area and business capital.By understanding their readiness, the type of SFT and the availability of supporting infrastructure in accordance with local potentials and farmers' needs can be identified.Table 5 shows the results of the SFT adoption readiness of the respondents.
Based on Table 4, the low readiness of small farmers to utilize SFT is generally influenced by their poor technical mastery of the different types described and demonstrated during the research.Those who cultivate food commodities are less able to use computers and their devices, smartphones and participate in online programs.Meanwhile, the availability of supporting infrastructure such as electricity is in good condition, as well as access to the internet and road to production centers are in a fairly good category.This phenomenon shows that the government has not optimized the SFT utilization socialization program for small farmers who cultivate food crops.In the meantime, among the extension workers, the readiness and technical capability in using this modern technology are better than farmers.For example, several central government and private programs have started to introduce SFT to extension workers through the training of trainer.It is expected that they would introduce SFT to farmers in their respective working areas.In Aceh, the reality is that it has not been widely practiced by smallholder farmers in the cultivation of food commodities.
Based on Table 6, it is evident that most of the extension workers are able to use smartphones and participate in online programs using the internet, computers, and their devices.Therefore, one of the adoption strategies for using SFT is their involvement in farm production management.These extension workers need to be motivated to help manage agriculture in their assigned areas.Figure 6 shows the types of SFT that are perceived to be absolutely useful in the cultivation of food commodity.This perception serves as an indication of the order in which the type of SFT is adopted according to the type of food commodity they cultivate.
We also show to farmers' information about the types of SFT in the form of pictures, prices, and functions.Then, we calculate the number of farmers from each type of commodity who are interested in using it, and we state the results of these calculations as a percentage as depicted in Figure 6.The result shows that for corn and potato farmers, the highest benefits of SFT types are FMIS or Apps, followed by autonomous machines, recording or mapping, and GPS or connected tools.Meanwhile, for rice farmers, the highest benefits are autonomous machines, followed by FMIS or Apps, recording or mapping, and GPS or connected tools.Based on these results, it was concluded that FMIS or Apps and autonomous machines are preferred by corn, potatoes, and rice farmers.However, these two devices are quite costly, indicating that a strategy is needed to ensure its adoption yields optimal food crops and is in accordance with the local potentials and farmers' needs.

Strategy to increase SFT adoption
The result in Figure 5 shows that, from the technological point of view, the main factors inhibiting SFT adoption are high investment costs and lack of farmers' capability to use this modern equipment.Therefore, it is classified into two categories, namely weakness in economy and personal capacity.This is further clarified from the fact that farmers' readiness level is affected by their inability to use smartphones, the internet, and computers.This is coherent with their hesitation to take part in the online programs.
Based on this finding, the strategy employed to increase farmers' readiness to adopt SFT needs to focus on their economic and capacity building.This entails providing farmers with appropriate SFT devices to overcome the high investment cost as well as organize capacity-building programs such as SFT training.In order for the strategy to be successfully implemented and the performance to be quantitatively measured, there is a need to employ a model

Discussion
Generally, farmers and extension workers have a positive perception of SFT.Table 3 shows that the average perception value of each potential SFT for both farmers and extension workers is greater than 3.This simply means that the both parties believe in the potential benefits of SFT if adopted.The result simply implies that this modern procedure tends to be greatly accepted or adopted by the farming community and supported by extension workers.This is an important step in terms of having a better understanding of their decisions.Nathanael and his colleagues adopted a similar step [33], and they introduced SFT the distribution of questionnaires to corn, soybean, wheat, and cotton farmers who had purchased plates in the form of SFT from a farm company.However, in this research, SFT was introduced through videos, pictures, descriptions, and demonstrations of android-based applications.Nathanael and his colleagues acquired samples of 837 farmers by selecting 20.5% of the sample consisting of agriculture with an area of wheat and 4.5% of the sample consisting of agriculture with a cotton area.In this research, the respondents for each commodity are 70 people, and it is considered adequate in terms of representing farmers' commodity group in Aceh due to the fact a great deal had not used SFT.In another research, Kernecker and colleagues [21] examined farmers' perceptions of this modern procedure, while focusing on the perceptions of those who had adopted it and those who had not used SFT.It was concluded that both parties had similar perceptions.The number of adopters is directly proportional to the farm size owned by farmers, and this is in line with the findings of this research, as shown in Figure 3.Those who own larger land or farm size have a different perception of SFT adoption compared to those who own smaller size.
This research discovered that farmers who took credit loans were significantly different from those who did not, and this finding is inconsistent with other research.Meanwhile, those who took credit loans have a lesser positive perception than those who did not, as shown in Figure 4.In contrast, those who own larger land or farm size have a more positive perception of the potential benefits of SFT.This simply implies that those likely to adopt SFT applied for huge loans.
Therefore, SFT service sellers and the government need to target Acehnese farmers who have these characteristics to promote and encourage them to pilot others to adopt this modern practice.This information also helps the local government to minimize the risk of farming businesses by ensuring all farmers groups are provided with SFT, thereby ensuring the organized agricultural land becomes wider and the smooth monitoring of this modern operations to yield more profit.
Generally, farmers are not ready to use SFT, as shown in Table 5.This is possibly related to their inability to use the internet and smartphones or follow online or computer's tutorials.On the other hand, extension workers have the ability to operate the equipment that supports SFT, as shown in Table 6.The main barrier to farmers' adoption of this equipment is the high cost and the lack of demonstrations.The types of equipment that are prioritized by food farmers (rice, corn, potatoes) are FMIS or App and autonomous machine, as shown in Figure 6.Therefore, based on these findings, the government needs to assist these farmers by providing them with these two tools.

Conclusion
The perception of farmers and extension workers in Aceh concerning the potential implementation of SFT is positive.Farmers believe that this modern procedure can: (1) reduce input costs, (2) provide better information for decision making, (3) limit agricultural pollution, (4) improve productivity, and (5) increase farm income.This finding is in line with Kernecker et al. [21].Meanwhile, the main factors influencing their perception are land area and access to funds, and this is consistent with Nyang'au et al. [21,34] and Kernecker et al. [21].Farmers and extension workers face similar obstacles in terms of implementing SFT, such as high investment costs, lack of access to demonstrations, size of land that does not support technology, and their inability to operate equipment and interpret data.
Irrespective of the fact that the facilities that support the implementation of SFT are quite good (electricity, internet, and road access), the ability of farmers and extension workers to implement this modern technique is still low.Different food commodities are cultivated, using diverse SFT types [21].Judging from the types, corn, and potato farmers, are more interested in FMIS or Apps, such as the RITX application.However, rice farmers are completely interested in autonomous machines, such as remote tractors and drones.Currently, several FMIS or Apps can be used for free for certain features, such as pest detection, monitoring land area temperature, market prices, and agricultural consulting.Its introduction to extension workers is the right step to help educate these farmers.
The government can also help farmers by providing drones or autonomous machines to extension centers to be jointly utilized.The presence of these equipment is also highly presumed to aid farming activities.The support from various parties, specifically from the government, campuses, NGOs, private sector, and extension workers in the form of programs, demonstrations, tools, and initiatives, is needed for SFT to be enjoyed by farmers.
, where x is the value of perception, i show sample to i = 1, 2, 3, …, n , and n is the number of samples.The standard deviation value is obtained from

Figure 3 :
Figure 3: Box plot showing comparative distribution of perceptions on the potential of SFT based on the land scale.

Figure 4 :
Figure 4: Comparative histogram of distribution of perceptions on the potential of SFT based on the access to credit funds.

Figure 5 :
Figure 5: The inhibiting factors for food farmers in adopting SFT based on their perception of the SFT.

Table 1 :
Variables in the research questionnaire

Table 2 :
Mean value of the respondents' answer

Table 3 :
The value of the perceived power of farmers and extension workers on the potential of SFT

Table 4 :
Different tests of factors that influence the perception of farmers on SFT ***, **, and * show statistical significance with confidence levels of 99, 95, and 90%, respectively.

Table 5 :
Farmers' level of readiness regarding support for the use of SFT

Table 6 :
Extension workers' level of readiness related to the use of SFT Exploring the smallholder farmers' adaption on smart farming technology  867 area approach, consisting of both farmers and extension workers, which cultivate similar food commodities.It has several positive impacts, namely (1) the possibility to control productivity and operational efficiency, (2) the ability to regulate the economic and value-added scale through agroindustry, (3) to stimulate and promote SFT innovations that are locally adaptive, and (4) to facilitate the commercialization and establishment of new food commodity businesses among smallholder farmers in the model region.However, a synergy or collaboration of the government, private sector, universities, and farmers is required for its success factors.
Fair Figure 6: Types of SFTs that are considered most useful for which types of agricultural commodities they cultivate, by size class, and adoption.