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