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Crop production around the globe is increasingly at risk from invasive organisms, particularly the Fall Armyworm (FAW, Spodoptera frugiperda), which has caused severe deterioration of maize harvests across both Africa and Asia starting in 2016. To enable targeted pest control, an adaptive Echo State Network (ESN) was constructed that projects yearly maize output while factoring in FAW burden. A 15-year (2010–2024) monthly repository was built, integrating vegetation indicators from satellites, field-level meteorological measurements, soil nutrient readings, and FAW observation tallies. The intensity of FAW infestation is represented on a continuum from 0 to 100, derived by fusing monthly trap catch figures (40%) with larval concentration (60%). The ESN is initially calibrated on the entire body of environmental features to estimate crop performance. Next, isotonic regression is deployed to link infestation intensity with the ESN's positive residual errors, yielding a monotonic penalty mapping. This mapping captures the magnitude of output depression at varying infestation levels. When generating estimates, the inferred penalty is applied to the unadjusted ESN projection, modifying harvest forecasts to incorporate pest-driven losses without changing the foundational ESN configuration. In testing across held-out folds, the FAW-sensitive ESN yields an R² near 0.55 and cuts forecast discrepancies by as much as 67% compared to unadjusted reference models, faithfully reproducing documented harvest declines beyond 20% during major infestation episodes. The approach beats standard regression and deep neural architectures by comparable margins. It supports agriculturalists in directing countermeasures toward high-threat areas, lowering both chemical input volumes and operational expenditures. These outcomes emphasize its promise as a forward-looking alert mechanism for pinpointed actions that curtail agrochemical usage and improve input distribution. Ongoing field assessments will gauge its capacity to scale and its tangible efficacy within maize-growing zones affected by FAW.

A precision spray perception system using LiDAR was designed, algorithmically developed, and experimentally verified to address the problem of low navigation line extraction accuracy in self-propelled sprayers, which causes wheel encroachment on ridges and pesticide overuse. For this perception system, a data processing framework was built encompassing preprocessing, adaptive segmentation separating crops from ditches, and the derivation of navigation lines and crop positioning, all originating from raw LiDAR point cloud species. To validate the system’s stability, field data on cabbages at different growth stages were collected and analyzed. A controllable, constant-speed experimental configuration was set up to benchmark LiDAR against a depth camera within the same field conditions. The trial outcomes reveal that at sprayer speeds of 0.5 and 1 ms⁻¹, the peak lateral deviation reaches 0.112 m within cabbage ridge settings containing inter-row weeds. In comparison, the mean absolute lateral error stands at 0.059 m. Each frame is processed in no more than 43 ms. Compared with the machine vision approach, the proposed technique reduces the mean processing time by 122 ms. In terms of identification accuracy, computational speed, and overall robustness for crop detection and navigation line derivation, the system introduced here outperforms its machine vision counterpart.