Artificial Intelligence in Agriculture

Introduction

• The human population has been increasing leaps and bounds since the 20th century. It is expected that the 9 billion marks would be reached by 2045. Food and nutrition demand would increase by 70% from its current levels.
• With such a large number of empty stomachs to feed and meet the nutritional demand, pressure on the natural resources like soil and water would enhance exceedingly, to grow more and more crops.
• In such a scenario, growing crops by the utilization of minimal resources becomes very critical.
• Agriculture has been the most crucial sector asIt produces crops to feed human hunger, apart from providing raw materials to various industries. However, the prevalence of subsistence farming, high poverty levels, illiteracy, and negligence towards environmental degradation have worsened the problem.
• Reckless groundwater exploitation and agrochemical application have eventually led to a rise in abandoned arable lands.
• Correspondingly, health of animals and human beings is put to risk and biodiversity loss could be a major resulting outcome.
• Moreover, Indian agriculture faces multiple challenges like high dependence on monsoon, resource intensiveness – heavy use of resources (water, inorganic fertilisers and pesticides), degradation of land and loss of soil fertility, and low per hectare yield, among others.
• With existential threats of changing climate, food security, depleting non-renewable energy resources and urban sprawl, sustainable agriculture is meant to be our sole saviour. The incorporation of sustainable agriculture would mean the use of less agrochemical and less land to produce more crops and increase productivity and yield.

In this context, Artificial Intelligence (AI) could play a catalytic role in improving crop yield from various factors like climate changes, population growth, employment issues and food security problems.

Understanding Artificial Intelligence (AI)

• It refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.
• The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.
• The ideal characteristic of Artificial Intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal. The goals of artificial Intelligence include learning, reasoning, and perception.
• Artificial Intelligence is being used across different industries including agriculture, finance and healthcare.
  
• A subset of Artificial Intelligence is machine learning, which refers to the concept that computer programs can automatically learn from and adapt to new data without being assisted by humans.
• Deep learning techniques enable this automatic learning through the absorption of huge amounts of unstructured data such as text, images, or video.

Lifecycle of Agriculture

• Preparation of soil: It is the initial stage of farming where farmers prepare the soil for sowing seeds. This process involves breaking large soil clumps and removal of debris, such as sticks, rocks, and roots. Also, fertilizers and organic matter are added depending on the type of crop to create an ideal situation for crops.
• Sowing of seeds: This stage requires taking care of the distance between two seeds and the depth for the planting of seeds. At this stage, climatic conditions such as temperature, humidity, and rainfall play an important role.
• Adding Fertilizers: Maintaining soil fertility is an important factor so that farmers could continue to grow nutritious and healthy crops. Farmers turn to fertilizers because they contain nutrients, such as nitrogen, phosphorus, and potassium, and supplement the required elements absent in the soil. This stage determines the quality of the crop grown.
  
• Irrigation: This stage helps to maintain humidity and keep the soil moist. Underwatering or overwatering could hamper the growth of crops and if not done properly, it could damage crops.
• Weed protection: Weeds are unwanted plants that grow near crops or at the boundary of farms. Weed protection is an important factor affecting crop yield. Weed growth decreases yield, increases production cost, interferes with the harvest, and lowers crop quality.
• Harvesting: It is the process of gathering ripe crops from the fields. It is a labour-intensive activity. This stage also includes post-harvest handling such as cleaning, sorting, packing, and cooling of the farm produce.
• Storage: It is that phase of the post-harvest system during which the products are stored in a safe and accessible way to guarantee food security during periods of low crop production. It also includes the packing and transportation of crops.

Challenges faced by farmers by using traditional methods of farming

• Pollution and deforestation: In farming climatic factors such as rainfall, temperature and humidity play an important role in the agriculture lifecycle. Increasing pollution and deforestation have resulted in climatic changes, which have made it difficult for farmers to take decisions during soil preparation, seed sowing, and crop harvest.
• Deficiency of nutrients: Every crop requires specific nutrition in the soil. There are 3 main nutrients required in fertile soil - nitrogen(N), phosphorous(P) and potassium(K). The deficiency of nutrients could lead to poor quality of crops.
• Weed growth: Weed protection plays an important role during the agricultural lifecycle. If weed growth is not controlled, it could lead to an increase in production cost. Further, it absorbs nutrients from the soil which could cause nutrition deficiency in the soil.

Scope of Artificial Intelligence in Agriculture

• Augmenting Entire Supply Chain: Worldwide, agriculture is a $5 trillion industry, and Artificial Intelligence technologies can help to yield healthier crops, control pests, monitor soil, and growing conditions, organize data for farmers, help with the workload, and improve a wide range of agriculture-related tasks in the entire food supply chain.
• Opportunity for High Growth: Globally, Artificial Intelligence applications in agriculture reached a valuation of nearly $1 billion in 2019, and this valuation is estimated to grow to almost $8 billion by 2030. However, the Indian agri-tech market, presently valued at $204 million, has reached just 1% of its estimated potential of $24 billion.
• Huge Agricultural Data Resource: Due to the diversity of its soil types, climate, and topography, India provides a great opportunity for data scientists and Artificial Intelligence experts to develop state-of-the-art Artificial Intelligence tools and solutions for agriculture. Indian farms and farmers provide vast and rich data to help create Artificial Intelligence solutions not just for the country but the world at large. This is one of the factors facilitating the opportunity to Artificial Intelligence in Indian agriculture unparalleled.

Use of Artificial Intelligence in Agriculture

• Use of weather forecasting: With the changes in climatic conditions and rise in pollution, it is difficult for farmers to determine the right time for the sowing of seeds.
• With the help of Artificial Intelligence, farmers could analyze weather conditions by using weather forecasting which would help them plan the type of crop to be grown and the suitable time for sowing of seeds.
• aWhere, a Colorado-based company uses machine learning algorithms in connection with satellites to predict the weather, analyze crop sustainability and evaluate farms for the presence of diseases and pests.
• Soil and crop health monitoring system: The type of soil and nutrition status plays an important role in the type of crop grown and the crop quality.
• Due to the increasing deforestation, soil quality has been degrading and made it hard to determine the quality of the soil.
• A German-based tech start-up PEAT has developed an AI-based application called Plantix that uses image recognition-based technology and could identify the nutrient deficiencies and prevalence of pests and diseases in the soil. It could help farmers in getting an idea about which fertilizer to use to improve harvest quality.
• Analysing crop health by drones: SkySqurrel Technologies has introduced drone-based Ariel imaging solutions for monitoring crop health.
• In this technique, the drone captures data from fields and then data is transferred via a USB drive from the drone to a computer and analysed by experts.
• FarmShots is another start-up focused on analysing agricultural data derived from images captured by satellites and drones. Specifically, the company aims to “detect diseases, pests, and poor plant nutrition on farms.”
• Artificial Intelligence helps analyze farm data: Farms produce thousands of data points on the ground daily.
• With the help of Artificial Intelligence, farmers can now analyze a variety of things in real-time such as weather conditions, temperature, water usage, or soil conditions collected from their farms for better decision making.
• Farmers are also using Artificial Intelligence to create seasonal forecasting models to improve agricultural accuracy and increase productivity.
• Automated quality analysis of images of food products is an accurate and reliable method for grading fresh products (fruits, grains, vegetables, cotton etc.) characterized by colour, size and shape.
• Precision Agriculture: Precision agriculture uses Artificial Intelligence technology to aid in detecting diseases in plants, pests, and poor plant nutrition on farms.
• Artificial Intelligencesensors can detect and target weeds and then decide which herbicides to apply within the right buffer zone.
• This helps to prevent the over-application of herbicides and excessive toxins that find their way into our food.
• It would increase productivity by introducing precision agriculture.
• Tackling the Labour Challenge: With fewer people entering the farming profession, most farms are facing the challenge of a workforce shortage.
• One solution to help with this shortage of workers is Artificial Intelligence agriculture bots. These bots augment the human labour workforce and could be used in various forms.
• These bots could harvest crops at a higher volume and faster pace than human laborers, more accurately identify and eliminate weeds, and reduce costs for farms by having the clock labor force.
• Additionally, farmers are beginning to turn to chatbots for assistance. Chatbots help answer a variety of questions and provide advice and recommendations on specific farm problems.

• Driverless tractors: The unavailability of farm labour has led to multiple companies across the world, introducing self-driving tractors.
  • Even Mahindra and Mahindra Ltd, India’s largest manufacturer of tractors, showcased its first driverless tractor in Sept 2019.
  • Mahindra’s tractor can steer automatically using GPS-based technology, lift tools from the ground, recognise the boundaries of a farm, and can be operated remotely using a tablet.
• Irrigation: Artificial Intelligence helps in irrigating agricultural fields. It can monitor and analyse the soil moisture and other conditions, such as aridity or humidity in the surrounding atmosphere, and then open water valves to provide water to the fields automatically, without any human involvement.
  • Artificial Intelligence can alert personnel in times of drought and can also help mitigate wastage of water by releasing it only when it is required. Moreover, the illegal consumption of water can also be prevented.
  • Thus, as capital costs of technology decrease and the software capabilities of AI in agriculture increase, we will see a jump in efficiency and sustainability, which will eventually meet the world’s food needs.
• Warehousing: Even after a good harvest, if the crops are not stored and taken care of, they may rot. Thus, good warehousing facilities are a must.
  • AI can help in deploying correct grain storage techniques by maintaining the appropriate temperature, pressure, and humidity conditions, by adjusting its analysis according to different types of crops.
  • It can also give real-time information to the warehouse manager if there is any change in set parameters in the grain storage. Moreover, it constantly updates itself with the number of grains remaining inside the warehouse so that they can be replenished.

Facts related to Artificial Intelligence in India

• Microsoft India in collaboration with ICRISAT (International Crops Research Institute for the Semi-Arid Tropics), has developed an AI Sowing App.
  • The app sends sowing advisories to participating farmers on the optimal date to sow crops.
  • The farmers don’t even need to install any sensors in their fields or incur any capital expenditure. All they need is a cell phone capable of receiving text messages.
• To calculate the crop-sowing period, historic climate data (spanning over 30 years from 1986 to 2015) for the specific area in Andhra Pradesh was analysed using Artificial Intelligence.
  • To determine the optimal sowing period, the Moisture Adequacy Index (MAI) was calculated.
  • MAI is the standardized measure used for assessing the degree of adequacy of rainfall and soil moisture to meet the potential water requirement of crops.
• Microsoft has also partnered with United Phosphorous Limited, India’s largest producer of agrochemicals, to create the Pest Risk Prediction App.
  • Today, these farmers across the Indian states of Andhra Pradesh and Karnataka wait to get a text message before sowing the seeds.
  • In a few dozen villages in Telangana, Maharashtra, and Madhya Pradesh, farmers receive automated voice calls alerting them whether their crops are at risk of a pest attack based on weather conditions and stage of the crop.

Challenges to Artificial IntelligenceAdoption in Agriculture

• Artificial Intelligence engineers know little about agriculture, the problems and the opportunities in this field. Correspondingly, farmers want to adopt Artificial Intelligence but they aren’t going to waste their time if it’s not usable in the field.
• Another major challenge is the lack of simple solutions that seamlessly incorporate and embed Artificial Intelligence in agriculture. The majority of farmers don’t have the time or digital skills experience to explore the Artificial Intelligence solutions space on their own.
• Artificial Intelligence and machine learning are still far from being able to predict critical outcomes in agriculture purely through the cognitive ability of machines.
• There have been technical challenges – such as the lack of implementation of a rural broadband structure – that remain to be resolved, but more importantly, as in other industries, Artificial Intelligence has promised more than it has delivered.

Conclusion and the Way Forward

• Artificial Intelligence-driven technologies are emerging to help improve efficiency and address challenges facing the agriculture sector including, crop yield, soil health and herbicide-resistance. Crop and soil monitoring technologies will be important applications going forward.
• Agricultural robots are poised to become a highly valued application of Artificial Intelligence in this sector. Evidence of wide adoption is apparent in dairy farming where thousands of milking robots are already operating.
• It will be important that farmers are equipped with training that is up-to-date to ensure the technologies are used and continue to improve. Additionally, extensive testing and validation of emerging AI applications in this sector will be critical as agriculture is impacted by environmental factors that cannot be controlled, unlike other industries where risk is easier to model and predict.
• With therecent reforms in the Agriculture sector, there is a likelihood of increased investments in contract farming and infusion of technology for better yields and productivity. This will further push the adoption of Artificial Intelligence in agriculture. Further, to help these Artificial Intelligence solutions scale increased investments needed, both from the public and private sector.
• In this context, the recently concluded Responsible Artificial Intelligence for Social Empowerment Summit (RAISE–2020) Summit has helped provide a platform for global stakeholders to come together to finalize the roadmap for using Artificial Intelligence for the public good.