Nearly 100 million families across the world rely on cotton farming for their livelihood. Cotton is particularly vulnerable to pest attacks, leading to overuse of pesticides, lost income for farmers, and in some cases farmer suicides. We address this problem by presenting a new solution for pesticide management that uses deep learning, smartphone cameras, inexpensive pest traps, existing digital pipelines, and agricultural extension-worker programs. Although generic, the platform is specifically designed to assist smallholder farmers in the developing world. In addition to outlining the solution, we consider the set of unique constraints this context places on it: data diversity, annotation challenges, shortcomings with traditional evaluation metrics, computing on low-resource devices, and deployment through intermediaries. This paper summarizes key lessons learned while developing and deploying the proposed solution. Such lessons may be applicable to other teams interested in building AI solutions for global development.