The agricultural sector is among the critical and crucial industries around the globe, especially in India. For 58% of the Indians, agriculture is the primary source of income. The gross value added (GVA) of agriculture, forestry, and fisheries was estimated at Rs. 19.48 lakh crores ($276.37 billion) in FY20. According to the first flash estimates of FY22 national income, the percentage of agriculture and related sectors’ GVA (at current prices) is 18.8% of the total GVA. Consumer spending in India is set to grow again in 2021, rising by up to 6.6% post-pandemic. Agriculture and related activities recorded a growth rate of 3.6% at constant prices in FY21.
India’s food industry is facing tremendous growth and increasing its contribution to the global food trade every year due to its immense value creation potential, especially in the food processing industry. The Indian food and grocery market is the sixth-largest in the world, with retail accounting for 70% of sales. India’s food processing industry accounts for 32% of the country’s total food market. Total exports of agricultural and related products in FY21 were US$41.25 billion.
As digital technologies revolutionize all industries, agriculture is no exception. Like any other sector, the agricultural sector faces various challenges including climate change, labor shortages, and the disruptions caused by the pandemic. Digital technologies such as computer vision can help the agricultural sector meet these challenges and achieve efficiency, resilience, and sustainability.
Computer vision can be called an extension of AI which enables machines with the efficiency and capability of organized thinking and interpreting the data as human counterparts. Computer vision technology requires analysis of a plethora of visuals from footage to images and it has the ability to identify, track, precisely predict and assess specific objects within the stored visuals.
Smart farming is about harnessing the power of technologies like artificial intelligence, computer vision, and many more to automate and improve the quantity and quality of crops. Even with fluctuations in commodity prices, a ~5% improvement in crop yields can ensure farming remains profitable.
From using computer vision technology for crop and soil monitoring to disease detection and predictive analytics, agribusiness is entering a whole new phase of evolution thanks to AI.
It’s not just future potential, but growing interest and investment in the agricultural sector:
Agricultural management is a complex subject due to its vast breadth and the sheer complexity that is bound with it. Manual inspections are therefore very time-consuming and expensive.
Plants, that are grown in greenhouses often use humidity, temperature, and light sensors to ensure optimal growing conditions for plants. However, many sites will continue to rely on manual logging and monitoring of environmental conditions, allowing for a quick response to changes or technical issues, such as the failure of a heating unit is prevented.
In agriculture, climatic factors such as precipitation, temperature, and humidity play an important role in the life cycle of agriculture. Increasing deforestation and pollution are causing climate change and making it harder for farmers to make decisions about soil preparation, seed planting and harvesting. Each culture requires specific nutrition in the soil. The soil needs 3 main nutrients: nitrogen (N), phosphorus (P), and potassium (K). Nutrient deficiencies can lead to poor crop quality.
If not properly controlled, it can lead to an increase in production costs and also absorb nutrients from the soil, which can lead to nutrient deficiency in the soil. The Main Problems to Concern About
In order to successfully produce crops, suitable irrigation functionality is required. The ML algorithm can improve irrigation leading to the following :
Here are some irrigation systems in the machine learning realm:
Making the right choice or decision is a key factor in farming. The correct decision leads to better income outcomes. Predictive analytics is a great machine learning tool that plays an important role in making the right decisions.
Farming is all about calculated risks, but what if risks can be calculated and cured in advance? The anomaly analysis can help you identify the strengths and weaknesses of the soil, which generates more revenue and saves a lot of time.
Agriculture faces a serious problem today. There is a huge scope for improvement as present conditions require specific solutions. Simply put, harvests are not properly monitored. Classification analysis is the key to efficient damage control and higher ROI than before.
The climate has now predominantly become a data issue. Previously, inaccurate weather forecasts have yielded a devastating loss of many crops, along with wastage of invested time, effort, and money. The good news is, that technology has significantly improved and one of the key solutions for this can be Regression analysis which can help with precise and accurate forecasting.
In developing countries, around 40-50% of the total crops are lost because of post-harvest issues, crop diseases, and pests. Even in developed countries such as the US, around 20-30% of crop yield is lost to similar culprits.
However, you can use image analysis to detect the present object in the field/farm. By classifying the objects, one can easily spot weeds on the farm, which can be dealt with, ensuring better crop growth.
We know that acquiring a new customer is harder and more expensive than retaining a paying customer. Analysis of the recommender system will assist you in identifying the customers who’re most likely to buy your product/service and the probability of your existing customers. In addition, this leads to customer loyalty and broadens the overall range of innovations in service.
The correct minerals are the most important requirement for plant growth. Spotting anomalies through unattended analysis helps you choose the required volume of minerals, leading to faster-growing plants and helping you produce more plants in comparison to competitors.
AI models, especially the ones based on computer vision have made numerous contributions in different areas of the agricultural domain such as plant health monitoring and detection, weeding, weather condition analysis, harvesting, planting, etc. A few of such contributions are:
Because of their autonomous flying abilities, drones have become significantly popular in the market. Also, they’ve become an indispensable factor in farming and agriculture. Because of their ability to cover large distances via air, drones are capable of capturing tons of data using a camera(pre-installed.) The camera, if enabled with computer vision, can be taught to detect crop health info, and unfavorable conditions, get a hawk’s eyes view of the farmland even identify soil conditions using geo sensing abilities. For identifying and detecting a specific object, techniques like image annotations and semantic segmentation are used.
The combination of deep-learning techniques for the construction of status calculation models is used to analyze plant performance. Automating all the time-taking and mundane tasks such as segregating good crops from bad and finding the best one for shipping has been a blessing in farming. Identification of crop longevity and early detection of crop damage are the two most vital areas of yield analysis. Fruit and vegetables are graded as Quality to determine which batches of produce should be shipped first and which can last longer and be shipped to distant destinations.
In agriculture, pesticides are commonly sprayed in order to protect farm produce. Also, drones that use computer-vision technology are increasingly becoming popular in the agricultural market. These computer vision drones can easily monitor & detect crops that are infected and spray the required amount of pesticides to deal with the same. Contributions like the above have helped the workforce stay healthy by preventing them from getting exposed to harmful pesticides.
Today, the use of phenotyping to identify plant traits for precision farming is widespread. Advanced computer vision algorithms are the key to the Phenotyping method. Computer vision algorithms are integrated with image processing functions to remove unwanted information and churn out only the relevant information needed for the analysis. Techniques such as deep estimate, color improvement, identification, and segmentation of the region have proven their mettle for in-detail analysis and flagging any unnecessary information overload.
Acquiring data on the trees spread over several hectares of land has become easy with the help of drones and other aerial capabilities offered by intelligent systems. Using these technologies, activities such as tree detection, drainage information, unused land, tree health, yield estimation, and stem analysis become pretty easy.
Artificial intelligence is widely used in the livestock market. Investments in AI are expected to increase significantly through 2026, with computer vision accounting for the largest share of this market.
Vision technology combined with IoT can provide the following benefits for precision farming:
With the help of cameras, computer vision systems help monitor animals such as pigs, sheep, cattle, etc. Also, neural networks help analyze the video feeds in real time. A computer vision system is a non-invasive, automatic, and low-cost animal monitoring alternative which is why it’s becoming increasingly popular. Vision systems help extract information without any major external efforts such as maintenance and sensor adjustment at affordable prices.
Therefore, computer vision systems are necessary for collecting data, analyzing it, and making informed decisions pertaining to livestock farming. These insights help enhance the welfare, environment, genetics, engineering, and management of farm animals using proof-based farm management and facility design.
Monitoring systems for animals offer continuous real-time monitoring and assist producers in making management decisions. Computer vision technology is extremely efficient in timely detection and triggering alerts for disease prevention and figuring out production inefficiencies. Artificial intelligence or AI vision insights can provide more consistent reports of animal behavior and phenotypes than subjective manual observations.
A computer vision system is an amazing way to automate the procedure of monitoring activities taking place on the farm. And this, in turn, helps comply with all animal welfare laws. Corrective action needs to be triggered on time, and a deep learning algorithm can be of great use here.
Vision systems make use of cameras based on AI to offer consistent reports on animal welfare in field conditions. Newer methods are able to assess the resources made available to animals (resting substrate, space, access to watering places) and to measure the animals themselves for lameness, signs of disease or injury & abnormal behavior. Therefore, computer vision offers mensurable animal welfare data to ensure animal welfare compliance on the farm.
DataToBiz has pioneered vision analytics with special domain expertise in Agriculture and smart farming. With years of extensive experience and expertise, they can help you identify the right problem and provide you with the technological capabilities to solve all your perennial issues. They have an in-house dedicated team of experts in the arenas like AI, Data Science, and Business intelligence solutions. With expertise spread across different business challenges, they can be the right fit for you if you are planning to adopt Computer Vision Technology.
They have the experience and agility to understand what’s possible and deliver to our expectations.
