Sampling is the operation that consists of taking a certain number of elements to process or observe, that is to say, that the sample is a subset of the population. For our case, we use the formula which allows us to determine the sample size using a 10% margin of error. We then apply:

N = Z²& × P × Q/E²

& = 0.5 implies that Z = 1.96.

P = 0.5.

Q = 1 − p = 0.5.

E = between 1% and 10%.

N: represents the sample size.

Z² = reduced center normal law.

& = represents the degree of confidence.

E² = represents the maximum or systematic error.

We will therefore have

N = 1.96² × 0.5 × 0.5/0.1² = 0.9604/0.01 = 97.

That is to say 97 market gardening professionals.

In market gardening activities, controlling the temperature, climatic conditions, soil quality and even water potential are important for those in the field. Understanding climatic phenomena and soil quality allows you to anticipate decisions and have insight into how to carry out this work. Artificial intelligence in market gardening culture helps for irrigation, weeding, spraying with the help of sensors and other means embedded in robots and drones. These technologies save the excessive use of water, pesticides, and herbicides, and maintain the fertility of the soil [3] .

By collecting data over a large period of time, AI can generate seasonal forecasting systems. Market gardening professionals can take into account the probability of certain recurring events in their forecast. It is evident that AI technology has a key role to play in the future market gardening culture sector [4] .

Drones which are controlled by artificial intelligence make it possible to monitor operations. These drones collect data which can then be analyzed and used. This data can then identify the various problems on the site and proposed solutions can be identified.

A monitoring system can be deployed to regularly check the status of the crops such that in case of pest attacks or when the crop health looks degraded it can be efficiently noted and necessary steps can be taken [5] .

We ask the question to 97 market gardening professionals about their use of AI. The result is as follows.

Table 1 shows us the frequency, the percent, the valid percent, and the cumulative percent of the use of AI. We clearly see a low use of AI in the field despite these benefits. Indeed, There has been a significant development in digital agriculture management applications, which has impacted information and communication technology (ICT) to deliver benefits for both farmers and consumers [6] .

Figure 1 shows a low rate of use of AI in the field of market gardening and cultivation.

We analyze the performance over a period of one year of production of a market gardening professional not using AI.

Table 2 shows the rate of yield in kilograms over a period of one year which shows that production is not continuous. We see an increase and a reduction on both sides of production due to uncontrolled factors that could have been resolved with the use of AI.

Figure 2 shows us a variance in quarterly production, which of course is not constant and does not respond to any logic.

For another producer who has decided to launch into AI, for which we have deployed the infrastructure and collected the data, we show the results in terms of yield.

Table 3 clearly shows us a consistency and an increase in production due to the use of drones and seasonal forecasting techniques. Factors that would have led to a reduction in production were ruled out thanks to AI techniques used to eliminate risk factors. AI-based technologies increasingly substitute and complement humans in managerial tasks such as decision making [7] .

Figure 3 shows us exactly how AI is beneficial through rational and constant production, allowing professionals in the field to be more confident and reassured about the consistency of their production.

Drone is used bi-weekly for disease detection and targeted pesticide spraying, reducing chemical use.

Forecasting Systems: Used daily to optimize irrigation schedules, minimizing water wastage and preventing drought stress.

Production Increase: The combination of drones and forecasting systems resulted of increase due to better crop management and reduced losses.

We analyze by comparing the 2 previous situations and we note the effectiveness of the contribution of AI in market gardening. It is important to clarify that they produce the same crops under similar conditions. Galmi onion is the crops which scientific name is Allium Cepa. Their farmer size is the same.

Table 4 compares productions and shows us the yield gaps that may exist between the use of AI and the lack of use of AI.

Figure 4 shows us a rising curve on the one for which we used AI, indicating a clear and precise confirmation of the added value of AI on market gardening.