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Pesticide optimisation

Introduction

Agriculture has been an important part of the society for thousands of years and is still one of the main industries in the world. Due to the population increase all over the world the agriculture industry also faces the pressure of producing more and more food to fulfill the needs of the increasing population. Of which, pesticide utilization is one of the most important aspects that need to be well addressed in order to meet this objective. Pesticides are materials applied in the management of pests which affect crops and if used properly they enhance crop production. However, if used carelessly or in large quantities, pesticides cause contamination of the environment, affect the health of people and even make pests become resistant to them. Hence, the proper use of pesticides is a major concern in the agriculture sector. With the help of latest techniques and developments such as machine learning and artificial intelligence it has become possible to find new ways of solving this problem. This paper seeks to establish that ML and AI have the capability of processing large data sets, making recommendations, and providing valuable information that can be useful in the management of pesticide use.

Challenges

There are several barriers that the agriculture industry encounters in efforts to enhance the management of pesticides. The problem is how to determine the right quantity of pesticide to be used. This can lead to the wastage of the product and adverse effects on the environment in case of overuse while on the other hand, underuse can lead to infestation by pests. Another challenge is that of establishing the right time for applying pesticides. This is because if the pesticides are applied at any time other than the right time they are less effective. Also, pests are known to become resistant to pesticides, thus requiring that there is regular assessment of the effectiveness of the pesticides and changes made where necessary. However, other factors like weather conditions, type of soil and kind of crops also play their roles, which makes it even more challenging. And last but not least, farmers do not have an easy access to the right information at the right time, which makes farmers to make decisions blindly when it comes to pesticide usage.

AI Solutions

These challenges can be addressed by the state-of-the-art AI and ML technologies. They can take in vast amounts of information from different aspects including weather predictions, soil probes, and aerial photographs to offer the right time, place and quantity of pesticide application. Predictive models for example can assist in identifying the onset of pest infestation thus allowing for prevention. There are certain AI algorithms which can help in analyzing the trends of pest incidence and their resistance to specific pesticides and thus can be useful in the creation of better pesticides and their usage. Such companies as IBM and Microsoft are already working on it. For instance, IBM’s Watson Decision Platform for Agriculture integrates AI to generate insights on weather, soil, and crops condition to assist farmers in their decision making process. Also, there is Microsoft’s FarmBeats project which applies AI and IoT to gather and process information about crops’ condition, including pests.

Benefits

There are several advantages of using AI in pesticide optimization. First, it can improve on crop yields by helping to ensure that the pests are well managed. Second, it can save on costs by reducing the amount of pesticides that are used and wasted. Third, it can lower the impacts on the environment and health by decreasing the amount of pesticides that are applied. Fourth, it can assist in managing resistance by enabling the adjustment of pesticide strategies at the right time. Fifth, it can strengthen the farmers by giving them the right information at the right time to help them in their decisions. Last, through enhancing the effectiveness and efficiency of farming, it can support the UN’s Food and Agriculture Organization’s goal of producing enough food for the future population while adapting to climate change.

Return on Investment

It is possible to outline the opportunities for ROI when implementing AI in pesticide optimization. In this way, AI has the potential of increasing crop yields at the same time reducing costs and minimizing risks to enhance the profitability of farming businesses. For example, a research done by McKinsey Global Institute showed that the implementation of AI in agriculture could improve crop production by up to 60% and the cost of production could be cut down by 20%. Also, through the principle of sustainability, AI can also play a vital role in ensuring the future viability of farming. But to achieve these outcomes, there is a need to make some investments on AI technologies, data collection and management systems, and training of the farmers. Despite the initial expenses, the expected gains provide a strong argument for the integration of AI in pesticide optimization.

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