Efficient Farm Operations for Increased Productivity
Enabling Efficient Farm Operations for One of the World’s Largest Agrochemical Corporations Through Satellite Crop Monitoring
The client is an American agrochemical and agricultural biotechnology major and one of the first companies to apply the biotechnology industry business model to agriculture.
The Client’s Challenges
- No insights on the maize acreage from village level to district level
- Field staff conducted acreage estimation by the using methods like crop cuts, farmer survey, harvest sample method, whole plot harvest, expert assessments, crop cards, and allometric methods
- Data inconsistencies
- Labor-intensive and cost-ineffective practices
- Inadequate monitoring due to lack of standard benchmarks
- Manual intervention in crop yield estimation meant inaccurate reporting of yield at the plot level
How Cropin helped
Cropin partnered with the client to provide acreage insights at the village level using AI and ML-based solutions that use high-frequency satellite imagery across Bellary, Shimoga, Bijapur, and Dharwad regions of Karnataka, India. Remote monitoring of village-wise maize acreage for three years using AI and ML-based satellite imagery helped better management of delinquencies.
Impact:
Maize acreage provided for four districts
District |
2017 |
2018 |
2019 |
Bellary |
115,381 Hectares |
131,114 Hectares |
105,441 Hectares |
Bijapur |
62,330 Hectares |
46,616 Hectares |
73,659 Hectares |
Dharwad |
26,853 Hectares |
31,415 Hectares |
14,374 Hectares |
Shimoga |
56,023 Hectares |
70,745 Hectares |
61,859 Hectares |
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The client is an American agrochemical and agricultural biotechnology major and one of the first companies to apply the biotechnology industry business model to agriculture.
The Client’s Challenges
- No insights on the maize acreage from village level to district level
- Field staff conducted acreage estimation by the using methods like crop cuts, farmer survey, harvest sample method, whole plot harvest, expert assessments, crop cards, and allometric methods
- Data inconsistencies
- Labor-intensive and cost-ineffective practices
- Inadequate monitoring due to lack of standard benchmarks
- Manual intervention in crop yield estimation meant inaccurate reporting of yield at the plot level
How Cropin helped
Cropin partnered with the client to provide acreage insights at the village level using AI and ML-based solutions that use high-frequency satellite imagery across Bellary, Shimoga, Bijapur, and Dharwad regions of Karnataka, India. Remote monitoring of village-wise maize acreage for three years using AI and ML-based satellite imagery helped better management of delinquencies.
Impact:
Maize acreage provided for four districts
District |
2017 |
2018 |
2019 |
Bellary |
115,381 Hectares |
131,114 Hectares |
105,441 Hectares |
Bijapur |
62,330 Hectares |
46,616 Hectares |
73,659 Hectares |
Dharwad |
26,853 Hectares |
31,415 Hectares |
14,374 Hectares |
Shimoga |
56,023 Hectares |
70,745 Hectares |
61,859 Hectares |