CerealPestAID

Deep learning models for classifying 26 cereal crop pest species from images. Three architectures are provided: EfficientNet-B6, MobileNetV3-Large, and InceptionV3, each available in PyTorch, ONNX, and TFLite formats.

Model Description

These models were trained to identify 26 species of cereal crop pests from field images. The goal is to support automated pest monitoring and integrated pest management (IPM) in cereal crop agriculture.

Project Website: cerealpestaid.net
Mobile App:
Developed by: Luke Sheneman, University of Idaho
Model type: Image classification (CNN)
Language: English (labels)
License: MIT
Source code: github.com/sheneman/CerealPestAID
Dataset: sheneman/CerealPestAID-dataset

Performance Summary

Evaluated on a held-out test set of 2,381 images:

Model	Test Accuracy	Parameters	Input Size
EfficientNet-B6	92.94%	~43M	528x528
MobileNetV3-Large	90.09%	~5.4M	528x528
InceptionV3	79.00%	~27M	528x528

Classes (26)

Index	Species	Index	Species
0	Cabbage seedpod weevil	13	Lygus bug
1	Bird cherry oat aphid	14	Non-pest herbivores
2	Cabbage aphid	15	Occasional pest
3	Cereal grass aphid	16	Pea aphid
4	Cereal leaf beetle	17	Pea leaf weevil
5	Clickbeetles / wireworms	18	Pea weevil
6	Crucifer flea beetle	19	Predators
7	Cutworms	20	Rose grain aphid
8	Diamondback moth	21	Russian wheat aphid
9	English grain aphid	22	Stink bug
10	Greenbug	23	Striped flea beetle
11	Green peach aphid	24	Turnip aphid
12	Hessian fly	25	Wheathead armyworm

Model Files

├── efficientnet-b6/
│   ├── best_factai_efficientnet-b6.pth      (469 MB)
│   ├── efficientnet-b6_simplified.onnx      (155 MB)
│   └── efficientnet_b6_fp32.tflite          (155 MB)
├── inceptionv3/
│   ├── best_factai_inceptionv3.pth          (289 MB)
│   ├── inceptionv3_simplified.onnx          (83 MB)
│   └── inceptionv3_fp32.tflite              (83 MB)
├── mobilenetv3/
│   ├── best_factai_mobilenetv3.pth          (49 MB)
│   ├── mobilenetv3_simplified.onnx          (16 MB)
│   └── mobilenetv3_fp32.tflite              (16 MB)
└── label_mappings.txt

Usage

PyTorch (EfficientNet-B6)

import torch
import torch.nn as nn
from torchvision import transforms
from efficientnet_pytorch import EfficientNet
from PIL import Image

# Load model
model = EfficientNet.from_name("efficientnet-b6", num_classes=26)
state = torch.load("efficientnet-b6/best_factai_efficientnet-b6.pth", map_location="cpu")
model.load_state_dict(state["model_state_dict"])
model.eval()

# Preprocess
transform = transforms.Compose([
    transforms.Resize(572),
    transforms.CenterCrop(528),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

image = Image.open("pest_image.jpg").convert("RGB")
input_tensor = transform(image).unsqueeze(0)

# Predict
with torch.no_grad():
    output = model(input_tensor)
    pred = torch.argmax(output, dim=1).item()
print(f"Predicted class: {pred}")

PyTorch (MobileNetV3-Large)

import torch
import torch.nn as nn
from torchvision import models, transforms
from PIL import Image

model = models.mobilenet_v3_large(weights=None)
model.classifier[3] = nn.Linear(model.classifier[3].in_features, 26)
state = torch.load("mobilenetv3/best_factai_mobilenetv3.pth", map_location="cpu")
model.load_state_dict(state)
model.eval()

transform = transforms.Compose([
    transforms.Resize(572),
    transforms.CenterCrop(528),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])

image = Image.open("pest_image.jpg").convert("RGB")
input_tensor = transform(image).unsqueeze(0)

with torch.no_grad():
    output = model(input_tensor)
    pred = torch.argmax(output, dim=1).item()
print(f"Predicted class: {pred}")

ONNX Runtime

import onnxruntime as ort
import numpy as np
from PIL import Image

session = ort.InferenceSession("efficientnet-b6/efficientnet-b6_simplified.onnx")

image = Image.open("pest_image.jpg").convert("RGB").resize((528, 528))
input_data = np.array(image, dtype=np.float32) / 255.0
input_data = (input_data - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225]
input_data = np.transpose(input_data, (2, 0, 1))[np.newaxis, ...]

output = session.run(None, {"input": input_data.astype(np.float32)})
pred = np.argmax(output[0])
print(f"Predicted class: {pred}")

TFLite

import numpy as np
from PIL import Image
import tensorflow as tf

interpreter = tf.lite.Interpreter(model_path="efficientnet-b6/efficientnet_b6_fp32.tflite")
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

h, w = input_details[0]['shape'][1], input_details[0]['shape'][2]
image = Image.open("pest_image.jpg").convert("RGB").resize((w, h))
input_data = np.expand_dims(np.array(image, dtype=np.float32) / 255.0, axis=0)

interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
output = interpreter.get_tensor(output_details[0]['index'])
pred = np.argmax(np.squeeze(output))
print(f"Predicted class: {pred}")

Training Procedure

Hardware

All models were trained on the University of Idaho RCDS HPC cluster using SLURM, with CUDA 11.8:

Model	GPU	Epochs Completed	Wall Time	Best Checkpoint Epoch
EfficientNet-B6	NVIDIA RTX 3090 (24 GB)	10	~5h 47m	1
InceptionV3	NVIDIA RTX 4090 (24 GB)	51	~6h 02m	4
MobileNetV3-Large	NVIDIA RTX 4090 (24 GB)	79	~6h 04m	5

All three training runs were terminated by the SLURM scheduler wall time limit. Best model checkpoints were saved based on minimum validation loss, which was achieved early in training for all models. Subsequent epochs continued to reduce training loss but showed increasing validation loss (overfitting), confirming that early checkpoint selection was appropriate.

Training Curves

Key observations:

EfficientNet-B6 converged fastest, achieving its best validation loss (0.433) at epoch 1 with 88.8% validation accuracy. By epoch 10, training accuracy reached 98.2% while validation accuracy peaked at 92.2% (epoch 7).
InceptionV3 achieved its best validation loss (0.578) at epoch 4. Validation accuracy plateaued around 89-90% while training accuracy continued to ~99.3%, indicating overfitting after ~15 epochs.
MobileNetV3-Large achieved its best validation loss (0.474) at epoch 5. It trained the longest (79 epochs), reaching 99.7% training accuracy while validation accuracy stabilized around 90-92%.

Hyperparameters

Optimizer: Adam (lr=1e-4)
LR Schedule: ReduceLROnPlateau (factor=0.9, patience=5)
Batch size: 8
Max epochs: 1000 (early stopping via best validation loss)
Loss: CrossEntropyLoss
Class balancing: WeightedRandomSampler (inverse class frequency)
Input resolution: 528x528 (resize to 572, then center crop for val/test)

Learning Rate Decay

The ReduceLROnPlateau scheduler reduced the learning rate by 0.9x each time validation loss failed to improve for 5 consecutive epochs:

Model	Starting LR	Final LR	LR Reductions
EfficientNet-B6	1e-4	9e-5	1
InceptionV3	1e-4	4.8e-5	8
MobileNetV3-Large	1e-4	2.8e-5	13

Data Augmentation (training only)

RandomResizedCrop (528, scale 0.6-1.0)
RandomHorizontalFlip
RandomVerticalFlip
RandomRotation (30 degrees)
ColorJitter (brightness=0.2, contrast=0.2, saturation=0.2, hue=0.2)
ImageNet normalization

Limitations

Models were trained on a specific dataset of cereal pest images and may not generalize well to pest species not in the training set.
The non_pest_herbivores class is the most challenging across all models (66.7% accuracy at best), likely due to high intra-class visual variability.
Input images are expected to be well-lit, focused close-ups of individual insects.

Acknowledgments

This project, titled "Harnessing Artificial Intelligence for Implementing Integrated Pest Management in Small-Grain Production Systems," is funded under the U.S. Department of Agriculture No. 2021-67021-34253.

Team

Sanford Eigenbrode - Distinguished Professor, Entomology, Plant Pathology, and Nematology, University of Idaho (PI)
Arash Rashed - Virginia Tech Southern Piedmont Agricultural Research and Extension Center
Marek Borowiec - Assistant Professor, Insect Systematist, Director of C. P. Gillette Museum, Colorado State University
Subodh Adhikari - Assistant Professor, Entomology Extension Specialist, Utah State University
Luke Sheneman - Director of Research Computing, University of Idaho
Jennifer Hinds - Research Applications Architect, University of Idaho
John Brunsfeld - Senior Full Stack Developer, University of Idaho

Citation

@misc{sheneman2025cerealpestaid,
  author = {Sheneman, Luke},
  title = {CerealPestAID: Deep Learning Models for Cereal Pest Identification},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/sheneman/CerealPestAID}
}

Downloads last month: 24

sheneman
/

CerealPestAID