The combined effect of these technological advancements couldn\u2019t be more clear: AI in agriculture is projected to grow from an estimated $1 billion in 2020 to as much as $4 billion by 2026, at a CAGR of 25.5% between 2020 and 2026, according to a marketing report<\/a> by MarketsandMarkets.<\/p>\n Source: MarketsandMarkets <\/a><\/em><\/p>\n Agriculture Software Development Services<\/p>\n Is it important to understand that AI and ML are not magic spells or silver bullets capable of doing away with any problem. However, they are extremely powerful technologies that help turn vast arrays of data collected from video and photo surveillance, groups of sensors, and other sources into meaningful and actionable insights.<\/p>\n Overall, the use of artificial intelligence in agriculture<\/a> may lead to a number of tangible benefits:<\/p>\n Cost savings<\/b>. The cost of every harvest is a combination of multiple factors (energy, water, gas, human labor, fertilizers, herbicides, seeds, etc.), so being able to tell whether these resources are used effectively is extremely important. Machine learning in agriculture is perfectly positioned to plow through numbers, compare them, detect unwanted and wasteful practices, suggest process optimizations, and streamline day-to-day operations without calling for major investments of capital.<\/p>\n Yield boost<\/b>. AI\/ML algorithms can be applied to dig through vast arrays of both historical and new data to suggest effective ways of increasing crop yields from particular areas through regular crop rotation, targeted fertilization, timely pest control, and other important activities. Drone image processing coupled with IoT data collection<\/a> are also getting increasingly more commonplace and are helping agricultural companies identify land productivity issues faster and with increased accuracy.<\/p>\n Better compliance with sustainable farming best practices<\/b>. Using the land and natural resources to the fullest without causing soil exhaustion or overconsumption of water from the nearest water reservoirs helps form a body of constant, recurring practices that aim to preserve the land\u2019s capacity to remain productive for generations to come. To that end, AI and ML technologies can help farmers develop sustainable resource consumption scenarios based on historical data.<\/p>\n Let\u2019s now take a look at some specific areas where companies can leverage the potential of AI and ML in agriculture for increased productivity, higher yields, and slower soil degradation.<\/p>\n Vertical farms<\/a> are steadily gaining popularity in areas where traditional farming techniques are either ineffective or simply impossible \u2014 for example, in arid regions or in space-limited agricultural facilities located within city limits.<\/p>\n Due to the sheer complexity and intensity of the plant-growing process, these facilities require a great deal of control, automation, round-the-clock monitoring and robotic assistance. A number of companies are already offering end-to-end robotic solutions bundled with powerful, unified farm management solutions<\/a> that tie all the processes together and enable such next-gen indoor farms to operate very effectively within confined spaces and in a near-autonomous mode.<\/p>\n Drones are an invaluable tool in the technology arsenal of any modern agricultural company. Relatively affordable, fast to deploy and extremely maneuverable, they can be programmed to automatically follow the same predefined routes day after day, taking photos and recording videos along the way or at particular waypoints.<\/p>\n Source: CropLife<\/a><\/em><\/p>\n With the right drone management and data analysis software<\/a> in place, modern farmers can quickly obtain a much better understanding of the dynamics of plant growth, spreading of crop diseases, pest infestation, watering needs, and many other aspects of land and crop management.<\/p>\n What\u2019s most important is that agricultural drones can be fitted with high-precision, multispectral sensing systems capable of singling out fields or parts of fields that need to be treated chemically or mechanically, thus lowering land maintenance costs and making for a much faster reaction time to adverse effects of any type.<\/p>\n In addition to observations with the help of state-of-the-art remote sensing technologies, drones can be used for delivering and sowing seeds or spraying crops with chemicals \u2014 also with great precision and at a fraction of the cost of conventional land treatment operations.<\/p>\n Learn how to use drones to improve visibility across your vast farmland and set it all up the right way. Intellias is instrumental with drones and knows how to interpret agricultural drone data for crop management decisions<\/p>\n Machine learning in agriculture has become an increasingly popular tool used for the development of complex algorithmic models capable of predicting crop yields based on a variety of parameters: from real-time data delivered via weather stations and soil analysis sensors to drone imagery, digital maps, and computer vision analysis of the soil.<\/p>\n Today, fairly accurate yield forecasts can be made even before the seeds fall into the soil \u2014\u202fgiven, of course, that they are fueled by comprehensive big data coming from trustworthy sources<\/a>.<\/p>\n A productive and healthy field is one that has not been overexploited for years, has received enough water and fertilizers, and has been mechanically prepared for planting. All of these parameters can be effectively tracked remotely using IoT sensors and drone images.<\/p>\n This complex task can be achieved with the help of custom solutions tailored to the individual characteristics of an agricultural business (region, landscape, harvesting periodicity, dominant crop types, and more) or commercial off-the-shelf solutions like eAgronom<\/a>, to take one example. Such specialized software can result in considerable cost savings and bump up the \u0441ombined yield by a fair margin.<\/p>\n Soil analysis and management solutions go hand in hand with advanced field mapping services and tools like OneSoil<\/a> that combine satellite imagery with custom GIS data to create a bird-eye view of vast farm territories.<\/p>\n These and similar solutions for agricultural precision mapping<\/a> are capable of constantly monitoring nutrient levels in the soil and comparing them with those registered in the years that demonstrated the highest yields. To lessen the chemical impact on the soil, AI algorithms can provide recommendations on the minimally sufficient dosage of fertilizers and pesticides that should address any detected issues without inflicting excessive damage.<\/p>\n Effective farming is not just about plowing, sowing, and harvesting. It\u2019s also about being able to quickly supply lots and lots of materials and chemical compounds to the right fields, in just the right amount, and without having to overpay or stockpile excess materials for months.<\/p>\n On the commercial side, it\u2019s also about the ability to effectively store and quickly sell ripe produce, which is especially important for crops with a short life span, such as cucumbers or tomatoes.<\/p>\n Both goals can be achieved with complex, deeply integrated precision farming systems<\/a>, either fully custom or customized to meet specific requirements. Machine Learning in Supply Chain Management<\/p>\n Cattle farmers can also take full advantage of cutting-edge machine learning in agriculture. AI-driven computer vision systems aided by IoT technology can help count animals within corrals of any size, locate runaway cows or sheep, identify the right moment for relocating livestock from depleted pastures, and detect anomalies in animal behavior that may be a sign of diseases.<\/p>\n![\"Using](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2022\/06\/ml-in-agro-1.jpg\")
<\/p>\nBenefits of AI and machine learning in agriculture<\/h2>\n
Smart greenhouses and deep farm automation<\/h2>\n
![\"Using](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2022\/06\/ml-in-agro-2.jpg\")
<\/p>\nReal-time monitoring of crop fields<\/h2>\n
![\"Using](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2022\/06\/ml-in-agro-3.png\")
<\/p>\nAccurate crop yield forecasting models<\/h2>\n
Field mapping and soil preparation<\/h2>\n
![\"Using](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2022\/06\/ml-in-agro-4.png\")
<\/p>\n![\"Using](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2022\/06\/ml-in-agro-5.jpg\")
<\/p>\n![\"emissions](\"https:\/\/intellias.com\/wp-content\/uploads\/2022\/09\/emissions-free-whitepaper-cover.png\")
<\/div>\n\t\t\tIntelligent supply chain management<\/h2>\n
\nLivestock monitoring<\/h2>\n
![\"Using](\"https:\/\/d17ocfn2f5o4rl.cloudfront.net\/wp-content\/uploads\/2022\/06\/ml-in-agro-6.png\")
<\/p>\n