AI powers the agrochemical economy; enables companies to stay ahead of the competition

Agri-input-companies-staying-ahead-with-ai

Table of contents

Understanding customers’ needs is the key to a successful business, and the agrochemical industry is no exception. Farmers must collaborate with various stakeholders, including agri-input companies, to ensure successful cultivation. These agrochemical companies must provide distributors with sufficient stock of the specific product that will have high demand. On a related note, the per-acre yield is determined by multiple factors, of which the use of agrochemicals is essential.

To improve agricultural productivity, protecting the crops from pests and diseases, maintaining soil fertility, and ensuring the health of the crops is important. Agrochemicals play a critical role in ensuring this, irrespective of the crop or region of cultivation. With the right approach and understanding of the pain points of farmers and the cultivation process, agrochemical companies have enormous potential to bring about transformation in the value chain and increase return on investment (RoI) with efficient usage.

With the increasing population and demand for sustainably grown food, the future of the agrochemical industry looks promising. The global agrochemicals market, which was pegged at USD 221.38 billion in 2021 is estimated to touch USD 286.08 billion by 2030, increasing at a CAGR of 2.89%, according to Straits Research.

Challenges faced by agrochemical companies

Thanks to digitalization in agriculture, today these barriers are being overcome through the adoption of technology. Cropin, a leading Agtech company, has developed a unique intelligent cloud platform to address the needs of the Agri-industry and transform its businesses. Smart Farming has helped agrochemical companies to penetrate deeper into the market with well-designed go-to-market strategies. These are based on the accurate ground-level intelligence procured with the help of Artificial Intelligence, Machine Learning, weather updates, satellite monitoring, and cloud computing.

Cutting-edge intelligence provides data-driven insights on

crop-detection

Crop detection and acreage estimation

pixel-level-farm

Hyper-local pixel-level farm intelligence

health-and-growth

Identify crop signature

crop-detection

Identify crop health and growth

Using these, agrochemical companies can determine supply-demand market dynamics to drive sales strategies.

Value delivered

Insights

Insights at a fraction of the traditional cost

minimal-manual-effort

Minimal manual effort and intervention

Error-free-data

Error-free data collection

dynamic-marketing

Enabling dynamic marketing strategy designing & supply chain activities

increased-roi

Increased ROI

Upping the game

Rainforest Alliance, a leading international non-profit organization, partnered with Cropin to develop a future-ready artificial intelligence/machine learning-based predictive solution called CocoaSense, with a target to triple the average yield of Ghanaian cocoa farmers to 1500 kilograms per annum.

Challenges faced in cocoa farming in Ghana included the following:
The partnership implemented a holistic system to estimate crop acreage and get village-level crop acreage information. Supported by artificial intelligence/machine learning in farming, along with big data, high-frequency satellite imagery, and geotagging the cocoa farming process witnessed a digital transformation resulting in improved yields.

Benefits post digitalization

Farmers received mobile-enabled individualized digital farmer development programs, that guided them over a seven-year period. The benefits enjoyed by smallholder farmers were
Cutting-edge deep learning technology provides PIN code-level intelligence efficiently and cost-effectively. The lack of this critical information makes the conversion of production to sales challenging. In contrast, the use of digital technology enables targeting specific farmer communities and implementing successful marketing and sales strategy to achieve targets.

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