Computer Vision, part of AI, can bring about radical changes in agriculture, increasing yields and incomes. Its time farming became a high-tech industry
Sight, sound, smell, taste, and touch: it is our senses that establish us on the top of the dominance pyramid. In an era where machines have surpassed humans in various facets, the lack of some if not all, senses is what makes artificial intelligence (AI) artificial.
Swedish philosopher Nick Bostrom said, “Artificial Intelligence in the last invention that humanity will ever need to make.”Computer Vision (CV), a branch of AI, proves this quote to be truer today than ever.
In an interdisciplinary field, CV enables machines to gain a high-level understanding and derive useful information from images, videos, and visual inputs. Based on the information, computers can make recommendations, forecast details, and automate tasks.
The advent of AI technology can contribute across multiple industries. The agriculture industry, the backbone of a nation’s economy, needs an amalgamation of smart technologies to overcome its current shortcomings. According to Xavier Besseyre des Horts (sales manager, agritech, at Microsoft), AI can increase agricultural productivity by 45% while reducing costs by 35%.
The major advantages of CV in agriculture include reduction in cost, time and manual errors, accurate results, lower dependency on human intervention, non-destructive techniques, and real-time results for better decision-making. Here are some specific areas where CV, along with deep learning and other AI techniques, can transform the crop sector.
Drones: With the help of drones that leverage sophisticated imaging technology and remote sensing cameras, CV gives a bird’s-eye view of the field. This can help advance the end-to-end farming process. This includes soil sampling, seed planting pattern, fertilizing and irrigation strategy as well as finding alterations in leaf color and stem growth.
Drones can also be used to spray pesticides and fertilizers properly. Techniques like bounding box, semantic segmentation, image, and polygon annotation are used to train drones for mapping the fields and analysing the data for forecasting.
An interdisciplinary field, CV enables machines to gain high-level understanding and derive useful information from images, videos and visual inputs.
Yield prediction and analysis: CV can gather details about soil conditions, nitrogen levels, moisture, and weather with minimum effort. This, combined with historical yield information, can help analyze the crop and predict the yield.
Phenotyping: It refers to all measurable features of a plant such as a leaf color, shape, and height. CV helps gather the plant’s images and provides information that helps study physiological responses and the development of crops in various conditions. This allows farmers to make informed decisions for future yields.
Root Phenotyping can critically help in understanding the genetic improvement techniques of crops. With CV, we can study roots using the non-destructive image capturing method that does not damage them.
Disease and pest detection: Computers can assess crop health and detect crop diseases. According to various studies, trained models have so far shown an accuracy of 99.35%.
Weed detection: The combination of image processing and deep learning techniques can distinguish a weed from the crop, achieving precise herbicide spraying. This reduces pollution caused to farmland due to the full coverage splattering of herbicides.
Quality inspection and grading: Post-harvest applications of CV are the most interesting ones. CV is used to sort good crops from bad ones, separating lots stable for longer shipments and determining which lots can be shipped to local markets. The size of the product matters in a customer-centric market. Automated grading and size measurement of fruits and vegetables can save time and manual effort.
Indoor farming: Unlike the traditional model, indoor farming is quite expensive in the initial setup and maintenance. CV can help with suitable light intensity, temperature, crop inspection, and quality control, with minimal expert input. This leads to a reduction in operational costs by reducing human efforts.
Computers can assess crop health and detect crop diseases. According to various studies, trained models have so far shown an accuracy of 99.35%.
Opportunities for improvements
For a long time, agriculture has been a low-tech industry. This leaves room for measures to set the base for a successful amalgamation of agriculture and technology. It is necessary to create awareness among farmers regarding the importance of technology, innovation, and collaboration. The gradual erosion of prevailing technophobia, alongside the positive impact of technology, will enable farmers to trust collaboration more.
Awareness and acceptance come with the need to make technology viable and easy to use. Cheaper internet access, proper infrastructure, provisioning of smart devices, training the farmers to operate and make the most out of available technology are critical steps towards moving to agrotech.
According to MGI research, if IT infrastructure connectivity is implemented successfully in agriculture, the industry can bring in an additional value of USD500 billion to the global GDP by 2030. This would release much of the current pressure on farmers with a 7%-9% improvement from the expected total.
The agriculture sector can greatly benefit from CV and its advancements. With help of technologies like CV, one can prepare to deal with future roadblocks that nature might send our way. Despite challenges, a world of digitisation is an inevitable reality. It is only right to seize the positives to achieve the best agricultural output with future tech.
After all, we are at the start of a new revolution.