To begin with, agriculture and artificial intelligence are ironically opposing topics in tradition-based agricultural countries like India. At the same time, most of the recent start-ups are based on precision agriculture, fully automated vertical aero/hydroponic farms and AI-based forecasting models. As humans are evolving with the advancement of technologies, the human population also grows rapidly hand in hand which simultaneously leads to adverse climate change and devastation. The world population is expected to be 8.6 billion, 9.8 billion in 2050 and the substantial demand for food is predicted to grow by 70%. To put this into an understandable perspective, we will have to produce more food in 35 to 40 years than in the last 10,000 years.
According to ‘The State of the World’s Land and Water Resources for Food and Agriculture‘ report by FAO, in developing countries, even if agricultural production doubles by 2050, one person in twenty will still be under-nourished, leaving 370 million hungry people at the highest risk of becoming underfed, most of whom will be in Asia and Africa. Above all, the agriculture and farming industry is involved in generating $330 billion annually to boost the economy as per the Environmental Protection Agency (EPA) report. Governments, international organizations are tirelessly engaged in the call agreed upon by many countries to promote prosperity while protecting the planet known as the Sustainable Development Goals, which aim to focus on achieving zero hunger by 2030. Here the question arises on land and resource availability. Keeping all this in mind, we are urged to produce significantly more food with significantly less land and resources through sustainable climate smart agriculture with the help of artificially intelligent technologies.
Predictive Modeling
Pest and disease predictive models typically contain one or more mathematical relationships that describe the progression of one or more aspects of pest or pathogen life cycles in terms of one or more environmental parameters. Some are relatively simple models, based on where a threshold is crossed or on accumulated temperature sums such as degrees per day. However, many models are more complex, are the synthesis of years of research, rely heavily on computing power to input data, perform calculations, and provide a tailor-made output. Similarly, other parameters such as soil oxygen levels, NPK, and micronutrients can be assessed and necessary measures taken accordingly.
Mostly all crops are susceptible to infestation or infection by various pests and pathogens. Pathogens may be fungi, bacteria, or viruses, while most pests consist of invertebrates such as insects and mites. The process of infection or colonization is strongly influenced by environmental conditions, together with other characteristics of the crop and cropping system, and the subsequent development of a pathogen or pest outbreak is also dependent on environmental conditions. Except for mammal and bird pests, all organisms that infect or colonize crops are ectotherms, meaning that their life cycles are highly dependent on ambient temperature. A wide range of pests and especially pathogens are also affected by humidity. This paves the way for attempting to predict the incidence and severity of pest and disease outbreaks using environmental data such as records of temperature, rainfall, and humidity through machine learning technology (MLT) and regression analysis.
Indoor vertical farms
Stuart Oda is an investment banker turned passionate urban farmer who co-founded Aleska Life, an agricultural technology company that develops solutions to improve food security, food safety, supply chain transparency, and farming efficiency. The company has transformed discarded shipping containers into indoor vertical farms in industrial parks across North America, urban cities across Asia and even arid regions of the Middle East. He refers to this method as controlled environmental agriculture or weather/climate-proof farming. These farms replace traditional elements with artificial ones. Sunlight is replaced with artificial full spectrum LEDs and soil is replaced with inorganic materials such as polyurethane sponges, biodegradable peat moss and perlite and clay pellets. The precise nutrient formula is circulated and recycled throughout the facility, pumped directly into the root zone of individual crops.
The entire structure is monitored through sophisticated monitoring and automation systems. Manual labourers are greatly reduced and robotic harvesting is used. Overall, year round production with predictable output is ensured. Resource use efficiency is high as it uses 90-99% less water, fertilizer and zero chemical use. These 3D vertical farms can yield up to 350 times more food than traditional farms. The major drawback here is the production process is energy-intensive. Next, research is being done to develop lasers optimized for plant growth by using fiber optic cables to channel sunlight directly into the farm during the day to reduce the need for artificial lighting. These futuristic farms can be set up in low, unused spaces in urban areas and are capable of providing quality vegetables to even the most underprivileged communities.
Harvest Crow Robotics was found to tackle the labor shortage in crop farming, especially in strawberries, with their exceptional set of technologies. They provide a harvesting service that automates crop management, harvesting, and packing of specialty crops. T heir harvester reduces CO2 emissions by up to 96% compared to traditional manual harvesting according to Destination Better environmental and sustainability consultants. In their AI based machine learning vision system, each berry on a plant is scanned to determine if it is ripe, healthy, and ready to be picked. With 6 claws per robot, each harvester has 96 claws that can pick fruits without damaging them. These technologies play a vital role in making strawberries affordable for everyone and entering the list of luxurious commodities to be enjoyed in the US.
Energid Citrus Picking System
Energid is funded by the US Department of Agriculture. Their system will combine the intelligence of robotics with the efficiency of bulk fruit removal to reduce the citrus grower’s harvesting costs. It uses multiple low-cost picking mechanisms organized in a grid. They are simple in detail with the picking mechanism, with no actuators and no sensors, easy to build and to replace.
Ecobotics AG
This particular company specializes in precision technology for weeding. One of their innovations, the Evo- a fully autonomous robot is used for 100% ecological weeding. The other one is the Ara- mounted sprayer. It has top notch spraying accuracy and the speed allows a huge reduction of chemicals products used.
Self-Driven Tractors
Taranis is one of the companies that have released autonomous tractors. These GPS-enabled tractors can plant, spray, and harvest with a professional in the driver’s seat. Another amazing creation is Auto-TRAC which is the heart of self-driving software. It uses a combination of GPS, laser range finders, thermal infrared sensors, color cameras, and inertial navigation to safely steer machinery. To enhance the accuracy of GPS, John Deere developed its RTK radio towers and connected its Starfire receivers to NASA ground stations to achieve an accuracy of +/- 1 inch. The self-driving technology can be used to reduce fatigue, maximize field utilization, and integrate automated delivery of fertilizers, seeds, water, and pesticides. Other similar technologies include the Agro-Bot E-Series, Blue River Lettuce Bot 2, Agribotix, and Robo Plant. Apart from these technologies, there are planes, drones, and satellites used for aerial imaging which in turn helps analyze the farm with the help of image annotations as well as computer vision algorithms that help farmers detect problems and find solutions.
Facing problems with your land? A solution in your hand
Piet is a Berlin AI-based start-up that has developed Plantix, a mobile advisory application for farmers. This crop doctor claims to diagnose infected crops and provide remedies for any pest, disease or nutrient deficiency problems with just a photo. The company claims that its results are 95% accurate in detecting unique imaging patterns. It covers about 30 major crops, detects 400 plant damages and is available in 18 languages. The direct competition for this application is Crop Diagnosis. It is a mobile application that aims to improve pest management decisions by making crop diagnosis accurate, selection of chemicals and application aided by personalized instructions. It does this by evaluating crop details (type, location, soil and history) and threat characteristics (type, presence and progress) entered by the user through a smart questionnaire.
Challenges on the way
Farmers in most developing countries are not aware of complex technologies, and it is difficult for them to adapt within a short period of time. To overcome these situations, governments and companies should put their hands together and introduce the technologies by explaining them step by step with multiple demonstrations. Most of the technologies even though reduce the time, manage labour shortage and are sustainable, are not affordable by small and marginal farmers. Collective farmers and large farmers can be benefited from this.
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