Editing AI in Agriculture (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''Crop disease detection from leaf images:'''
<syntaxhighlight lang="python">
import torch
from torchvision import models, transforms
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader
import torch.nn as nn

# Plant Village dataset: 38 classes (crop × disease combinations)
transform = transforms.Compose([
    transforms.Resize(256), transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
dataset = ImageFolder("PlantVillage/", transform=transform)
train_size = int(0.8 * len(dataset))
train_ds, val_ds = torch.utils.data.random_split(dataset, [train_size, len(dataset)-train_size])
train_dl = DataLoader(train_ds, batch_size=32, shuffle=True, num_workers=4)
val_dl = DataLoader(val_ds, batch_size=32)

# Fine-tune EfficientNet-B4 for disease classification
model = models.efficientnet_b4(weights="IMAGENET1K_V1")
model.classifier[1] = nn.Linear(model.classifier[1].in_features, len(dataset.classes))
model = model.cuda()

optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
criterion = nn.CrossEntropyLoss()

for epoch in range(10):
    model.train()
    for images, labels in train_dl:
        images, labels = images.cuda(), labels.cuda()
        loss = criterion(model(images), labels)
        optimizer.zero_grad(); loss.backward(); optimizer.step()
</syntaxhighlight>

; AI agricultural applications
: '''Disease detection''' → PlantVillage CNN, Plantix app (mobile)
: '''Yield prediction''' → XGBoost on NDVI + weather; NASA Harvest platform
: '''Irrigation''' → CropX, Hortau, Lindsay Zimmatic — soil sensor + AI
: '''Drone scouting''' → DJI Agras, Sentera NDVI analysis, PrecisionHawk
: '''Weed control''' → Carbon Robotics laser weeding robot (vision-guided)
: '''Livestock''' → Cainthus (cattle face recognition), SCR (dairy cow sensors)
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